Courses & Programmes

Accounting is the language required to do business. It allows executives, managers and employees to communicate; it allows investors, analysts, and governmental authorities to understand how an organization is doing. Thanks to the International Financial Reporting Standards, accounting can be seen as a global language, spoken across most countries.

Accounting is much more than book-keeping. Without accounting, it is not possible to evaluate a new business opportunity, assess the performance of a company, or design a solid business strategy for the near future. This course provides a thorough introduction to financial and managerial accounting. It provides fundamental knowledge that all business practices require.

This course teaches how to formally record and report economic events and transactions, how to read accounting information to make inferences and support decisions, and how accounting plays an active role in the success or failure of a company. More broadly, this courses explains why the accounting activity cannot be performed by robots, as it involves discretion in how information is recorded and reported. Moreover, the course offers a first look at what the accounting profession entails.

This course covers both theoretical and practical aspects of complex dynamic econometric models that are used in the industry, by central banks, governments, think tanks, and other research institutes. The students will be introduced to stochastic theory that allows them to fully understand the dynamic properties of complex models featuring nonlinearities, time-varying parameters and latent variables. Important concepts include invertibility, stationarity, dependence, ergodicity and bounded moments.

The students will also be introduced to advanced estimation theory that allows them to “bring” state-of-the-art models to the data and conduct inference on parameters under very general conditions. Important topics include the existence, measurability, consistency and asymptotic normality of extremum, M and Z estimators. We also cover advanced topics in nonlinear model selection and specification, estimation and inference under incorrect specification and metric selection. From a practical perspective, the advanced methods and state-of-the-art models are used for forecasting and policy analysis in a wide number of applications ranging from finance to macroeconomics.

The aim of the Advanced Econometrics 1 course is to obtain a deep understanding of econometric theory, practice and inference using a variety of advanced econometric techniques.

After passing the course, students should be able to apply advanced econometric techniques in practice, to extend currently available methods when needed for particular applications, to implement these methods in a matrix programming environment, and to understand and derive their statistical properties.

The aim of the Advanced Econometrics 2 course is to obtain a deep understanding of econometric theory, practice, and inference using four special advanced econometric topics. The contents of this course build upon the general knowledge acquired in the course Advanced Econometrics 1.

Topics include: bootstrap methods; semi- and non-parametric methods; weak identification; panel data models.

This course is part of the Mastermath programme. All information can be found at the Mastermath website.

Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is so pervasive today that it is used in everyday life without knowing it. In this course, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work yourself. We will discuss the theoretical underpinnings as well as the practical know-how needed to apply these techniques to new problems.​

The objective of this course is to teach students about advanced
concepts in networking technologies beyond the basic TCP/IP protocol and
socket abstraction. Upon completion of this course students will be able
to:
* Understand the internals of Linux’s TCP/IP stack implementation, and
data center networks
* Explain network stack design and implementation challenges associated
with multi-core systems and high-speed networks (100+ Gbps)
* Analyze various network stack designs (kernel, userspace,
application-specific) and APIs (e.g., sockets, RDMA and friends) on
their trade-offs, performance gains, and implementation efforts
* Understand how to build a high-performance network infrastructure for
large-scale cloud data centers and how to program and manage such a
network with software
* Learn about ongoing and emerging research challenging in networking
technologies

Please refer to the System and Network Engineering web pages for detailed and current course information.

Please refer to the System and Network Engineering web pages for detailed and current course information.

The course will feature a number of hands-on assignments accompanied by lectures on advanced operating system kernel design and programming concepts. In each assignment, students will be expected to start with a minimal kernel implementation and exercise their kernel hacking skills on one of the major operating subsystems (i.e., memory management, process management, drivers, etc.). This will involve programming in both C and assembly as well as directly interfacing with the hardware. The course will also link lectures and assignments to modern operating system features and offer insights into state-of-the-art OS research efforts.

To learn advanced programming skills, to get to know and understand advanced programming concepts like inheritance and to get experience with programming some of the data structures that were taught in the course Data Structures & Algorithms.

The student will have an appreciation of modern programming paradigms as realised in a range of current programming languages.
The student will have achieved an understanding of how programming languages are interpreted and compiled through the practical construction of interpreters.
The student will be able to quickly assess and acquire a novel programming language.

Learning Outcomes
Students who have completed the course will have an understanding of the principles of programming languages and the methodological foundations of computer programming as well as the demonstrated ability to apply these principles. They will have highly-developed analysis and problem solving skills as well as the ability to quickly acquire and program in a range of programming languages.

In this course we will cover a series of techniques that are more advanced than those covered in BRMS1, BRMS2, or Statistics for Sciences. We will work extensively with science and social science data and learn how to analyze and interpret data at an advanced level. The course covers the following topics:

– Review of multivariate linear regression and ANOVA
– Basic matrix algebra
– Model selection and diagnostics in multivariate linear regression
– Logistic regression
– (M)ANOVA and (M)ANCOVA
– Discriminant analysis and classification
– Principle component analysis
– Analysis of repeated measures
– Categorical data analysis and multi-way frequency tables

The central aim of the course will be that students acquire the skills to conduct and interpret quantitative analyses of various empirical studies. Students will also analyze science and social science data using the techniques learned in the course and complete data analysis assignments.

Please refer to the System and Network Engineering web pages for detailed and current course information.

At the end of this module a student is able to:
• correctly reproduce the central concepts of multiple regression analysis;
• independently and correctly calculate statistics such as the (un)standardized b-coefficient, intercept, Sum or Squared Errors, Total Sum or Squares r, R², f and t values, and standard deviations;
• independently and correctly perform multiple regression analysis with interaction-effects and categorical independent variables with statistical software;
• independently and correctly interpret the results of statistical analysis performed by themselves and/or by statistical software;
• independently and in a correct, precise and clear way write up results of quantitative hypothesis-testing research regarding variability among different social groups.

After this course, the students will understand the basic principles of
multilevel analysis and longitudinal data analysis. Furthermore, they
will be able to perform these techniques with standard software
packages. In addition, they will understand the principles of open
science and the current debate about null hypothesis significance
testing.
Specific goals are that:
• The student is able to explain and apply multilevel analysis for
cross-sectional data
• The student is able to explain and apply the basic principles of the
advanced techniques for longitudinal data.
• The student is able to explain the differences between different
methods and models of analysing clustered data and to motivate a choice
for one of these models in the context of epidemiological datasets/
research examples.
• The student can interpret results from the various methods and models
in the context of epidemiological datasets/ research examples
• The student is capable of performing the advanced techniques using
Stata
• The student can explain the principles of open science and is able to
relate these to quantitative data analyses
• The student can explain pitfalls of null hypothesis significance
testing (NHST) and can draw conclusions without reliance on NHST
The student is capable of performing the advanced techniques using
Stata
• The student can deliver an oral presentation following a scientific
format on a data-analysis assignment involving correlated data focusing
on the data-analyses, results and conclusion.
• The student can write the data-analysis, results and conclusion
section of a short scientific paper demonstrating he/she is able to
reflect on the results of the advanced analyzing techniques

In this course you will be made familiar with the main tools in the chemometrics toolbox, PCA, PLS, PLSDA and how to apply them in various fields and applications. Besides these tools we will look at different properties of spectroscopic and MS data and their preprocessing methods. Finally, validation of the multivariate models gets sufficient attention.

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms and data structures used to solve these problems. It also uses the Python programming language to implement and test algorithms and data structures on realistic datasets. The technological topics which will be covered in this course are:

• Python Programming Basics;
• Introduction to Object-Oriented Programming in Python;
• Algorithm Analysis;
• Basic Data Structures:
• Recursion;
• Sorting and Searching;
• Trees and Tree Algorithms;
• Graphs and Graph Algorithms.

Have you ever wondered how we can track a gene across 3 billion years of evolution? Sequence alignment can be used to compare genes from humans and bacteria, using a dynamic programming algorithm. In this course we focus on algorithms for biological sequences that can be applied to real scientific problems in biology.

Students will gain in depth knowledge on the theory of sequence analysis methods. They will also develop understanding and skills to apply the algorithms to protein and DNA sequences. We would like to stress that no biological knowledge is required to enter this course.

What is Data Science? Data Science & Artificial Intelligence is about discovering hidden patterns in large amount of data, using computers, brainpower and the wealth of data. These days, many innovations heavily rely on Data Science & Artificial Intelligence. Think about:

1. self-driving cars;
2. social network analysis;
3. teaching computers to understand text;
4. image and face recognition;
5. targeted advertising;
6. sport analytics.

Main objective of this minor is to learn you to carry out a Data Science & Artificial Intelligence project from the beginning to the end.

To increase your success during the minor, basic Python and Statistics knowledge will be required. To obtain this knowledge or to fresh it up we offer online courses. Consult ‘Registration and entry requirements’ for more information on these courses.

What will you learn in this Minor? You will learn to carry out a Data Science project from beginning to end. That involves: Asking the right (research) question(s), data collection and cleaning, data management and organisation, modelling, communication and visualisation, and implementation.

Your Future: This Minor will give you access to both UvA and VU Master programmes: Information Studies: The ‘Data Science’ track 
teaches you to apply Data Science in a wide 
range of areas; Business Administration: The ‘Digital Business’ 
track teaches you to apply Data Science in a commercial/business context (additional requirements also apply). This Minor will give you excellent job prospects. McKinsey have projected a shortfall of 190,000 Data Scientists by 2018!

Entry requirements: Successful completion of the first year of a Bachelor is required for participation in this Minor. In addition, the following knowledge is assumed: Basic Python & Statistics knowledge (if this is lacking self-study is required). Students taking this Minor typically follow
 a Bachelor in: Actuarial Sciences, Business, Communication Sciences, Econometrics, Economics, Psychology, STEM studies (Science, Technology, Engineering, Mathematics). The Minor is also open to other students, provided they have an affinity with quantitative methods.

Curriculum: The Minor is comprised of the following courses (more information in the course catalogue): Databases & Data Visualisation; Data Structures with Python; Data Wrangling; Ethics & Law; Machine Learning; Text Retrieval & Mining.

Further information: 

• www.uva.nl/minors
• www.schoolofdatascience.amsterdam/education/amsterdam-data-science-minor
• info@schoolofdatascience.amsterdam
• See the flyer

Network:

The Data Science Minor is part of the Amsterdam School of Data Science, an initiative of Amsterdam Data Science, which organises Meet-ups also open to students:

• www.meetup.com/Amsterdam-Data-Science/

In brief:

• Name: Amsterdam Data Science Minor
• Minor Credits: 30 EC
• Duration: 6 months
• Start: September
• Language: English
• Entry requirements: See section
• Minimum: 30 students; Maximum: 75

After this course students will be able to:

– Develop insights into personal strengths, weaknesses, core values and development priorities;

– Develop the ability to inquire and advocate in an effective way;

– Understand and apply different styles of influencing with integrity;

– Reflect on the effectiveness of one’s leadership behaviours by applying practical concepts;

– Develop insights into importance of diversity and inclusiveness in leadership;

– Create a culture of learning and giving/receiving high quality feedback;

– Generate an effective team charter in order to maximise the impact of team work.

In this 4-day interactive course  provided by the Amsterdam Business School you will learn key skills to perform as an Analytics Translator. You will play the bridging role between the technical expertise of data scientists and the operational expertise of domains such as marketing, HR, supply chain, finance. This role is crucial to ensure that the data science efforts connect flawlessly to the business needs. This unique course will prepare you for this new and valuable role in every organisation. The next edition will start on Thursday 15 April 2021.

What you will learn:

• A fundamental understanding of machine learning methods.
• Identifying business opportunities for data science solutions.
• Essential statistical concepts.
• Data visualisation, dashboard design and storytelling with data.
• Implementation of data science projects.
• How to translate between data science team and business team/management.
• Data science ethics and regulations, including GDPR.
• Understand data science roles and what kind of teams are needed.

In this four day interactive course you will learn key skills to perform as an Analytics Translator. You will play the bridging role between the technical expertise of data scientists and the operational expertise of domains such as marketing, HR, supply chain, finance. This role is crucial to ensure that the data science efforts connect flawlessly to the business needs.

This course is designed for business professionals who want to become the crucial link between business and data science and analytics teams. The profile of the participants will be business professionals in finance, auditing, control, risk, marketing, HR, sales, logistics, supply chain, etc.

In this interactive online programme provided by the Amsterdam Business School, you will learn how to connect the technical expertise of data scientists to the operational expertise of domains such as marketing, HR and finance. The programme consists of 2 modules: Machine Learning for Analytics Translators (start: Tuesday 27 October) and Data Science in Business for Analytics Translators (start: Thursday 12 November). Choose to follow the entire programme or pick one of the modules, whatever fits your needs best.

This online programme consists of 2 modules. You can choose to follow 1 or both modules.

Module 1: Machine Learning for Analytics Translators

As an analytics translator, you have some knowledge of machine learning and data science, but not to the technical depth at which data scientists do their work. You are uniquely able to approach the project from the viewpoint of the business. This module consists of 4 half-days in which you will gain an understanding of statistics and machine learning. This knowledge will allow you to spot opportunities for techniques that can be used to solve a variety of business problems. No programming skills are needed to follow this module.

Module 2: Data Science in Business for Analytics Translators

This module consists of 6 half-days, in which you will learn how you can guide your business through a digital transformation with data science solutions. You will gain an understanding of what it means to write a data science project plan that fits your audience. This will allow you to facilitate a successful implementation of data science techniques within your business. Statistics and machine learning basics are recommended for this module.

This course introduces you to the maths used within finance and financial institutions.

Topics covered include the theory of options, binomial method, Black-Scholes model and its application, heat equation, and numerical methods.

This course is about regression analysis, which in applied economics is a powerful tool to analyse empirical relationships. First, estimation and testing of the basic linear regression model by ordinary least squares (OLS) and instrumental variables (IV) will be reviewed. Particular attention will be paid to the statistical assumptions underlying the basic model. These assumptions should be valid in applications in order to give reliable outcomes. Second, we will focus on various applications and extensions of the basic model.

We will cover the analysis of: (1) experimental data, (2) panel data and (3) time series.

The objective of this course is to give Business students the necessary econometric tools to complete te Finance specialisation; Finance is a field with strong emphasis on empirical work and rich datasets. Upon successful completion of this course students should have the following knowledge, skills and attitudes:

• a solid knowledge of the various types of datasets used in Empirical Finance, and their corresponding techniques (time-series, cross-sectional, event-study and panels);
• a solid understanding of the assumptions underlying regression analysis;
• a solid knowledge of the statistical tests and techniques used to detect and remedy violations of these assumptions;
• a solid understanding of the factors influencing the significance found in empirical research;
• be able to conduct statistical tests on regression results in order to answer research questions.

All of this will have a strong focus on applied skills.

Public policies need to be evaluated in order to understand their
effectiveness and correct validation of economic theory can only be
achieved with empirical research. The main objective of this course is
to provide an overview of econometric research methods in urban, real
estate, transport and environmental economics and to teach you how to
apply these methods to real-world data. After following this course, you
will:
• have an advanced understanding of the mathematical and statistical
concepts underlying regression analyses in spatial economics;
• understand the importance of and difficulties in estimating causal
effects as opposed to correlations in spatial economic problems;
• know how to appropriately interpret regression results of various
estimators and know which one to apply in particular situations,
depending on (i) the nature of the data (cross-sectional / panel /
discrete data) and (ii) the task at hand (i.e., valuation of public
policies, testing of economic theories or estimating parameters as
derived from theory);
• understand and know how to apply techniques that are commonly in use
in urban, real estate, transport and environmental economics and policy:
spatial econometrics, discrete choice models and quasi-experimental
research designs;
• be able to apply these methods independently to typical datasets in
spatial economics using the software package STATA.

Public policies need to be evaluated in order to understand their effectiveness and correct validation of economic theory can only be achieved with empirical research. The main objective of this course is to provide an overview of econometric research methods in urban, transport and environmental economics and to teach you how to apply these methods to real-world data. After following this course, you will:
• have an advanced understanding of the mathematical and statistical concepts underlying regression analyses in spatial economics;
• understand the importance of and difficulties in estimating causal effects as opposed to correlations in spatial economics problems;
• know how to appropriately interpret regression results of various estimators and know which one to apply in particular situations, depending on (i) the nature of the data (cross-sectional / panel /discrete data) and (ii) the task at hand (i.e., valuation of public policies, testing of economic theories or estimating parameters as derived from theory);
• understand and know how to apply techniques that are commonly in use in urban, transport and environmental economics and policy: spatialeconometrics, discrete choice models and quasi-experimental research designs;
• be able to apply these methods independently to typical datasets in spatial economics using the software package STATA.

This course is about regression analysis, which in empirical finance is a powerful tool to analyse empirical relationships.
First, estimation and testing of the basic linear regression model by ordinary least squares (OLS) and instrumental variables (IV) will be reviewed. Particular attention will be paid to the statistical assumptions underlying the basic model. These assumptions should be valid in applications in order to give reliable outcomes. Second, we will focus on various applications and extensions of the basic model. We will cover the analysis of:

• experimental data;
• panel data;
• time series.

Machine Learning is marking a revolution in the world. Originally an academic research topic, the last decade has seen a major paradigm shift with Machine Learning used in many companies for a wide range of services. From deleting SPAM mail from your inbox to ranking the Google search results, and from defining your Facebook stream to serving you the advertisement on a website.

In the Applied Machine Learning course we study and learn from large collections of unstructured data, such as text documents, web pages, images and videos. We address the complete machine learning chain, from designing the system and its objectives, to representing data and selecting and evaluating the learning method. We review and focus on the foundations of retrieval, supervised classification and unsupervised clustering. You will learn the theoretical concepts during the lectures with a keen eye on the design of the full learning system.

In the tutorials we will focus on some of the important mathematical concepts, and in the lab you will gain hands-on experience through a number of coding assignments and by participating in a Kaggle competition. Finally, a few experts from the field (both academic as well as industry colleagues) are invited to give guest lectures.

This course introduces the basic concepts underlying applied statistics.
Its main focus is on estimation of effect sizes and confidence
intervals, and elementary statistical tests. The applied techniques that
will be introduced are aimed at description of observed data, and
estimation of and testing null-hypotheses about single population means
and proportions, differences between means and proportions, contrast
analysis of more than two means in independent and repeated measures
designs, and correlation. Tests that will be introduced are the t-test,
chi-square test, and the ANOVA F-test.

This course deals with a number of stochastic modeling techniques that are often used in practice. They are motivated by showing the business context in which they are used. Topics we deal with are: time-dependent Poisson processes and infinite-server queues, renewal processes and simulation, birth-death-processes, basic queueing models, and inventory models. We also repeat and extend certain parts of probability theory.

The Applied Stochastic Modelling course provides you with an insight into mathematical modelling and the way it is used in practice. You will explore a number of stochastic solution methods.

Topics that are dealt with are: birth-death-processes, basic queueing models, inventory models, renewal theory and simulation. We also repeat and extend certain parts of probability theory.

The development of modern computer technology requires professionals with a background in all science fields, who understand both hardware and software. The interaction between the hardware and software on a variety of levels provides a framework for understanding the fundamentals of computing. Whether your primary interest is hardware or software, computer science or electrical engineering, the central ideas remain the same. This course will show the relationship between hardware and software, and focus on the concepts that are the basis for today’s computers.

This course is only available in Dutch.

Archival and Information Studies offers two different tracks: 60 EC track Information Studies and a 90 EC (dual) track Archival Studies.

The Information Studies track of the Master’s in Archival and Information Studies is a one-year master’s programme for students who are interested in the critical and practical examination of this new shifting informational landscape. The track is an interdisciplinary programme taught by, archivists, information scientists, cultural theorists, and heritage specialists. It treats contemporary information theory and practice, trains students for the diversity of professional information roles and provides insight into both the history of information technology and practice, as well as its future directions.

Artificial Intelligence is about analysing and automating tasks that require intelligence. In other words, you teach machines to be as intelligent as possible. Computer systems to detect credit card fraud, or a telephone-based railway route planner which understands spoken language: these are just two examples of how artificial intelligence can be applied.

Computers are increasingly used to support people, in decision-making or in independently performing tasks requiring intelligence. Humans are an important source of inspiration for artificial intelligence. You cannot analyse and automate intelligence without understanding what human intelligence is; in other words, how people learn and reason. The Bachelor’s programme in Artificial Intelligence therefore focuses on cognitive psychology, logic, linguistics and philosophy.

The Artificial Intelligence (AI) Master’s programme in Amsterdam has a technical approach towards AI research. It is a joint programme of the University of Amsterdam and Vrije Universiteit Amsterdam. This collaboration guarantees a wide range of topics, all taught by world renowned researchers who are experts in their field. The primary focus is on the development and understanding of intelligent computational processes in order to create useful artefacts, as well as to aid in understanding (human) intelligence. In the programme, you acquire a working knowledge of efficient, robust and intelligent methods for interpreting sensory and other information from different modalities.

AI is a field that develops intelligent algorithms and machines. Examples include: self-driving cars, smart cameras, surveillance systems, robotic manufacturing, machine translations, internet searches, and product recommendations. Modern AI often involves self-learning systems that are trained on massive amounts of data (“Big Data“), and/or interacting intelligent agents that perform distributed reasoning and computation. AI connects sensors with algorithms and human-computer interfaces, and extends itself into large networks of devices. AI has found numerous applications in industry, government and society, and is one of the driving forces of today’s economy.

Immerse yourself in the multidisciplinary study of mind and cognition. Researchers in Cognitive Science come from a wide range of backgrounds, including psychology, computer science, artificial intelligence, philosophy, mathematics and neuroscience. They all share the common goal of gaining a deeper understanding of the human mind, for both theoretical and practical purposes.

The track focusses on the processes that underlie human functioning from two different research perspectives: empirical work and computational modelling. The combination of these two perspectives allows for a better understanding of the mechanisms underlying human functioning.

Artificial Intelligence is widely adopted in our society: think of face recognition in your smart phone, decision support for medical doctors, or smart homes for assisted living. At VU, you can study AI technology from a societal perspective: how can we develop and evaluate computer-based technology that exploits knowledge about human functioning. There are two tracks: one focusing on how to interpret, support and adapt to human behaviour, another on understanding cognitive aspects of AI. The two tracks Socially Aware Computing and Cognitive Science are oriented towards the practical application of AI technology in a broad, problem-oriented setting. You’ll learn how to model both mental and physiological processes of human functioning. Such models are the basis of intelligent applications that support humans in their daily lives in a dedicated manner, also to enable that support systems to understand humans better.

This course is part of the Mastermath programme. All information can be found at the Mastermath website.

In this course we will explore ways in which a researcher can approach data. For example, we will explore the theory behind machine learning, its application and its limitations. This course provides theoretical knowledge and subsequently challenges the students to apply this knowledge practically.

This course is designed to provide students with the background in discrete probability theory and programming that is necessary to follow other more advanced master-level courses in areas such as linguistics, natural language processing, machine learning, complexity theory, cryptography, information theory, quantum computing, combinatorics, etc. The goal is to make students that have had no prior exposure to probability theory and/or programming feel comfortable in these areas. To achieve this goal we will try to illustrate the theoretical concepts with real-life examples that relate to topics in, e.g., computer science, gambling, and the like. Moreover, we will make sure that there is a close tie between the theoretical and practical part of the course, thus enabling students to apply their newly acquired theoretical knowledge to real problems.

This course is designed to provide students with the background in discrete probability theory and programming that is necessary to follow other more advanced master-level courses in areas such as linguistics, natural language processing, machine learning, complexity theory, cryptography, information theory, quantum computing, combinatorics, etc. The goal is to make students that have had no prior exposure to probability theory and/or programming feel comfortable in these areas. To achieve this goal we will try to illustrate the theoretical concepts with real-life examples that relate to topics in, e.g., computer science, gambling, and the like. Moreover, we will make sure that there is a close tie between the theoretical and practical part of the course, thus enabling students to apply their newly acquired theoretical knowledge to real problems.

Social science is best characterized as an ongoing activity, namely the creation of new knowledge. This activity follows rather strict rules in order to gain acceptance by the scientific community.

This course covers all discipline-independent aspects of creating new knowledge:

• How to formulate a scientific question

• How to plan an investigation bearing on that question

• How to conduct the inquiry

• How to present the data that result from your research

• How to interpret your results, and extrapolate beyond your data

• How to report the results in an appropriate way

The central question we will address in this course is this: How do I conduct research such that it will yield conclusions that are acceptable to critical peers? Central concepts:

• The Empirical Cycle: start with a problem or question, conduct experiments / observe reality, interpret data in light of the original problem, form a tentative conclusion, make predictions.

• Experimental design: organize experiments / observations such that they allow unequivocal interpretations.

• Statistics: a branch of probability calculus that deals with data analysis. It aims to extrapolate the results of an investigation beyond the boundary of the sample by adding an element from outside the investigation: a mathematical description of the data. This addition allows for making quantitative judgments about the meaningfulness of the data.

• Communicating results: presenting findings using oral and written reports.

The central aim of the course will be that students

acquire the skills and knowledge to conduct research,

and quantitatively analyze data. The central question we

will address in this course is: How do I design research

and analyze data such that it will yield conclusions that

are acceptable to critical peers? On one hand, this course

will further students’ quantitative skills developed in

BRMS I (1st year course); various quantitative methods

commonly used in social science research are covered.

On the other hand the steps in designing research

(formulating research questions, operationalizing,

planning and conducting an empirical study) will be

treated.

The course will consist of an alternating series of

interactive lectures and practicals in which students

learn the theoretical background as well as the

application of several statistical techniques, including:

correlation, reliability, multiple linear regression, and

ANOVA. The practicals will mostly be used to learn

coding, analyzing, reporting and interpreting data using

SPSS.

In order to become a skilled and versatile methodologist, you will need a rich methodological toolbox. But before acquiring those tools, in this first course of the first semester of the PML specialization, you will build the box itself. Boring you say? No! The box will give you the fundament for all the advanced tools you will learn during the remainder of the programme. You will build your box from the basics of mathematics, statistical programming, and statistics. The more solid these fundaments are, the fancier your box will be, and the better you’ll be able to build your statistical toolset during the subsequent courses.

Mathematics

In this module, you will improve your algebra skills. You will simplify and solve equations involving logarithmic and exponential functions. In addition, you will learn how to differentiate functions. These skills will help you to compute maxima and minima of functions. Finally, you will learn how to conduct matrix algebra.

Statistical programming

In this module, you will learn the programming language R (and thereby fully replace SPSS). We will focus on working with datasets, using and programming your own functions, plotting and basic programming routines (if-statements and loops).

Frequentist statistics

In this module, you will use R simulations to gain a conceptual and practical understanding of fundamental concepts in statistics. We will cover elementary probability theory (e.g., marginal, joint, and conditional probabilities), probability distributions (e.g., discrete and continuous distributions), hypothesis testing (e.g., central limit theorem, standard deviations, p-values, confidence intervals, power), and regression analysis (e.g., polynomial regression, model selection, overfitting).

This course (named Computational Econometrics (E_EOR3_CE) in the past
academic years) will cover Bayesian statistics where the topics include
the prior and posterior density, Bayesian hypothesis testing, Bayesian
prediction, and Bayesian Model Averaging for forecast combination.
Several models will be considered, including the Bernoulli/binomial
distribution, the Poisson distribution and the normal distribution.
Obviously, attention will be paid to the Bayesian analysis of linear
regression models. Also simple time series models will be considered. An
important part of the courses is the treatment of simulation-based
methods such as Markov chain Monte Carlo (Gibbs sampling, data
augmentation, Metropolis-Hastings method) and Importance Sampling, that
are often needed to compute Bayesian estimates and predictions and to
perform Bayesian tests.

This course will cover Bayesian statistics where the topics include the prior and posterior density, Bayesian hypothesis testing, Bayesian prediction, and Bayesian Model Averaging for forecast combination. Several models will be considered, including the Bernoulli/binomial distribution, the Poisson distribution and the normal distribution. Obviously, attention will be paid to the Bayesian analysis of linear regression models. Also simple time series models will be considered. An important part of the courses is the treatment of simulation-based methods such as Markov chain Monte Carlo (Gibbs sampling, data augmentation, Metropolis-Hastings method) and Importance Sampling, that are often needed to compute Bayesian estimates and predictions and to
perform Bayesian tests.

Frequentistische statistiek is gebaseerd op de veronderstelling dat de data verdeeld is volgens een onbekende distributie. Bayesiaanse statistiek gaat uit van een ander beginsel, waarin data en parameter op gelijke voet behandeld worden. De Bayesiaanse procedure vereist naast specificatie van het model ook keuze van een zogeheten prior verdeling over het model. De data wordt ingezet om de prior om te zetten in de zogenaamde posterior verdeling. In dit vak kijken we naar enkele gangbare statistische vragen, zoals schatting van de model parameter, toetsing van hypotheses, constructie van betrouwbaarheidsintervallen en het opstellen van beslisfuncties, waarin telkens frequentistische en Bayesiaanse methoden worden beschreven en vergeleken. Voorts wordt ruim aandacht geschonken aan de keuze voor de prior, die afhangt van zowel het statistisch model in kwestie, als het beoogde doel van de posterior.
Dit vak gaat niet in op computationele aspecten, en slechts in beperkte mate op niet-parametrische en asymptotische eigenschappen van de posterior. Het hoofddoel is begrip van de basiselementen, waarop voortgebouwd kan worden in puur statistische vorm, of in de vorm van artificiele intelligentie en machine learning.

This course is only available in Dutch

This course is part of the Mastermath programme. All information can be found at the Mastermath website.

At the end of this course, students can (a) name key components of Bayesian statistics (paraphrasing), (b) paraphrase basic aspects of Bayesian statistical methods, and (c) analyze psychology papers that use these methods. Based on an evaluation of the studied materials, students are also able to (d) program rudimentary probabilistic models and statistical methods (scientific thinking) that are then used to (e) analyze practical research questions (evaluation). Students can (f) report their thinking in short essays (written communication and reflection).

Modern machine learning methods are based on mathematical concepts, especially from probability theory and statistics. This course treats these concepts in detail, through the spectrum of the Bayesian school of thought in machine learning. This will lay the groundwork for a solid understanding of advanced machine learning methods taught in other courses. Additionally, the mathematical theory will be made more concrete through programming exercises.

Het vermogen van organisaties om data (‘big data’) om te zetten in kennis en inzichten – ook wel business intelligence, business analytics of data science genoemd – wordt steeds belangrijker. Organisaties voorzien een toenemende behoefte aan kennis en vaardigheden die nodig zijn om data om te zetten in actiegerichte inzichten.

Om in deze behoefte te voorzien, heeft het Amsterdam Center for Business Analytics (ACBA) van de VU de postgraduate opleiding (PGO) ‘Business Analytics & Data Science (BADS)’ ontwikkeld. De PGO is gebaseerd op vakken binnen de bestaande VU-programma’s Bedrijfskunde en Business Analytics en biedt een brede, algemene basis voor iedereen die zich verder op het gebied van business analytics en data science wil ontwikkelen.

De opleiding is zowel geschikt voor personen die zich bezighouden met het omzetten van data in ‘actionable insights’ als voor personen die de resultaten van en behoefte aan dergelijke actiegerichte inzichten moeten definiëren en beoordelen (tactisch en strategisch management).

The course focuses on the modeling, analysis and optimization of
complex decision making processes which involve human behavior (such as
selfish, risk-averse, altruistic or malicious behavior). Building on
game-theoretic foundations, you learn to model processes of complex
decision making and to quantify the inefficiency caused by human
behavior.

The main goal of the course is to equip you with algorithmic
optimization techniques to master the challenging task of reducing the
inefficiency of such processes. These techniques find their applications
for example in Traffic Routing, Network Design, Cost Sharing,
Resource Allocation and Auction Design.

Understanding data about human behaviour is an important and valuable skill in today’s society. Companies, public institutions and governmental organizations — they all use the continuous stream of big data to describe and predict human behaviour.

The police use data to predict risk of burglary by area and week of year, insurance companies adjust their prices based on client data, and schools adjust educational programmes based on what is known about student progress.

Leveraging the full potential of these massive amounts of behavioural data towards these goals greatly benefits from a thorough understanding of data science techniques and human behaviour modelling. The master’s track Behavioural Data Science aims to combine these two. The programme assumes knowledge of psychology, research methodology, and applied statistics at an undergraduate level, and continues with training on advanced (big) data techniques, academic skills, as well as practical and professional skills.

Learning about datasets is at the core of this course. When looking at datasets, students will first learn to see if they are dealing with a more traditional Data Warehouse / Business Intelligence dataset, or if there are big data related issues. With the help of technical tools for traditional data issues (ETL, DWH, Cubes) or big data issues (NoSQL, Hadoop, Mahout, R) students will either build traditional data warehouse solutions or big data solutions. This course is in Dutch.

This course aims to provide an introduction into the main challenges that big data applications pose across all layers of a processing system, from its infrastructure to its performance. The common solutions – from design to implementation – that are being used to tackle these problems will be presented. Specifically, students will be introduced to storage and processing solutions, infrastructure options, performance challenges, systems and tools for data analytics at scale. Finally, the different success metrics to be used for these solutions will be introduced. Additionally, ethical concerns, as well as interaction with traditional data producers and consumers will be discussed. Therefore, upon completing this course, the students should be able to design a big data analysis framework, reason about its infrastructure requirements, and provide a prototype implementation using modern tools and technologies.

Overview of big questions in (geospatial) data and its practical and ethical implications for society

– Students can demonstrate a basic knowledge of the aspects of data, including an understanding of how data is collected, stored, and used and gain practical experience in all these aspects.
– Students can demonstrate an understanding of how algorithms and visualization tools are used to extract patterns and knowledge out of data and have a hands-on experience with such tools.
– Students can demonstrate an understanding of how data is used within academia, business, and
governments and civil society.
– Students can demonstrate an insight into the societal impacts, e.g. benefits vs intrusion of privacy, that come with the datafication and have the breadth of knowledge to form and articulate a position on these issues.
– Students can demonstrate an ability to describe and evaluate their own learning in relation to the above learning outcomes.

This course is open only to AUC students

Big data analytics encompasses many techniques necessary for extracting insights from large amounts of data. After this course students are familiarized with various aspect of big data analytics, and will have hands-on experience with these techniques applied to a wide range applications using a wide range of software tools (R, Excel, Tableau, ggplot, MySQL, Shiny). Students will be able to handle data from various sources, slice and dice data, build predictive and analytic models, and visualize derived data insights. Student will not only be able to conceptually explain the various machine learning techniques (supervised: regression, regression trees, bagging, boosting, random forests, k-nearest neighbours, logistic regression, classification and regression trees, support vector machines, (convolutional) neural networks, discriminant analysis; unsupervised: principal components analysis, clustering, k-means), but will also be able to explain how they overlap with statistical and psychometric methods learned in bachelor course. As this is a first exposure for students into the field of big data analytics and machine learning, students are encouraged to deepen gained insight in elective specialization courses later in the year.

The seminar will provide insight in the basic concepts, challenges and opportunities associated with data so large that traditional research methods (like manual coding) cannot be applied anymore and traditional inferential statistics start to lose their meaning. Participants are introduced to strategies and techniques for capturing and analyzing digital data in communication contexts, through concrete examples and templates than can be shared and modified for the students’ own research projects. We will focus on (a) data harvesting, storage, and pre-processing and (b) computer-aided content analysis, including natural language processing (NLP) and computational social science approaches.

In the internet era, data plays center stage. We all continuously communicate via social networks, we expect all information to be accessible online continuously, and the world economies thrive on data processing services where revenue is created by generating insights from raw data. These developments are enabled by a global data processing infrastructure, connecting the whole range from small company computer clusters to data centers run by the world-leading IT giants. In the Big Data Engineering track you study the technology from which these infrastructures are built, allowing you to design and operate solutions for processing, analyzing and managing large quantities of data. This track is part of the joint Master in Computer Science, in which renowned researchers from both VU and UvA contribute their varied expertise in one of the strongest Computer Science programmes available in Europe.

Two-day overview course on Big Data for (project) managers and consultants. Look beyond the hype: the terms, techniques and technologies of Big Data by experienced data-consultants and university professors.

This elective addresses important concepts in bio- and neuroinformatics
and big data analysis, with powerful applications in neurosciences. Lectures
and practical assignments provide theory and hands-on experience in fast
moving fields of exploratory, graph and predictive data analytics,
neuroscience, connectomics and metagenomics.

Due to the growing digitisation in society, increased
international collaboration, data sharing and technological advances,
there is a vast increase in the volume and complexity of data sets that
are analysed in modern science. One social media experiment, brain
imaging study or DNA sequencing project easily encompasses terabytes of
information.
Large data volumes, commonly referred to as “Big data”, have many
advantages (more information, increased statistical power, mining for
previously unknown relations) but come with the need for special
strategies and approaches both to manage the data (transfer, storage,
updating and sharing) and process and analyse the data (cleaning,
visualization, querying, statistics, mining). In addition concepts such
as ethics and information privacy need to be considered. In this course
large data sets from experiments in cognitive and/or biological
psychology are the central theme. In the individual tutor meetings the
students run through the following steps (1); obtaining and examining
datasets and drawing-up hypotheses based on classical methods of
cognitive / biological psychology; (2) preparing the data for analysis;
(3) performing the analysis and interpreting the outcome; (5)
writing a research proposal and presenting the proposal using
PowerPoint.

In this module we dive into cloud technologies that allow organizations to tap into potentially thousands of computers at the click of a button at little upfront cost. We also explain the software that is used to do this and also to program such compute clusters, in order to use them for addressing Big Data problems. More info here: www.cwi.nl/~boncz/bads

Please note that this course cannot be followed separately.

Nowadays it is easy to measure, collect and store a large number of
observations. However, many of the traditional inferential methods were
developed for small data sets. While the questions traditional
inferential methods tried to answer like ‘What can we learn from the
data’ or ‘How can we use the data to make predictions for future
observations’ are as important as they were many years ago, the methods
we need to answer these questions when confronted with big data sets
have been developed only recently. In this course, we will first study
the methods that have been introduced for hypothesis testing of large
data sets. Next, we learn more about regression models for big data
sets. Moreover, we discover some of the possible pitfalls arising from
electronic computation and study selective inference.

After this course, the student should be able to: Drive focus on the critical Big Data opportunities (goal); Assess readiness on opportunity capture, metrics and models, technology, and people (situation); Develop a coherent vision and road-map to capture (direction); Lead a Big Data initiative to success (execute).

In this course we dive into cloud technologies that allow organizations to tap into potentially thousands of computers at the click of a button at little upfront cost. We also explain the software that is used to do this and also to program such compute clusters, in order to use them for addressing Big Data problems.

Deze cursus heeft als belangrijkste doel het praktisch te leren omgaan
met kleine en grote biologische data sets. Technieken voor het werken
met bestanden en databases, het samenvoegen van tabellen, het grafisch
weergeven en analyseren van grote data sets en  reproduceerbaar werken
zullen behandeld worden. De technieken worden geoefend aan de hand van
gegevens uit verschillende vakgebieden van de biologie. De gebruikte
programmeertaal is R.

This course is only available in Dutch

The question addressed in this course is how digital technologies and
big data are used to make decisions in human rights and security
domains, and how these uses require us to rethink the basic tenets of
existing legal norms and practices. The course focuses on three human
rights and security domains in particular: big data and social and
criminal justice; the use of digital technologies in warfare and the
fight against terrorism; and the use of technology in border management
and migration law. Students will become familiar with the legal
framework regulating the collection, use and analysis of big data, and
the storage and exchange of personal data through the use of digital
technologies. For this purpose, they will be introduced to EU and
international laws on privacy and data protection, and these laws will
be fleshed out with regard to the three case studies.
Data privacy laws can serve as tools to focus microscopically on the
ways in which the use of big data and digital technologies pose
challenges to individual rights, and clarify possible remedies for these
challenges (for example by de-identifying data, foregrounding the
question of consent to the collection, use, or disclosure of the data,
or through the concept of purpose limitation). Students will learn how
to apply the principles of data protection laws in the three human
rights and security domains, and become aware of the particular problems
that feature in these particular domains. But through the case studies,
the course will also require students to reflect on the more fundamental
question as to what extent legal responses to human security threats are
undergoing a fundamental shift through the use of modern technologies.
Thus, it has been argued that these technologies facilitate a
“fundamental jurisprudential shift from our current ex post facto system
of penalties and punishments to ex ante preventative measures that are
increasingly being adopted across various sectors of society.” Whether
such a shift to from individual justice to so-called actuarial justice
is currently taking place will be discussed through looking at diverse
fields such as predictive policing, the use of smart city technology for
crowd control and surveillance, the use of big data and algorithmic
decision-making for tracking, capturing, killing or blacklisting
suspected terrorists, and the use of modern technologies in the
implementation of border and immigration policies.

This course is only available in Dutch

Today’s digital and information-dense society produces a massive amount
of data. Much of this data is generated by or related to human behavior
and can inform social scientists about societal dynamics. Examples
include social media data, parliamentary minutes, email collections or
collections of stories that have been made available in a digital
format. In this course, students learn about such digital trace data,
that are often voluminous, unstructured, and/or embedded in complex data
structures – we refer to such data as Big Data. Students learn about how
Big Data differs from data generated by traditional social science
research methods and the opportunities and challenges that Big Data pose
in present day society. They are introduced to R, a programming language
which they will use to gather and link data, and to make sense of these
data. Students learn about ways to analyze data derived from social
media such as forums or social network sites by using both computational
and interpretive approaches. The latter are key to Small Data, data with
meaning to individual citizens.

This minor is open to undergraduate students in Computer Science, Information, Multimedia and Management, Lifestyle Informatics, Biology, Medical science, Biomedical Sciences, Chemistry, Mathematics, Physics and students of related courses. Also students in 3rd or 4th year of a Bachelor Bioinformatics study are welcome. With this minor, you can move on to MSc Bioinformatics (and Systems Biology).

In the first two months, this minor introduces you to Bioinformatics and Systems Biology and examples from scientific research. The last three months are used to provide supplementary knowledge, for example, programming for students with a Bachelor in Biology, Biology for students with a background in Computer Science and Mathematics or Statistics for students of HBO Bioinformatics training.

Observations from biological high-throughput experiments will allow us to improve diagnosis and give a personalised treatment plan for patients. However, integrating data from several sources and using this data for predictions is non-trivial. This is a theoretical and practical Bioinformatics course on computational methods for Translational Medicine; we will focus on Bioinformatics algorithms that are used to predict the clinical outcome for patients and analysis methods to obtain deeper understanding of complex diseases, by combining data from various high-throughput experiments such as proteomics, microarrays and next-generation sequencing as well as existing biological databases.

In the analysis of biochemical systems, many measurements are performed, leading to complex multivariate data sets. The tendency is to measure more and more of just a few samples. Multivariate data analysis methods are often used to explore such sets. This course covers a broad range of multivariate data analysis methods, for e.g. exploration, clustering, classification. The latter is especially important in biomarker discovery. Design of experiments and ANOVA for multivariate data is also discussed. Furthermore, the interpretation of selected features in terms of function and networks is discussed.The course starts with an introduction on the properties of the different types of functional genomics data.

The purpose of this minor is to acquaint the student with the different fields of Neuroscience. The student will gain insight into the latest knowledge of how the brain works and also how this knowledge can be used to understand cognitive processes, social interactions between individuals, anti-social behavior as well as different brain diseases, such as depression, addictions, attention, or eating disorders. The nature-nurture debate will be discussed as well as recent updates in human genome research. In addition, the minor provides an introduction into the fields of neuro-economics (decision making) as well as into recent scientific technological advances in brain-machine interfaces, deep brain stimulation, and robotics. The integration between disciplines, such as biology, psychology, sociology and genetics plays a central role in this minor. Students learn to think critically about how knowledge of the brain and the human genome can be applied to deal with societal issues.

Business Analytics at VU Amsterdam is a unique program by bringing together a combination of disciplines to prepare you for business success. You will learn how to collect and manipulate large data sets, how to analyse these data using statistics and data mining techniques, and how to use your findings to predict the future and make optimal decisions. You will work on a variety of business problems that modern businesses face every day. An example is the prediction of workload, cargo and the number of passengers travelling via Schiphol or specific airlines. Another example is product pricing for big fast food chains, or segmentation and profiling of customers. You may also work on detecting fraud based on a large dataset of financial transactions, or finding ways to mitigate financial risks for banks or insurance companies. It’s all about turning (big) data into smart decisions.

The Master’s in Business Analytics (BA) is a multidisciplinary programme, aimed at improving business processes by applying a combination of methods based on mathematics, computer science and business management. You will be trained in recognizing and solving in-company problems. BA is a hands-on programme: you will use your expertise in the various fields to improve business practices by examining and analysing real-world situations as faced by companies daily.

The postgraduate programme Business Analytics & Data Science (PGO BADS) is based on courses in existing VU programmes on Business Administration and Business Analytics and offers a broad, generic basis for everyone who wants to develop her-/himself in the area of business analytics and data science.

To prepare optimally for the Business Analytics Master’s you can opt for the minor in Business Analytics. In this minor you will be offered courses that provide additional background in the fields of mathematics, computer science and logistics.

The Research Master Business Data Science is a multidisciplinary research program in which course instruction is provided by top scholars from the three participating Schools with a central focus on the performance of academic research within business disciplines, such as entrepreneurship and innovation, finance, human resources and organization, marketing, and supply chains analytics. The Research Masters prepares talented and motivated students to start their doctorate at one of the Schools in business and economics of the three partner universities or any other high quality PhD program in Business. A job market support program for PhD students will support BDS graduates to enter the international academic job market.

Data is hot! How organizations deal with the overabundance of data and the ability to transform data into insights have become critical success factors for every organization. Key words in this context are ‘big data’, ‘data science’, and ‘data-driven decision making and innovation’. This course offers the handles that are needed to fully deploy the potential of data, and business intelligence & analytics solutions in order to create competitive advantage. The course primarily has a managerial focus, technology will be used primarily to create hands on experience with relevant BI&A technologies and as such enhance insights in their features and characteristics. There is a lot of business involvement in this course: experts from industry and BI&A consultants will share their insights and experience in the weekly workshops.

This module focuses on: objectives and design of the Business Intelligence & Analytics (BI & A) function in organizations, design and content of important BI & A processes, and BI & A project management.

Please note that this course cannot be followed separately.

Business IT & Management is one of the learning paths of the HBO-ICT bachelor programme. In this programme you will gain the knowledge and experience needed to bring ICT and company strategy together. You are a networker and are able to easily communicate with stakeholders on different levels: managers, users and programmers. You combine these social skills with your knowledge of ICT, allowing your projects to run smoothly and in time. The resulting ICT applications will help companies to work more efficiently and more customer-friendly. Business IT & Management is one of the learning paths within the HBO-ICT bachelor programme. Other learning paths are Game Development (GD), Software Engineering (SE), System and Network Engineering (SNE) and Technical Informatics (TI). This course is in Dutch.

Requirements engineering (RE) is concerned with the identification of the goals that need to be achieved by an envisioned system, the operationalisation of these goals into services and constraints, and the assignment of responsibilities for the resulting requirements to agents such as humans, devices, and software. The processes involved in RE include domain analysis, elicitation, specification, assessment, negotiation, documentation, and evolution. Getting high-quality requirements is difficult and critical. Recent surveys have confirmed
the growing recognition of RE as an area of utmost importance in software engineering research and practice. Within this course, we put an emphasis on requirements elicitation,
specification, and modelling. The overall goal is to teach the key concepts related to RE and to create an awareness for the importance of this topic in practice. The various lectures and instructions will be devoted to the following topics:
• Introduction to Requirements Engineering: In this lecture, you will learn what RE is all about, its aim and scope, its critical role in
system and software engineering, and its relationship to other disciplines. You will also learn what requirements are, what they are not, and what good requirements are.
• Requirements Elicitation and Evaluation: In this lecture (and the corresponding exercise session), you will learn a variety of techniques that we may use for understanding the domain in which a software project takes place and for eliciting the right requirements for a new system. What is more, you will learn how to evaluate the elicited requirements.
• Requirements Specification and Documentation: In this lecture (and the corresponding exercise session), you will learn how to specify requirements in a proper way. Among others, we will discuss templates in natural language, diagrammatic notations, and formal specification methods for critical aspects of the to-be system.
• Modelling System Objectives: In this lecture (and the corresponding exercise session), you will learn how to model the functional and non-functional goals of the to-be system. The models you will learn to create are referred to as goal models.
• Modelling Conceptual Objects: In this lecture (and the corresponding exercise session), you will learn how to capture the structural perspective of the system. More specifically, you will learn how to use UML class diagrams to characterise, structure, and inter-relate the conceptual objects manipulated in the system.
• Modelling System Operations and Behaviours: In this lecture (and the corresponding exercise session), you will learn how to model the functional and the behavioural perspectives of the system. To this end, you will learn how to create operational models and behavioural models.

You will tackle quantitative business problems with the aid of mathematical algorithms which are then implemented in decision support systems.

A practical introduction to the different aspects of simulation. During this course we study the different aspects of Monte Carlo simulation and discrete-event simulation in a coherent way. Subjects treated are: Modeling of business problems, statistical outcome analysis, simulation optimisation, software tooling, programming of simulations in Java.

International business administration is a subject in which data are of
prime interest. Is there convincing evidence that your online marketing
campaign results in more sales than your standard newspaper adds? Or
that your increase in employees’ schooling budgets increase company
loyalty? As a business professional, you want to act on evidence, not
only on your gut feeling. Statistics is the key tool that helps you to
analyze and make sense of empirical evidence and to support informed,
data-driven decision making. These skills are highly valued in todays
labor market.

This course is part of your methodological toolkit and the
methodological learning line of your program. It builds on your aptitude
with symbols as built in Business Mathematics, and your critical
academic evaluation as trained in Academic Skills. The course also leads
up to a further deepening of tools in Business Research Methods, and
applications as dealt with in your courses on Business Processes,
Integrative Project, and the Bachelor Thesis.

At the end of this course students are familiar with some basic principles of functions of one real variable, like limit, continuity, derivative and (improper) integral. They also know some important theorems about these topics and are able to solve exercises with various calculus techniques.

At the end of this course students are familiar with some basic principles of series, of functions of several real variables and of ordinary differential equations. They also know some important theorems about these topics and are able to solve exercises with various calculus techniques.

Please refer to the System and Network Engineering web pages for detailed and current course information.

Coding skills are increasingly in demand: they enable scholars to develop the appropriate applications for processing and analyzing data, either big or small.

This course teaches foundational coding skills using Python (a popular programming language), with the goal of:

1. helping students and researchers to understand when and how to automate a task or analyze data programmatically;
2. developing custom applications, rather than using ready-made ones, which can benefit the actual practice of humanities research as well as its outputs.

The course introduces foundational programming concepts (variables, data types, flow control, functions, input/output), and mentions useful extensions focused on data analysis (Pandas) and natural language processing (Natural Language ToolKit).

This is the first course in coding, therefore no prior knowledge is required.

In this course you will learn about the theory of combinatorial optimization problems. Also, you will apply the theory to model and solve complex problems using the available software.
In particular, we consider performance measures for algorithms for combinatorial problems such as the running time and the quality of solutions.

In the study Communication and Multimedia Design (CMD) you will learn to design digital media with a view to the end user. This enables products, services and even entire organizations to improve. This programmes is in Dutch.

Complexity theory deals with the fundamental question of how many resources, such as time, memory, communication, randomness, etc., are needed to perform a computational task. A fundamental open problem in the area is the well-known P versus NP problem, one of the Clay Millennium problems. In this course we will treat the basics of complexity theory, NP-completeness, diagonalisation, Boolean circuits, randomised computation, interactive proofs, cryptography, quantum computing, and circuit lower bounds.

In the course Computational Intelligence, we will focus mainly on computational aspects of Artificial Intelligence, namely, optimization algorithms for solving learning problems. Specifically, we will consider problems that cannot be solved using information about gradient due to their combinatorial character or complexity of the objective function (e.g., non-differentiability, blackbox objective function). These problems pop up in computer science and AI, such as, identification of biological systems, task scheduling on chips, robotics, finding optimal architecture of neural networks. For this purpose, We will introduce different classes of algorithms that can be used to tackle these problems, namely, hill climbing and local search, and evolutionary algorithms. Additionally, we explain sampling methods (Markov Chain  Monte Carlo) and population-based sampling methods, and indicate how they are linked to evolutionary algorithms. In the second part of the course, we will discuss neural networks as current state-of-the-art modeling paradigm. We will present basic components of deep learning, such as, different layers (e.g., linear layers, convolutional layers, pooling layers, recurrent layers), non-linear activation functions (e.g., sigmoid, ReLU), and how to use them for specific problems. At the end of the course, we will touch upon alternative approach to learning using Reinforcement Learning and Bayesian Optimization. We will conclude the course with a recently revived field of neuroevolution that aims at utilizing evolutionary algorithms in training neural networks.

In this course we discuss numerical and simulation-based methods for their use in econometrics and data science. In the first part we review matrix computations, numerical methods for optimization and Monte Carlo integration. We illustrate how these methods are used for estimation of parameters in statistical models. In the second part we investigate properties of estimators, test statistics and residuals using simulation studies. In particular, we simulate distributions of parameter estimates under different data generation processes, but also distributions of
test statistics such as unit-root tests, of R-squared goodness-of fit in spurious regressions, and of model selection criteria such as Akaike information criteria. We finally use simulations to verify the accuracy of diagnostic tests related to normality and heteroskedasticity.

Understanding and predicting developments in our complex world, and transforming them into advanced computer models is what the Master’s programme in Computational Science is all about. This is a joint UvA and VU degree programme. You will look at important questions facing the world now, and the ones it will face in the future, whether they relate to forecasting financial markets, anticipating human behaviour in crisis situations or studying future cities. You will have the opportunity to focus on your specific field of interest, varying from biology and chemistry to mathematics and finance.

Computer Science studies the technology that has become ubiquitous in our global, connected society. Traditionally, the computer had been the primary object of study. Nowadays, globally distributed information processing services have taken center stage, with the Internet connecting a wide variety of information processing devices, ranging from mobile phones to data centers operated by the world leadership companies. Computer Science at the VU and UvA is very broad and wide-ranging compared to other universities. You will choose a specialization/track as part of the programme. Besides the compulsory courses, you will have the opportunity to take optional courses from the whole range of computer science.

Computer Science is all about our interaction with information. That information can range from railway timetables to personal health care data or exciting new virtual games. Computer technology has given us a wealth of new opportunities, but unfortunately it has also created serious risks. The Bachelor’s in Computer Science at VU Amsterdam focuses on both aspects, with Networks and Security as two major topics in the program. The openness and transparency that we value in the Netherlands are reflected in our educational system. You will receive a pro-active and challenging education, and our lecturers’ doors are always open for any questions you might have. Our lecturers combine teaching with top-level research. They also maintain solid relations with various businesses in the field, and projects you will work on during your program are often organized in cooperation with such companies.

The Bachelor’s in Computer Science is offered by the UvA. You will learn to understand computer technology, in all its complexity and about possible applications for the future. During the first year, you will get to know more about Computer Science in the broadest sense, covering subjects such as computer architecture and operating systems. In the second and particularly in third year, you will have the opportunity to focus more on your specific field of interest, which can vary from software engineering to graphics and game technology.

Hardware and software are inseparable: robots in industrial production lines to operating systems of tablets and smartphones. This Computer Science programme is part of the training HBO ICT. You will learn to program for both hardware and software, and specifically hardware and software programs for Embedded Systems, Industrial Automation and Robotics. For this program, it is important that you are willing to learn and understand how technology works. Furthermore, you must have mathematical insight, an inquisitive attitude and enjoy working in a team. At the end of this program, you will be able to develop and maintain innovative ICT solutions, including computer networks, processors, robotics and embedded systems. HBO ICT consists of the learning routes: Business IT & Management (BIM), Game Development (GD), Software Engineering (SE), System and Network Engineering (SNE) and Computer Science (CS).

This course is only available in Dutch.

The Master track in Computer Systems Security focuses on system and security issues related to operating systems, hardware and applications (topics like hacking, malware, reverse engineering, vulnerabilities). This specialization in Computer Systems Security is a joint effort by VU Amsterdam and University of Amsterdam. The emphasis on system related issues is what sets this track apart from other master programmes on security, which tend to have a focus on formal methods or the math behind cryptography. You’ll be taught by leading researchers in the field of computer security. Many of the challenging courses have a very hands-on character.

Students graduating in the Computer Systems Security specialization have knowledge of

• security issues in system-level software including weaknesses and defenses;
• static and dynamic analysis techniques for software (benign and malicious);
• modern scalable computer and network architecture;
• secure software development for modern, highly parallel computer systems.

The Amsterdam-based Master track in Computer Systems Security is unique in the Netherlands in focusing on system security issues in operating systems, hardware and applications. Have you ever wondered how attackers bypass even the most advanced security mechanisms, how we can reverse engineer state-of-the-art malware, or in general, what “hacking a system” is all about? This specialization in Computer Systems Security, a joint effort by VU University and University of Amsterdam, is different from security tracks in other universities which tend to focus more on formal methods or the math behind cryptography. Instead, we focus on systems. Our philosophy is that you learn by doing and, moreover, the way to learn most about security solutions is to break them. So, you will learn how to write exploits and how to bypass some of the strongest defenses commonly deployed. The courses are extremely challenging and most of them have a very hands-on character.

Digital cameras have become ubiquitous in the form of consumer cameras, webcams, mobile phones, and professional cameras. These cameras yield enormous streams of data and provide the means for communication, observation, and interaction. In this course, image understanding is addressed with the focus on core vision tasks of scene understanding and object recognition.

A broad range of techniques are studied on how computers can understand the visual world of humans including image formation and filtering, features (color and shape invariants, interest point detectors, descriptors, SIFT, HoG), visual information representation (vector space, statistical models, bag-of-words), learning and classification (nearest neighbor, kernel density estimation, SVM), dimension reduction (PCA, LDA and SVD), object detection and classification, object tracking (mean-shift, Kalman), and user interaction (active learning).

The field of computer vision is by impact one of the forefront fields of AI.

Computer vision has matured to a degree that many applications have become possible or nearly are possible.

In this course, computer vision is seen as an enterprise that uses statistical methods to disentangle image data using models constructed from geometry, physics, and machine learning.

The course aspires a thorough understanding of many of the current techniques, aiming at a broad basis, and extending to state of the art methods. Topics range from texture analysis, (photometric) stereo, and multiple view geometry, to sophisticated techniques of model based vision, material recognition, tracking, object detection, and scene classification.

To appreciate the nowadays possibilities, practical experience will be gained with some state-of-the-art techniques.

This course provides a comprehensive presentation of the foundations and programming principles for multicore computing devices. Specific learning objectives are: To provide insight into fundamental notions of multicore computing and their relation to practice: locks, read-modify-write operations, mutual exclusion, consensus, construction of atomic multi-reader multi-writer registers, lost wakeups, ABA problem. To provide insight into algorithms and frameworks for multicore computing and their application in multi-threaded programs: mutual exclusion algorithms, spin locks, monitors, barriers, AtomicStampedReference class in Java, thread pools in Java, transactional memory. Analyzing algorithms for multicore computing with regard to functionality and performance: linearizability, starvation- and wait-freeness, Amdahl’s law, compute efficiency gain of parallelism. Mastering elementary datastructures in the context of multicore computing: lists, queues, stacks. Programming in multi-threaded Java, and performing experiments with such programs.

A number of disruptive innovations are coming to the consumer space and more generally to every consumer’s life. Companies, governments, and technology influencers like Stephen Hawking and Elon Musk emphasize the need for understanding how consumers/employees will react, and advocate a focus on the behavioural and ethical consequences.

This course takes a future-oriented perspective above and beyond current developments such as social media and “big data”, and aims to enhance the understanding consumer behaviour in the age of disruptive innovations. In detail, it

1) introduces a number of (marketing-relevant) disruptive innovations, such as:

• Humanoids and Robots
• Human Enhancement Technology & Cyborgism
• Artificial Intelligence
• Blockchain
• Virtual Reality and Augmented Reality
• Future of Food (sonic-enhanced food, lab-grown meat, insects)

2) teaches theories from marketing, psychology, neuro-science and information science to explain consumer acceptance, biases, and behavioural change upon exposure to these disruptive innovations,

3) provides insights into (theory-based) strategies companies can apply when introducing and using disruptive innovations with a focus on consumers (e.g., in product design, launch communication).

The course builds on the foundations provided by the courses Finance / Financiering, Investment and Portfolio Theory 1 and Investment and Portfolio Theory 2. Starting from the unifying principles of value maximization and the law of one price, we discuss how successful organisations value and identify profitable investment opportunities, manage their capital structures and create shareholder value. Some of the topics covered during the course include: capital budgeting and valuation methodology, managing and valuing real options, the choice between debt and equity, managerial incentives and executive compensation, mergers and acquisitions, restructuring, initial public and seasoned equity offerings and time permitting, corporate governance. Our discussion of the key concepts of corporate finance will be illustrated with real-world business cases and analysed with rigorous methodology.

The aim of this course is to provide an in-depth understanding of the tools and concepts of modern corporate finance. After successfully completing this course students:

• understand the economic goals corporate finance policies are meant to achieve;
• have learned how to learn and develop the skills to propose and defend ideas;
• effectively and enthusiastically participate in real-world competitive and professional environments.

The scope of corporate strategy goes beyond the individual product market, instead focusing on how firms can create value across different businesses. Thus, how can the company create value above and beyond the value created by the individual business units. In this course we will focus on three main decisions: Corporate governance (ownership vs. control and stakeholder strategy), Business portfolio choices (growth and vertical and horizontal integration), and organisation (The role of corporate headquarters, organisational structures).

Upon completion of this course students have the ability to:

• explain how important theories in the field of strategy (e.g. agency theory, transaction cost economics and resource-based view) inform our understanding of firm’s corporate strategy (specifically: corporate governance, vertical (dis)integration, growth, diversification, and organisational structures).
• compare and criticise these theories, including their ethical implications for practice, based on a good understanding of their limitations;
• apply the theories discussed in the course to determine the best course of action regarding strategic issues faced by firms.

We hear almost everyday in the news that governments and companies have to deal with cyber attacks and data leaks. Recognized digi disruptions such as blockchain, robotisation and internet of things require specialists who can provide a stable and secure ICT service.  The System and Network Engineering (SNE) learning path has therefore been changed to Cyber ​​Security (CS). The courses in the area of ​​CS have been tightened up in terms of content and the focus is shifting to CS. You learn to analyze, design, realize and manage technical infrastructures on the basis of a system and network architecture. For this you must be able to work accurately and be interested in control and computer systems, new technologies and the internet. You advise companies which ICT solution is best for their organization and ensures stable, secure connections, networks and systems. HBO-ICT consists of the learning routes Business IT & Management (BIM), Game Development (GD), Software Engineering (SE), Cyber ​​Security (CS) and Technical Informatics (TI). This course is in Dutch.

Digital Forensics on Systems and Networks are the main focus-point of the course. Topics are a proper forensics methodology to acquire and analyze evidence from a wide variety of sources. Legal as well as ethical aspects of digital forensics are discussed. In a forensics project students work to develop new techniques and prototype tools to further the field.

This course teaches how to apply computing technology to financial trading strategies. This is a broader set of skills than simply writing programs that execute trades. Students who complete this course will gain intuition into how financial trading strategies depend on relationships between different securities’ prices, or between prices and firm characteristics. Students will also learn that an algorithm’s fundamental purpose is to provide a set of detailed instructions for a computer to execute.

To achieve these goals, the course will demonstrate how to use the Python language to program various financial algorithms, such arbitraging price discrepancies between related securities or factor models that identify stocks with high returns. The first 1/3 of each lecture will explain the theoretical relationships behind each financial trading strategy. The other 2/3 of each lecture will demonstrate how to write programs that execute the strategies. Throughout the course, students will also learn the limitations of trading algorithms.

The programming section of each lecture will emphasize the importance of breaking down a trading strategy into a specific set of steps. It will then show how to solve each step using Python. Weekly in-class assignments will help students to gain practice with coding.

By the end of this course, students should possess a good understanding of the foundation of financial trading algorithms, and how to think like programmers. The goal is to provide enough knowledge of the fundamentals of finance and computer science that students can subsequently teach themselves how to program additional trading strategies.

One of the most important recent innovations in journalism is the increasing use of data. Often referred to as data journalism, we see a development of using computational techniques to make use of, for instance, massive sets of documents (e.g., leaks), or government data, provided via APIs or scraped from the web.

In short, the increased availability of digital data, fueled by the trend towards open governance or the use of online media, has opened new ways for journalists to discover and research interesting and relevant stories. While the use of data in journalism is not new, the amount of data and their digital nature require new skills from journalists. At the same time, audiences are demanding greater transparency from news organizations, and the news cycle is ever-more choked with content, both of which challenge journalists to use data in ways that are creative, compelling, transparent, and innovative.

This course combines discussion of these developments with practical skills training. Students will be introduced to the programming language Python, widely used for retrieving data from the web and analyzing both textual and numerical data. Additional topics include data visualization, finding stories in large amounts of data, and cleaning messy data.

This course surveys basic data-mining techniques and their application in solving real-life problems in such areas as marketing, fraud detection, text and web mining, and bioinformatics.

The course is intended to introduce Data Mining Techniques to students that are new to the field as well as to more experienced students. The main aim is to gain a more practical perspective towards Data Mining Techniques/Machine Learning. Lectures will cover more basic things for those new to the field (general introduction into Data Mining, classical algorithms such as decision trees, association rules, neural networks, ensemble learning, etc.) and on top will discuss advanced topics including deep learning, recommender systems, big data infrastructures, and text mining. A number of successful applications in the area will also be discussed. In addition to lectures, there will be an extensive practical part, where students will experiment with various data mining algorithms and data sets. The grade for the course will be based on these practical assignments (i.e., there will be no final examination).

In this course you will build your own toolkit of useful programs for processing and visualizing data that you may encounter in your field. The theory of visualizations will be discussed. After this course you can follow the programming project to apply your knowledge to a project examples in your own field.

In this course you will work with large databases, often the web. Both text and numerical data will be used. Analyses will mostly be with the Python’s module pandas. Specifically, you will learn to edit typical data science data: (very) large amounts of text with annotations, represented in a database, spreadsheet or XML, or as a collection of text files, for scientific research. This includes transforming, annotate, categorize, classify and organize data. All through computers, and as little as possible by hand. You will learn to evaluate the quality of a computer program by edited data. You will learn to work quickly and effectively with ipython notebooks, Python, and various modules for Data Science, in particular, pandas. You will gain knowledge of results with eHumanities and computational social science techniques, and the code to reproduce similar results in comparable datasets.

In the one-year Data Science Master’s track, you will acquire knowledge of the theories and tools used in data science. We will teach you how to use these tools for working with data in different domains, such as Healthcare, Media and Communication, Smart City, Life Sciences and Digital Humanities. Graduates have an integrated view on the possibilities and development of data science in society.

Students will benefit from the strong collaboration with Amsterdam Data Science (ADS), bringing together leading researchers across the entire life cycle of data science, from expertise in machine learning and information retrieval to human computer interaction and large-scale data management.

The Econometrics Master is a multi-disciplinary programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages such as Eviews, R and Matlab, to explore and analyse problems in economics and finance.

The specialisation/track Data Science and Business Analytics deals with large and complex data from widely different sources for the use in economics and business.

This interactive programme offered by the Amsterdam Business School will be taught by experienced university lecturers and consultants, who will share their knowledge and expertise on the topic. There will be plenty of room for discussion and work-related questions.

Modules

Join 1 or all of the 5 hands-on Data Science Essentials modules, the next edition will start in 2021. You will learn the key concepts of working with data and analytics techniques and grow the skills to efficiently run data science projects, answering your organisation’s key questions.

Module 1: Python Programming Skills
Module 2: Data Cleansing and Visualisation
Module 3: Business and Soft Skills
Module 4: Applied Machine Learning
Module 5: Applied Optimisation

De module DSA1 heeft als doelstelling om de student de eerste beginselen van data science in auditing bij te brengen. Doel is het zelfstandig kunnen ontwikkelen van onderdelen van een datagedreven audit-aanpak, waarbij eenvoudige analysetechnieken in de praktijk kunnen worden gebracht. Na afloop moet de student in staat zijn zelf ideeën op dit gebied te ontwikkelen, de opgedane kennis in de praktijk toe te passen, om te gaan met onvolledige informatie over dit onderwerp en zijn of haar kennis met andere professionals te delen.

De stof wordt behandeld aan de hand van een casus, waarbij de data-gedreven accountantscontrole van een havenbedrijf centraal staat. Binnen deze casus lost de student eenvoudige vraagstukken op in de programmeertaal R in een speciaal hiervoor ontwikkelde Jupyter-omgeving.

De module sluit aan bij de Eindtermen met raakvlak stream ICT – variant Assurance uit de Eindtermen Accountantsopleidingen, voor zover deze niet in andere modules aan de orde komen.

This course is only available in Dutch

De module DSA2 bouwt voort op DSA1 en heeft als doelstelling om de student bij te brengen hoe uit de statistiek bekende exploratieve en confirmatieve analysetechnieken in een datagedreven audit-aanpak kunnen worden ingezet. Na afloop moet de student in staat zijn zelf ideeën op dit gebied te ontwikkelen, de opgedane kennis in de praktijk toe te passen, om te gaan met onvolledige informatie over dit onderwerp en zijn of haar kennis met andere professionals te delen.

De stof wordt behandeld aan de hand van een aantal cases, waarbij de data-gedreven accountantscontrole centraal staat. Binnen deze cases lost de student eenvoudige vraagstukken op in de programmeertalen R en Python in een speciaal hiervoor ontwikkelde Jupyter-omgeving.

De module sluit aan bij de Eindtermen met raakvlak stream ICT – variant Assurance uit de Eindtermen Accountantsopleidingen, voor zover deze niet in andere modules aan de orde komen.

This module is only available in Dutch

Na het afronden van deze module beschikt de student over kennis, inzicht en vaardigheden op de volgende gebieden:

• Data-driven auditaanpak
• Steekproeven en het testen van hypothesen
• Extract, Transform, Load
• Correlatie en regressie
• Classificatie
• Visualisatie
• Data-analyse in fraudeonderzoek
• Process mining
• Advanced analytics (big data, machine learning)

This course covers the basic theory of multivariate data analysis with a focus on the most relevant multivariate techniques, as well as their application to econometric data in computer practicals.

Topics include: Introduction to Python, NumPy and Pandas; data scraping, cleaning and wrangling; data visualisation; model evaluation; cross-validation; shrinkage methods: ridge regression + lasso; principal component analysis; discriminant analysis; nearest neighbour methods; model averaging.

Big data refers to data that are more voluminous, but often also more unstructured and dynamic, than traditional data. This concerns, in particular, data-collection that draws on Internet-based data sources such as social media, large digital archives, and public comments to news and products. One of the big challenges is to derive information from these messy or unrefined data. We will focus on (a) acquiring and storing data (b) data wrangling: cleaning, transforming, merging and reshaping the data and (c) computer-aided exploratory analysis using robust methods. Students are expected to be interested in learning how to write own programs where off-the-shelf software is not available. Some basic understanding of programming languages is helpful, but not necessary to enter the course.

The course is concerned with data structures and the design and analysis of algorithms. We study several subjects from the book by Cormen et al: linear data structures such as stacks, queues, linked lists, tree-like data structures such as binary trees, binary search trees, balanced binary search trees, heaps, graph-like data structures, and hash tables. Further we study several sorting algorithms. some graph algorithms. string matching, and the programming paradigms divide-and-conquer, dynamic programming, and greedy algorithms. We consider the worst-case time complexity and in some cases the correctness of algorithms.

This project stretches of the whole semester and will provide students of both tracks with the opportunity to apply their gained knowledge to solve a complex problem in a real world project. The Project is founded on two pillars:

1. Experience and understand the creative process of developing an interaction environment as part of research into complex systems, with a particular focus on stakeholder research, user-research, data identification, context mapping, interaction design from agile development to a technologic prototype, and evaluation (validation).

2. Stimulating personal & professional leadership, via activities that improve team building and project management skills, and activities that contribute to one’s intellectual development, autonomy and employability. These activities are either organized by the students themselves, or are offered in the form of workshops. The aim, of this course is also to introduce students to a rigours application of academic skills, such as research question formulation, experiment design, and evaluation.

After this course, students should be able to: understand the purpose of various types of data visualization, ranging from infographics to visual analytics; apply design principles to design information dashboards; understand the applicability of various visualization techniques; use visualization tools to perform visual analysis. The course will be comprised of the following: advanced visualizations for numeric, categorical, temporal, and geographical data; advanced visualizations for tree and network structures; the role of perception and cognition in visualization; visual analytics models; multimedia analytics; telling stories with data.

Big data refers to data that are more voluminous, but often also more unstructured than traditional data. This in particular concerns data-collection that draws on Internet-based data sources such as social media, large digital archives, and public comments to news and products. One of the big challenges is to derive information from these messy data. The first step in this process is also called data wrangling, which is the main subject of this course.  Once the data is parsed and cleaned, it is usually analysed in an exploratory way before more advanced statistical or machine learning techniques are applied.

Students will investigate the interface between sensors, data, APIs, machine intelligence and societal interventions with practical application to people and real world problems. Research led activities on the course will be centred around applying theory in projects involving building and programming prototypes of remote sensing devices and physical data driven interventions. Students will be have to evaluate and reflect on impact on society of data and ubiquitous computing systems as distributed data driven services.

Entrepreneurship is fundamental to generate value-added from innovation and it is an increasingly important subject for students and professionals, also in the context of data science. The growing complexity of the data science sector and its accelerating dynamics urge professionals to think and act in an entrepreneurial way. Due to the informatization of society, data about nearly everything emerge every day. By using data, searching for inferences and patterns, may entrepreneurs help to better support their new ventures.

This course is especially useful for ambitious students who want to demonstrate their ability to analyze data in order to pursue a career as an entrepreneur or who want to work at an entrepreneurial firm. During the last hundred years, entrepreneurial innovation is the main generator of jobs and welfare in Modern Society, the “true source” of national competitive advantage. Many Universities, Research Institutes and Research Departments of large Enterprises have adopted policies to stimulate the relationship between entrepreneurship and innovation, in the hope of facilitating economic growth.

During the course, students will learn basic knowledge about how to successfully launch a new venture and its underlying business idea. Working in teams, students will be requested to collect and analyze data in order to identify and validate an innovative business idea – representing a starting point for a potential start-up – to present in a “business pitch” and a “short final report” at the end of the course. Students must support their proposal with data analysis.

The course objective is to obtain a good knowledge and understanding of relational database systems. This includes the ability to develop conceptual database models, as well as key concepts and skills in relational database model theory and practice.

Data and databases play a central role in any information system from transaction processing to enterprise systems and, of course, data science applications. The purpose of this course is to offer a solid understanding of the core concepts in this area as well as an opportunity to apply these concepts hands-on in structured exercises as well as in a much less-structured ‘living case’ setting. These core concepts are based on the relational data model and data modeling as well as SQL -the de facto standard database language – combined with data visualisation and the design of metrics and dashboards. The course includes a significant practical part.

This one-week Deep Learning course covers theoretical and practical aspects, state-of-the-art deep learning architectures and application examples.
The lectures will introduce to students the fundamental building blocks of deep learning methods and the weaknesses and strengths of the different architectures. Students will learn how to tailor a model for a particular application. During tutorials students practice the theory using exercises and have the opportunity to ask for additional explanation for those parts of the material perceived as more difficult. Computer lab sessions aim at making the material come alive and train students in how the methods learnt in class can actually be applied to data. The lab sessions are meant to work on the assignments, such that the students automatically keep up with the material.

The summer course welcomes Master’s and PhD students, alumni, professionals in economics and related fields, who are interested in deep learning. The level is introductory, targeted at participants who would like to familiarize themselves with the topic, and acquire a good basis from which to approach deep learning potential applications.

Topics covered

• Introduction to Deep Learning (High-level definitions of fundamental concepts and first examples)
• Deep Learning components (gradient descent models, loss functions, avoiding over-fitting, introducing asymmetry)
• Feed forward neural networks
• Convolutional neural networks
• Embeddings (pre-trained embeddings, examples of pre-trained models, e.g., Word2Vec)
• Recurrent neural networks
• Long-short term memory units
• Advanced architectures (Densely connected networks, Adaptive structural learning)

Deep learning is primarily a study of multi-layered neural networks, spanning over a great range of model architectures. More specifically, the following content will be the studied both from theoretical (during lectures) as well as from practical (during the practicals) point of view.

Linear regression, logistic regression, perceptrons. A recap of previous simple machine learning models

Back propagation and optimization. Since neural networks are notoriously difficult to train, we will deepen our understanding on how to optimize them both theoretically and practically.

Convolutional neural networks(CNN). The driving force behind the popularity of deep learning. CNNs have their main application in image recognition, object detection, automatic text translation and speech recognition.

Recurrent neural networks(RNN). CNNs or other traditional network architectures have only feedforward operations. With RNNs we add feedback to the model, thus providing it with memory. With RNNs one can build machines that write Shakespeare, automatic caption generators, automatic music generators or even machines that dream new pictures.

Transfer learning. Normally deep learning requires big data. For some problems big data is not available. With transfer learning one can combine different sources of data to build a more powerful model.

Restricted Boltzmann Machines, autoencoders. These are two examples of generative deep models that can train from unlabeled data. Other variants of deep learning models and architectures will also be presented.

• Gain a good understanding of the deep learning architectures that are used for a spectrum of NLP problems
• Gains practical knowledge in developing one or two models or model variations to a address a concrete NLP problem
• Learns how to evaluate and analyze a deep learning model and its performance for specific NLP problems and based on this analysis learns how to formulate scientific questions
• Learns how to present and motivate deep learning modeling choices for specific NLP problems

The Deep Programming Minor is open to Bachelor students in Computer Science, Information, Multimedia and Management, and Lifestyle Informatics. The Minor includes courses in Concurrency and Multithreading, Systems Programming, Information Retrieval, Operating Systems, and Equational Programming.

This course offers an overview of techniques to describe quantitative
data. Topics are, among others, mean, variance, correlation and
regression. Using the elaboration model students learn to interpret the
relation between two variables controlling for the effects of a third
variable. This course also offers an overview of statistical techniques
how to analyze collected
data in order to test hypotheses. Afterwards a student is able to
formulate hypotheses, to test them, to draw correct conclusions, to show
the relation between levels of significance, p-values, statistical
power, and statistical errors. During SPSS tutorials all techniques will
be applied.

The course focuses on the process that communication and data science professionals should go through to (1) identify communication & business challenges that could be answered by digital analytics, (2) gather and understand the data available, (3) prepare the data for analysis, (4) create models, and (5) evaluate the effectiveness of the models in addressing the challenges. Central to this course are digital analytics, including web analytics, search analytics, social media analytics and journey analytics. We discuss 1) designs and procedures for gathering analytics data, 2) validity and biases of analytics data, 2) how to analyze these data, 3) how organizations and brands can use the results and optimize communications with their stakeholders, 4) privacy, security and ethical aspects. The course is not overly technical, but rather aims at advancing the student’s knowledge and understanding of digital analytics as a way to enable her or him to be part of teams that use digital data in creative ways to solve communication challenges, and to work effectively with data science or computer science professionals.

The sources and objects studied in history, media, literature, art, and social sciences are increasingly becoming available in digital formats. The minor Digital Humanities and Social Analytics will train you in how to create and analyse different types of data collections, using tools for text mining, data analysis and visualization.

The courses include hands-on training, research internships in ongoing research projects, as well as theoretical reflection on the promises of ‘the digital’ for your own discipline. Practical computational training will sharpen your analytical skills and enhance your job opportunities in the future.

To organize this minor VU Amsterdam works closely together with the KNAW Humanities Cluster in Amsterdam, where students participate in cutting edge digital humanities projects.

The goal of the course is to get acquainted with digital humanities research, by collaborating in a current project through an intensive internship of one month. Students learn to put digital theory into practice, applying the knowledge gained from previous minor courses to a real-world project.

Do you feel a bit lost in the digital wonderland? Join the executive program to gain deep knowledge and a broad perspective on what digital transformation may mean for your organization.

Digital transformation is the radical organizational change that is due to the emergence of digital innovations such as artificial intelligence, robotics, digital platforms, innovation ecosystems, blockchain, virtual reality, and internet of things. We hear these buzzwords all around: but what do they really mean for your organization?

The program will help you navigate the digital landscape.

The executive program will help you recognize the relevant opportunities and threats to create value with digital innovation for your organization. Why? Because the program looks at the entire ecosystem: you will study the emergence, development, implementation, and prolonged usage of technologies in practice by engaging in in-depth studies at a wide variety of international organizations.

It is difficult to imagine a standalone modern computer system: every such system is one way or the other connected through a communication network with other computer systems. A collection of networked computer systems is generally referred to as a distributed (computer) system. As with any computer system, we expect a distributed system to simply work, and often even behave as if it were a single computer system. In other words, we would generally like to see all the issues related to the fact that data, processes, and control are actually distributed across a network hidden behind well-defined and properly implemented interfaces. Unfortunately, life is not that easy. As it turns out, distributed systems time and again exhibit emergent behavior that is difficult to understand by simply looking at individual components. In fact, many aspects of a distributed system cannot even be confined to a few components, as is easily seen by just considering security. In this course, we pay attention to the principles from which modern distributed systems are built. Unfortunately, these principles cannot be viewed independently from each other: each one is equally important for understanding why a distributed system behaves the way it does.

This course is concerned with reinforcement learning and its origin
dynamic programming. These are fields dealing with goal-directed
decision making over time, such as finding your way in an unknown area,
playing a game or pricing airline tickets.
We look at these areas from different angles:
– we deal with full-information “planning” problems, but also with
partial-information “learning” problems
– we consider different algorithms, some of which are guaranteed to find
the best solution, but also heuristics
– we consider high-dimensional problems (such as games) and methods to
solve them
– we look at small toy problems to understand algorithms and sharpen our
intuition, but also bigger problems for which we learn how to implement
algorithms (in python or R)
– we look at different types of applications, both from AI (search
problems, games) and OR

This course will give you an overview of the theory of discrete and continuous dynamical systems (first period), and a foundation in the most commonly applied numerical algorithms used to solve algebraic and dynamic problems (second period) found in concrete applications.

At the end of the course, the student is able to: analyse one and two-dimensional difference and differential equations systems; solve systems of non-linear ODEs numerically; linearise a non-linear system, compute corresponding eigenvalues (by hand and numerically), and draw conclusions on the stability of fixed points; use several numerical algorithms in concrete applications; make programs in Matlab.

The Master of Science programme in Econometrics is a multi-disciplinary Master’s programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages such as Eviews, R and Matlab, to explore and analyse problems in economics and finance.

The specialisation/track Econometrics emphasises statistical techniques for micro- and macro- econometrics analysis.

The Master of Science programme in Econometrics is a multi-disciplinary Master’s programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages such as Eviews, R and Matlab, to explore and analyse problems in economics and finance.

Econometrics emphasises statistical techniques for micro and macro econometric analysis, whereas Financial econometrics focusses on mathematical and statistical techniques and their application to financial models and time series. Mathematical economics emphasises mathematical modelling of economic and financial markets. Big Data Business Analytics deals with large and complex data from widely different sources for the use in economics and business. The specialisation depends upon the electives and Master’s courses chosen; a flexible mixture of these four specialisations is also possible.

After this course, the student should be able to:

– Translate economics and business questions into econometric models and hypotheses;

– Analyze discrete choice data, panel data, time series;

– Interpret the conclusions properly, and understand the role of assumptions;

– Use the statistical programming language R for econometric model building.

In this course the multiple regression model is developed with applications, in particular, to cross sectional data. The treatment makes extensive use of matrix algebra and multivariate statistical theory. Discussed are: the classic linear regression model, standard assumptions, properties of the LS estimators, fit, consequences of omitted or redundant variables, partial regression, multicollinearity, linear restrictions, prediction, asymptotic properties and variable transformations, dummy variables, test for parameter stability, test for normality, heteroscedasticity, serial correlation, endogeneity of explanatory variables and instrumental variables. Applications and simulations are carried out with the software packages EViews and R or Python.

The course explains a number of fundamental concepts that are important for the interpretation of quantitative results: including measurement errors, simultaneous equation bias, self-selection, censoring, truncation, etc. These ideas are fairly easy to explain with econometric models. Using these models, tests can be derived and solutions can be found for the problems involved.

Modeling itself is also an important skill and the course provides some initial techniques and extensions for correct modeling of economic variables. We will consider:

• non-linear methods including NLS and Maximum Likelihood;
• models with endogenous regressors (simultaneous equations, IV, GMM);
• models for discrete choices and other limited dependent variables (including Logit, Probit, Tobit);
• estimation and testing of the above models in practice using the computer;
• correct application of techniques and methods suitable for econometric research and the correct reporting thereof.

The Bachelor Econometrics and Data Science (EDS) specialization at the VU Campus in Amsterdam will offer you an inspiring and challenging study that is ideal for today’s digital world. EDS is internationally-oriented, fully taught in English, and will provide you with the necessary theoretical knowledge to succeed as an econometrician and data scientist for:

– Companies providing services on the internet, such as Google, Amazon and Booking.com

– Banks, financial institutions and consultancies, such as ING, ABN Amro and Deloitte

– Economic decision centers, Research institutes, Central Banks and Governments

After having completed the Econometrics and Data Science Bachelor, you can become a specialist in analyzing complex economic and financial data. With your unique knowledge you can help shape the operations and marketing strategies of companies, the performances of banks, or the decisions of governments.

This specialization will help you to develop an integrated perspective to Econometrics and Data Science. You will learn to think analytically and gain a balanced overview of diverse and interconnected issues on collecting, analyzing, modeling and forecasting data. You will also learn how to visualize data and how to present your findings, results and conclusions. The Econometrics and Data Science study is a technical and hands-on study that will prepare you for any future!

The bachelor programme Econometrics and Operations Research combines various disciplines such as mathematics, statistics, informatics and economics. The programme is both broad and specific. Because of your knowledge of econometrics and practical skills, you will be able to provide solutions for issues in the field of economics. Your models for example will be able to determine the effects on employment of an ECB-initiated interest rate decrease, show the risk scenarios of a specific investment strategy, or help create the optimal planning for the deployment of trains. Econometrics and Operations Research at the VU is a small-scale education programme. You can always count on the personal help of senior students and teachers, and high-quality education. This course is in Dutch.

The Master’s programme in Econometrics and Operations Research is an academic programme focusing on the development and application of quantitative methods for analysing economic issues in a broad sense. The components of the Master’s programme correspond closely with the department’s research interests, which means that many of the latest scientific developments in areas like financial econometrics, logistics and game theory find their way directly into the teaching programme.

This is a course for the Duisenberg Honours Programme in
Quantitative Risk Management. It is accessible for outside students, if
they have sufficient background in probability, statistics, linear
algebra, econometrics and programming.

The course starts out with the theory behind common estimation methods
for linear, non-linear, or even non-parametric models. This knowledge is
applied to the study of factor, principal component and panel data
models. The second part of the course continues with a focus on time
series models with time varying parameters, both in a univariate as in a
multivariate setting.
Students are required to implement some of the methods in case
assignments using computer coding. We use Python as our standard
programming language, but students are free to choose some other
language if they prefer.

This is a course for the Duisenberg Honours Programme in Quantitative
Risk Management.
Following part 1 of this course, we proceed with studying econometric
methods for time-series data. We start with univariate and multivariate
linear time series models. We study their estimation and inference
procedures. Next, we consider non-linear time series models, In
particular, we consider models for volatility and financial risk. We use
this as a stepping stone towards general non-linear time series models
with time-varying parameters. We close with an introduction to state
space models and non-stationary time series models and their
applications within finance.

Students are required to implement the methods studies in case
assignments using computer coding. In this way, they develop hands-on
expertise with the methods and become familiar with both their potential
and their limitations.

We use Python as our standard programming language, but students are
free to choose some other language if they prefer.

This selective master course places students in contact with some of the
latest research in econometrics and data science. Instead of reading
textbooks prepared for the classroom, the students will read recent
research articles, published in top international scientific journals.
The students will also be trained in writing and presenting econometric
research.

The Master of Science programme in Econometrics is a multi-disciplinary Master’s programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages – such as Eviews, R and Matlab – to explore and analyse problems in economics and finance.

Four specialisations are offered. Econometrics emphasises statistical techniques for micro- and macro- econometric analysis, whereas Financial econometrics focusses on mathematical and statistical techniques and their application to financial models and time series. Complexity and Economic Behaviour emphasises mathematical modelling of economic and financial markets. Data Science and Business Analytics deals with large and complex data from widely different sources for the use in economics and business. The specialisation depends upon the electives and Master’s courses chosen; a flexible mixture of these four specialisations is also possible.

The main goal of this course is to make students familiar with using microeconometric techniques to empirically analyze economic models. Students should be capable to test economic theories empirically and to estimate policy relevant parameters. Next they learn how to interpret estimation results and to translate these into policy conclusions. Students learn to distinguish between causality and correlation.

The objective of the course is to show how econometrics can be applied to empirical questions in finance. In particular the course will cover topics such financial data and its properties, testing pricing efficiency and factor models, modelling volatility, risk management, continuous time finance. A mixture of academic papers and practical applications is used to study how econometric methodology is employed to facilitate financial decision making and extract information from financial market data.

This course aims to familiarize students with applied empirical
transport research and how to interpret recent applied work to evaluate
important transport policies. The course consists of lectures,
interesting home assignments and tutorials where assignments will be
discussed. In the lectures, we explain recent developments in empirical
strategies in transport research that are theoretically founded and
which help you to examine transport policies from a welfare perspective.

After this course, the student should be able to:

– To understand the core concepts and models of entrepreneurship in both new ventures and large existing companies (intrapreneurship);

– To analyse and understand key challenges of innovation and launching new digital products and services including innovations organize to execute issues within larger organizations;

– To analyse how companies execute techniques from the start-up and venture world;

– To collaborate in a team and create and present a new offering that solves a real business need in a complex organisation, including a business model;

– Via a case study of GE industrial internet, learn how the largest industrial company in the world is turning themselves into becoming the Digital Industrial Company;

– About the ’transition gap’ – the phase between ’lean start-up’ and ’crossing the chasm’, a critical phase which prevents some start-ups from growing to their full potential.

In the practical work we use the functional programming language Haskell. We practice with the basics such as lists, recursion, data-types, a bit of monads. The theoretical part is concerned with the foundations of functional programming in the form of lambda-calculus and equational reasoning. We study in untyped lambda-calculus beta-reduction, reduction strategies, encoding of data-types, fixed point combinators and recursive functions. In addition we study the lambda-calculus with simple types, its typing system and a type inference algorithm. In equational reasoning we work towards the results that all initial models are equal up to isomorphism, and that the term model is an initial model.

The process of data analysis consists of three phases: (i) data collection, (ii) querying the data and (iii) the consequences you draw from this analysis. Ethical and legal aspects play a role in all of these phases, and these will be discussed in this module.

Please note that this course cannot be followed separately.

The performance of a corporation depends on how well managers succeed increating shareholder value. We show you how to use tools that are offered by financial theory and help you just doing that: creating value. In this course we discuss three main issues in finance: capital budgeting, asset pricing and financial investments. The capital budgeting decision involves how firms select projects that create value. The theoretically optimal decision rule—th e net present value method—is discussed, also in relation to other selection criteria that are applied in practice. The asset pricing part concerns the way financial assets are priced by the market. The focus is on the pricing of shares issued by firms and bonds issued by firms and governments. Questions raised are: How are the term structure of interest rates and promised coupon payments related to bond prices? What is the influence of the expected stream of dividends and the level of market risk of firm’s projects on the price of shares? The financial investment decision is approached from a portfolio perspective and ends with a discussion of the Capital Asset Pricing Model (CAPM).

In this course, we aim to explain why accounting is seen as the ‘language of business’. Even though healthcare organisations often are not aiming to maximize their profit, like any organisation they need to explain their activities in financial terms. The field of accounting provides you with the tools and techniques to do so. Although it does not come natural to everyone, understanding the logic behind a unit cost, a balance sheet, or a profit number, will make your life as a manager easier.

The course deals with accounting techniques. These are not specific to healthcare situations. We will frequently use examples from a healthcare setting, but there is no such thing as a costing or budgeting technique for healthcare which uses a different logic or is based on other principles than those used for determining financial results of a company or the unit cost of a product. Having said that, upon completion participants should be able to understand and explain why it is difficult to evaluate the performance of hospitals from a financial perspective, or why it is difficult to improve financing mechanisms such as DBCs, DOTs or ZZPs.

After completing this course, participants are able to do the following:

• explain what assets, liabilities and equity are;
• prepare simple financial statements and interpret complex financial statements;
• use accounting information in decision-making;
• understand and apply cost allocation mechanisms;
• describe budgeting processes and explain the uses of budgets;
• perform job costing and cost allocations;
• explain what performance management is.

The Master of Science programme in Econometrics is a multi-disciplinary Master’s programme providing a balanced and rigorous training in quantitative analysis of problems in economics and finance. The programme consists of advanced courses in mathematical economics as well as econometrics. Mathematical economics deals with mathematical modelling of market phenomena such as price adjustment processes. Econometrics deals with statistical modelling, estimation and testing whether economic models match observed patterns in real economic and financial time series. At the end of the Master’s programme, students are able to apply advanced mathematical and statistical methods, supported by modern software packages such as Eviews, R and Matlab, to explore and analyse problems in economics and finance.

The Specialisation/Track Financial econometrics focuses on mathematical and statistical techniques and their application to financial models and time series. For this specialisation, the Bachelor’s course Mathematical and Empirical Finance must be a part of the Bachelor’s study programme or deficiency programme.

This course will enable you to become acquainted with econometric techniques that have been developed for the analysis of financial markets. Furthermore, to be able to apply these techniques on empirical data and to interpret the results of such empirical analyses from a financial perspective.

Unique two-day masterclass introduces participants to digital currencies, emerging mobile payment systems and blockchains, by the top expert of the world professor David Yermack from NYU Stern Business School.

After this course, the student should be able to:

– Acquire an overview of digital currencies, blockchains, and distributed ledger technology;

– Learn about potential applications of distributed ledger technology to new products and services;

– Explore blockchain technology and its potential to provide faster, cheaper, and more secure financial transactions;

– Understand the opportunities and risks from smart contracts and other emerging technologies.

This track aims at Computer Science students with a general interest in Computing and Concurrency and the application of formal methods for system design. Computing is a fundamental phenomenon in computer science and we provide courses addressing this field in a wide range: from distributed algorithms to protocol validation, and from term rewriting to logical verification. In order to enhance background knowledge and to support the further study of foundational questions some general courses in logic and mathematics are provided as well. Concurrency naturally occurs in the specification of distributed systems, and their analysis, verification and implementation require a systematic approach, aided by formal methods.

This courses focuses on the design and analysis of (multi) agent systems from a logical perspective. This course will be based on Michael Wooldridge’s book: An Introduction to Multi-Agent Systems (Wiley). The main topics include: goals and intentions, perception-action cycle, action planning, reasoning and search in agents, architectures for agent systems, collaborative agents, communication, and distributed problem solving. Focus will be on the logical approach to agent systems and on multi-agent settings. The main concepts will be applied to implementations. The course includes a practical part in which students implement agent systems and perform experiments.

Interested in Bioinformatics? Or you want to find out how biology can make an exciting application domain? Or you want to learn how what more you could do with your data, and with less effort? Enter here to start! Fundamentals of Bioinformatics (FoB) is the starting course of the Bioinformatics master. It aims to give a broad overview of important
topics relevant to the field, with a focus on current open problems. Students will be made aware of these open problems during practical sessions that aim to let the student ‘stumble upon’ these problems by themselves. Based on their background, students will be assigned to
separate classes where they will be working to fill gaps in their background knowledge in programming and/or biology.

Data science is a dynamic and fast growing interdisciplinary research field. Industry, governments and academia operates on large amounts of data. But how do we deal with such large amounts of data? Is there a general framework to gather, analyze, model and visualize the data? What techniques do we use? What are the legal and ethical aspects regarding these data sets? This course will introduce methods for a number of key aspects of data science: data gathering, data analysis, data visualization and ethical and privacy issues. During the course, you work in a small team of students on a series of three projects that bind together all elements of the data science process; from formulating a research question, gathering data, exploring the data, modeling the data and communicating and visualizing the results. The data sets are from external companies and organizations within the following domains: (1) politics, (2) marketing and branding, (3) health. We will be using Python for all programming assignments and projects.

Data analytics is fast growing in medicine and health care processes. In this module, we lay out the fundamentals and practice of this kind of research: statistics (parametric methods), machine learning (non-parametric methods) and design methods.

In this module some traditional parametric and newer non-parametric analytical techniques will be studied, discussed, and applied. We aim that students are capable of performing analytical research with complex data in an autonomous manner covering the phases of: data collection, cleansing, analysis, visualization, interpretation of results, etc. In particular, this module equips the student with conceptual and practical tools to understand essential issues in medical data science. These issues concern, among others, the systematic and structured approach to perform data research; representation of the data; the choice of an appropriate analysis technique; the assumptions made by the chosen approach; and the effective visualization of the results.

Learn the basics of empirical experimentation in the field of Software Engineering. Be able to operate in a lab environment and build a successful
 experiment for software energy consumption.
 Become familiar with the research problems in the field of green software engineering.
 Understand and measure the impact of software over energy consumption.

Students will work in teams to perform experiments on software energy 
consumption in a controlled environment. They will have to carry out all
 the phases of empirical experimentation, from experiment design to
 operation, data analysis and reporting. They will be provided with
 examples of previous experiments, but they will have to choose by 
themselves the experimental subjects and hypotheses to test. During the 
lab sessions, students will be assisted for technical operation of the
 lab equipment as regards measurement and data gathering. Students will
 also receive the required training for data analysis and visualization
 (i.e. graphs, dashboards) using specialized software.

It is hard to think about or society without also thinking about information and communication technologies (ICT). As a result the ways in which people and companies communicate with each other is rapidly changing. If you want to work in ICT, HBO-ICT is the bachelor programme for you. In four years you will become an ICT professional that is able to make a difference. For example as a mobile app developer, game developer, IT security specialist, telerobotics researcher, IT business analyst, or as an entrepreneur in the ICT sector. This course is in Dutch.

Objectives

• Identify the appropriate HPC methods and tools to scale up scientific applications
• learn to use Big Data Methods, techniques, and tools to solve data intensive applications
• Develop skills to use HPC, and Cloud facilities

Contents

The course covers a number of key topics in the field of high performance computing and big data engineering. The course is organized as a lectures and workshops which help the students to develop both theoretical and practical skills. Following is the list of topics covers in the course:

• Introduction to parallel programming models and Big Data
• Grid/Cloud Computing
• General-purpose graphics programing unit for Big Data Application
• Big data processing: Apache Spark and storm
• Relation BD and NoSQL, NewSQL
• Data Intensive computing with Hadoop: MapReduce and Pig,
• Local/Remote Visualisation for Data intensive application
• HPC Cloud.

Students will develop skills in High Performance Computing which are commonly used to solve Data intensive applications and avoid common pitfalls which often lead to misuse of valuable computing resources. In this course students will learn about: approaches used in HPC and distributed computing; methods and techniques to solve Big Data problems; and to develop skills to use HPC facilities and e-infrastructure.

Researchers and engineers from industry and academia alike frequently experience their daily work to be impeded by physical limitations of their ICT equipment: processing and storage capacity, visualization facilities and their integration. They feel that up-scaling to high performance computing (HPC) facilities would be very beneficial to their work, but don’t know how to do this and lack the time to investigate their options.

The objective of this course is to introduce individuals with limited programming knowledge to various HPC facilities. At the end of the course, they will be able to use them avoiding common pitfalls, thus saving them money and time. The course is composed of a number of independent modules touching on various HPC and Big Data issues: Introduction to Unix, distributed systems, and Big Data; Using state-of-the-art Super Computers (with hands-on on the National Super Computer Cartesius and the Lisa cluster); HPC Cloud; GPU programming; Local and Remote Visualization Techniques; Data management; Data Intensive Computing with Hadoop: MapReduce and Pig; MPI/OpenMP approaches used in HPC and distributed computing.

In the developed world computers are ubiquitous, and ICT has rapidly grown into a critical asset for economic, technological, scientific and societal progress. The main objectives of this course are:
To make the next generation of Computer Scientists aware of:
a) The importance of ICTs for the developing world and the unexpected way developing countries are leapfrogging into the information age
b) The opportunities and challenges that exist for an information scientist in the area of ‘development4development’
c) The influence of context in a typical ICT4D project
d) The complexity of deploying an ICT project within a development context, and how to tackle this.

To equip the students with some initial project management, technological and programming skills specific to an ICT deployment in a developing country. Positioned at the heart of the VU’s vision of social relevance as one of the guiding principles, the core aim of the course is to raise the awareness that we as Computer Scientists can make a significant difference by sharing our expertise according to well established principles of international development.

No organization can do without information systems. For some organizations, such systems are even of strategic relevance, as they offer a clear competitive advantage. Think, for example, of how Amazon has become such a dominant retailer or how an organization as Uber has conquered the taxi market. This course explains the relevance and use of information systems in modern organizations. We will briefly sketch how the role of information systems has developed over the years to reach its current ubiquitous level. Special attention is devoted to the rise of the internet and its impact on traditional organizations, as well as the emergence of new types of (cloud-based) organizations. Reasoning from the organizational importance of information systems, we will look into the way information systems are developed such that organizations can achieve their objectives. We will pay considerable attention to an important phase in information system development, namely how we analyze and model business processes. For this purpose, we will rely on the use of classical Petri nets. This course will approach the topic of information management in breadth and in depth. Breadth is achieved by giving an overview of all relevant topics in the area of information management; depth is attained by introducing students to a powerful, formal modeling technique that they will learn to master in the context of organizational analysis.

In this course you will learn how search engines and other information retrieval systems work, to understand the principles and methods, and to acquire some basic skills in programming important aspects of such systems.

The underlying question behind this course is: How do search engines work? To answer this question we dive into the details of information retrieval, the field that deals with search. During the course we discuss the various parts of search engines:

– Retrieval models: how do we retrieve relevant documents for a given query? And how do we rank these documents in the right order?

– Evaluation: given a working retrieval system, how do we determine its performance and how can we compare it to other systems?

Besides these two basics of information retrieval we explore other frequently used techniques, theories, and models (e.g., relevance feedback, learning to rank, and semantic search).

Building on the Information Retrieval course (MSc AI, term 3), this course will focus on the design, execution, and analysis of state-of-the-art retrieval methods. Over the years, IR ranking functions have come to include features based on content-based analysis, based on the analysis structure (page, site, link), and based on user behavior. Now that IR systems need to consider ever more heterogenous information sources (e.g., on the web, social media, etc.) to address more diverse user needs and expectations, new ways of integrating dozens or even hundreds of ranking criteria are developed. In this course we discuss new learning to rank models and approaches, and particularly focus on online models and their evaluation.

Topics include learning to rank models, click models, experimental design, social media search, recommendation algorithms.

This course aims to provide students with an understanding of how internal control has to be designed to realize reliable management information from a risk management perspective. Additionally the application of ICT technology within organizations is of great influence on the internal control of organizations. The formal organizational structure, segregation of duties and procedures can lose their importance if not completely redundant. The use of IT has consequences for the effectiveness and efficiency of the internal control and risk management systems of the organizations concerned. ICT requires continuous coordination between business objectives, governance, risks and compliance.

Information Science is about the development and potential applications of modern information technology, as well as its effects on people, society and business. Studying Information Science, you will learn how people process information, communicate, use technical resources, and how new media such as the Internet or mobile technology can support this. Besides learning how people process information, communicate and use technical resources, you will learn from a management studies perspective about how important information is to businesses, how information products are marketed, and how ICT investments of millions of euros are managed. An understanding of technology is necessary for understanding what is possible, and what is not.

Information Sciences is the multidisciplinary area bridging Information and Communication Technology (ICT) and its practical use in society. Are you interested in how information is created and processed in companies and institutions? Are you more interested in the application of technology than technology for its own sake? Do you believe it’s important not to lose sight of the role people, organisations and cultures play in designing, modelling, communicating and sharing information? Are you fascinated by knowledge and innovation? If so, then the Master’s programme in Information Sciences at VU Amsterdam is an excellent choice for you.

Information studies is a broad and interdisciplinary field, primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval and dissemination of information. It examines the interaction between people, organisations and any existing information systems, with the aim of creating, replacing, improving or understanding information systems. Information studies tackles systemic problems first rather than individual pieces of technology within that system: it focuses on understanding information problems from the perspective of the stakeholders involved, and then applying technologies as needed. Not only aspects of computer science are incorporated, but also aspects of research fields like cognitive science, commerce, communications, management, philosophy, public policy, and the social sciences. The Master’s programme in Information Studies at the UvA offers specialisations in: Data Science; Game Studies; Information Systems

Information Systems is the Master’s programme for you if:

– you are interested in the ways people interact with (new) technology and media, and how they are supported, hampered and influenced by them

– you want to analyse systems for the supply, storage and communication of information by means of various media (such as video, speech and text)

– you want to make connections between corporate/organisational management and the people responsible for developing technological solutions

– you want to translate user demands into innovative solutions

In this course, we quickly review the basics of probability theory and introduce concepts such as (conditional) Shannon entropy, mutual information and entropy diagrams. Then, we prove Shannon’s theorems about data compression and channel coding. An interesting connection with graph theory is made in the setting of zero-error information theory. We also cover some aspects of information-theoretic security such as perfectly secure encryption.

Gaining insight into large collections of data requires an intricate interplay between data analysis, data mining, domain knowledge, visualization, and interacting users. In this course we will study the development of methodologies which support the process of gaining insight in large and complex datasets by a combination of data analysis, machine learning, and information visualization. Methods are geared towards designing and realizing information visualizations which, in an optimal way, support the insight gaining process.

This course gives an overview over the theory and practice of Intelligent Systems, systems that perceive, reason, learn, and act intelligently. Students will acquire practical skills in developing intelligent systems building on a thorough understanding of well-understood Artificial 
Intelligence approaches, including Knowledge Representation and Machine Learning.

Our international study trip is your chance to intensify your outlook on international business. During this exciting 1-week trip you take lectures at a top business school, visit local companies and networking events. During the study trip you will meet industry experts, executives, entrepreneurs and consumers in a classroom setting, but more importantly in real-life corporate settings.

In the last few years the destination of the International Study Trip was Silicon Valley, California, U.S., home of companies that have built their success on big data, like Google, eBay, Facebook and Apple. The programme offered insight into the nature of business in this leading hub for high-tech innovation and development. Students got the unique opportunity to personally experience the transformative power of big data and learn from real life cases.

Internet and the World Wide Web play a central role in our society, and have changed the way software systems are engineered and provisioned. Recent advances in virtualization techniques as well as the emergence of Software-as-a Service (SaaS) and cloud-based paradigms have enabled new ways of providing and exploiting computing and IT resources over the Internet. This track aims specifically at preparing students to work in such a complex, dynamic and distributed environment. It gives both in-depth understanding of the key components in developing distributed software- and service-based systems over the Internet, and provide the students with technical and critical thinking skills for the design and performance evaluation of such systems.

Please refer to the System and Network Engineering web pages for detailed and current course information.

The Minor Applied Econometrics provides a thorough introduction to econometric methods and techniques with an emphasis on how to implement and carry out the methods in empirical studies and how to interpret the results. The key steps of model formulation, parameter estimation, diagnostic checking, hypothesis testing, model selection and empirical analysis are given extensive attention throughout the different courses.

Apart from the fundamentals of econometrics, much emphasis is given to how econometric methods are carried out in different empirical settings and studies. Particular attention will be given to issues related to “big data” in the context of different disciplines in economics and business. The students are given some flexibility to opt for a specialization/track in economics, finance or marketing; one may label such specializations as “Minor in Applied Econometrics”, “Minor in Financial Econometrics”, “Minor in Quantitative Marketing”, etc. It will allow the student to focus on a subject of their own liking.

This course consists of two Orientation segments and a Skills segment.

Orientation segment Econometrics:

• the student will get acquainted with the basics of econometrics and after successful completion of the course will be able to explain basic concepts of econometrics such as linear regression, testing of parameters, unbiasedness, consistency, and efficiency;
• after successful completion of the course, the student will be able to carry out both explorative analyses of economic data using simple statistical models and analyses of economic relations independently.

The student will also learn how to implement calculations and estimations during computer classes.

Orientation Actuarial Science

This segment is designed to offer students a notion about general topics in the fields of Actuarial Science, Mathematical Finance and Quantitative Risk Management. More detailed learning objectives for this segment are:

• basic knowledge of Life Insurance Mathematics and Financial Mathematics, such as the equivalence principle for cash-flows, calculations of premiums and technical provisions for simple life-insurances, and option pricing by replication and change of measure;
• Risk Theory and Risk Management, such as an analysis of the implications of the Law of Large Numbers, and analysis of premium and risk capital calculations for catastrophe and motor insurance.

The student will also learn how to implement calculations in the aforementioned fields using MS Excel and R during the computer lab sessions.

Skills segment:

The students will be able to:

• design and conduct an empirical research project;
• analyse and judge the results of this research;
• write a report about their research.

In this course students get an understanding of the contents and the objectives of the Business Analytics curriculum. There are lectures on relevant aspects of business administration, and through 2 cases students learn to see the connections between the different scientific fields. Also computer and communication skills are part of the course.

Bij zien komt heel wat kijken! Visuele taken die de mens letterlijk zonder nadenken uitvoert (omdat ze in onze hardware zitten) kunnen we computationeel duiden en expliciet implementeren.

In deze cursus converteren we digitale beelden gebaseerd op pixels naar ‘visuele kenmerken’ (features) voor latere verdere verwerking in tracking, classificatie, of 3D-reconstructie. Voor die overgang gebruiken we wiskundige modellen van de lokale structuur en kleur van beelden, het menselijke visuele systeem, het afbeeldingsproces van de camera, en computationeel efficiënte algoritmen. Die modellen zijn de basis van computer vision, het zien met de computer! We slaan een brug tussen deze basis modellen en meer geavanceerde technieken zoals het categoriseren van plaatjes met convolutional-neural-nets.

Computer vision maakt gebruik van lineaire algebra en Taylor-reeksen van multi-dimensionale functies (de plaatjes), en heeft als doel deze te implementeren op de computer. In dit vak ga je dus het vak Lineaire algebra en het vak Continue wiskunde en Statistiek (met name de calculus) gebruiken en je moet kunnen Programmeren.

• Low level vision (interpolation, warping, local operators, convolutions),
• Local structure in images, Scale-Space, Feature Detection (SIFT),
• Pinhole camera, Camera calibration, Stereo Vision
• Motion, Optic flow, Tracking (als het blokschema dit toelaat)
• Convolutional Neural Networks voor computer visie

This course is avaliable only in Dutch

This course in the minor Applied Econometrics is targeted at non-econometrics students. By the end of this course students will have had an introduction to modern econometric techniques, that will enable them to conduct methodological or empirical analyses of their own. In particular, students will be familiar with both econometric theory and with real-world applications in macroeconomics, finance and business.

Econometrics:
The goal of econometrics is to describe the relations between
observations as a useful model, from which predictions and inference can
be made. This introduction presents some first filters which can be used
for a
predictions, and continues with the principles of regression, applied to
real data.

Operations Research:
Core business in Operations Research is optimization. Several problems
from network optimisation are covered, among which the shortest path
problem, the minimal spanning tree problem, and the maximum flow
problem. For these problems, the mathematical structure is studied
leading to the design of algorithms for solving them. A glimpse will be
offered on complexity theory by analyzing the computation time of these
algorithms theoretically.

Mathematical Economics:
Within economic science frequent use is made of mathematical models.
Many of these models try to explain the choices of economic agents in
their economic environment. In this introduction, we set the first steps
in modelling mathematically the decision processes in economics, looking
both at behaviour surrounding individual choice in strategic decision
situations, and at cooperative decision making.

The course will comprise a basic introduction to MATLAB, designed to be suitable for students with little or no prior programming knowledge. The program will be based on introductory lectures and hand-on assignments, with the help and supervision of expert programmers. During the first half of the course the students will be guided to program their first tool to acquire actual neuroscientific data. In the second half students come up with a neuroscientific research question themselves. They will use their new knowledge to create a tool to acquire data to answer this question. Additionally, there will be introductory lectures on so called tool-boxes commonly used for data acquisition and analysis in neuroscience research.

This course teaches the use of computers to solve problems with algorithms and structured programming. The course content includes: primitive types, declaration, expression, assignment statement, iterations, methods, I/O using PrintStream and Scanner, array, class, object, standard classes String and Maths, design of programs, matrix, using several self made objects in a program, recursion and using a graphical interface through a pre-programmed package.

During this course, students learn to write program in Python using
types (int, boolean, float, list and str), expressions, assignment
statements, if-statements, iterations (while- and for-statement), They
also learn standard functions, module math, as well as how to make
functions, perform I/O, make classes and use objects.

Introduction to Systems Biology is the starting course of the Bioinformatics and Systems Biology master (together with Fundamentals of Bioinformatics).

Goals:
– To make the student acquainted with the major approaches and methodology in systems biology (to be studied in more detail in the master).
– To develop a basic understanding of biological concepts that are relevant to current topics in systems biology.
– To gain hands-on experience in basic modelling as a means of solving systems biology problems.
– To repair gaps in background knowledge.

This course covers both theoretical and practical aspects of time series econometrics including the analysis of stationary and non-stationary stochastic processes in economics and finance. The students are introduced to autoregressive moving average (ARMA) models, autoregressive distributed lag (ADL) models, and error correction models (ECM).

Furthermore, the course provides both theoretical and practical insight into parameter estimation in time-series and the use of these models for forecasting, testing for Granger causality, and performing policy analysis using impulse response functions.

Finally, the students are introduced to the fundamental problem of spurious regression in time-series analysis. We find a solution to this problem by taking a journey into the theory and practice behind unit-root test, cointegration tests and error-correction representation theorems.

This course covers both theoretical and practical aspects of time series
econometrics including the analysis of stationary and non-stationary
stochastic processes in economics, business and finance.

The students are introduced to autoregressive moving average (ARMA)
models, autoregressive distributed lag (ADL) models, and error
correction models (ECM). Furthermore, the course provides both
theoretical and practical insight into parameter estimation in
time-series and the use of these models for forecasting, testing for
Granger causality, and performing policy analysis using impulse response
functions.

Finally, students become familiar with the fundamental problem of
spurious regression in time-series analysis. We find a solution to this
problem by taking a journey into the theory and practice behind
unit-root tests, cointegration tests and error-correction representation
theorems.

A review will be given of estimation and testing in the linear
cross-sectional regression model. We will discuss the classical
assumptions, and the consequences arising when these assumptions are not
fulfilled. Throughout the course, the focus will lie on developing an
intuition for state-of-the-art econometric concepts. A balance will be
struck between theoretical derivations and empirical applications. The
textbook used (see below) is particularly well-suited for this purpose,
as it is targeted at an audience of advanced undergraduate students in
economics and business studies. Extensive use will be made of the
statistical software R, both for in-class illustration and for hands-on
exercises.

In dit vak wordt ingegaan op de IT-infrastructuren die ten grondslag liggen aan onze moderne informatiesystemen. Aan de orde komen onder meer de eigenschappen van moderne datacenters, datacommunicatie en protocolfamilies als TCP/IP, dataopslagtechnieken, veel gebruikte platformen zoals Unix en Windows, cloud- en virtualisatietechnieken voor servers en werkplekken, database managementsystemen, middleware concepten als Service Oriented Architectures en messaging bussen en de integratie tussen de IT infrastructuur en applicaties. Beveiliging krijgt hierbij speciale aandacht.

The objective of the Semantic Web course is to make students acquainted with methods and technologies used for expressing knowledge and data on the Web. At the end of this course, students will have built an intelligent web application that queries and reasons over integrated knowledge from various sources obtained from the Web.

Event though content on the web is generally produced from structured data sources (databases), its representation is in a form that is meant for human consumption. Linked Data allows to scale the walls of this siloed information space, by reusing identifiers and vocabularies across these datasets, and presenting that information in a way that is appropriate for machine consumption. Google, Bing and Yahoo already use this type of linked, structured information to improve web search and information retrieval. But it also helps content providers, such as the BBC, to better augment their content with content from other sources (e.g. from Musicbrainz).

In this course we will introduce the technologies and representation formats (RDF, RDFS, OWL) for expressing semantics and linked data in a web-accessible format, use the SPARQL query language to query over this data, and build a Web application that uses the data for some intelligent task.

The goal of the course is to provide insights in the concepts of information organization, knowledge representation, ontologies, and knowledge processes in relation to various ICT-based media. This course treats the principles and theories that form the foundation of information organization and knowledge-intensive processes, and puts them in relation to various media applications. Knowledge processes are those processes that use knowledge (reasoning), document knowledge (representation), acquire knowledge or transfer knowledge (teaching). The relation between knowledge processes and media will be explored, and various types of applications will be discussed.

Knowledge Engineering is a discipline that involves integrating knowledge into a program for solving a complex problem, which requires human expertise. Typical tasks are classification, diagnosis, planning etc. In the course we use CommonKADS as the methodology for the process of modeling the organisation, the context and the knowledge intensive tasks. This methodology give clear guidelines and concrete templates for modeling the organisational aspects and the expertise model, which is the core model of knowledge based system. The notion of pattern-based knowledge modeling is a key issue in the knowledge modeling process. The goal of the final project is to perform the entire knowledge technology process for a knowledge intensive problem of your own choice, starting with context analysis, up to a (partial) implementation of the knowledge based system.

Since its early days the question of how to represent knowledge and how to reason with it, has played a central role in Artificial Intelligence. The aim of the course is to make students familiar with a number of important knowledge representation formalisms. For each formalism, the course will discuss (i) the representational form (ii) an inference mechanism, and (iii) an example application problem on which to apply both representation and inference. Students will be asked to perform computational experiments with each of the formalisms.

In this course, you will learn the theory of knowledge representation languages that are used to express information on the Web, their application to real-world problems and data, and the research methods behind them.

After completing this course, students should be able to:

• Understand some of the most prominent language technologies, in particular:
  • Text representation for machine learning (tf-idf, lsi, nmf, lda)
  • Neural methods for machine learning (word2vec, doc2vec, rnn)
  • Information retrieval
  • Questions-answering
  • Information extraction and knowledge graphs
• Understand the possibilities language technology offers
• Envision applications of language technology
• Apply the techniques in use-cases

This course confronts the students with some data management tasks, where the challenge is that the mere size of this data causes naive solutions, and/or solutions that work only on a single machine, to stop being practical. Solving such tasks requires the computer scientist to have insight in the main factors that underlie algorithm performance (data access patterns, hardware latency/bandwidth), as well as possess certain skills and experience in managing large-scale computing infrastructure. Apart from the data being of large volume, another problem invariably is that data comes in strange forms and formats, is polluted, and needs to be transformed and cleaned. The main part of the course is the second assignment: a large big data analysis project where each student teams tackles a different problem, and while doing so gains experience in multiple aspects of large-scale data engineering (critical thinking, data management technologies, visualization techniques, paper writing).

More information is found on http://event.cwi.nl/lsde – also check the “showcase” section where you can see past project results (visualizations and papers).

After completing this course, students should be able to:

• understand the (international) principles and values concerning privacy and (personal) data protection;
• understand the basics of the EU General Data Protection Regulation (GDPR) and related EU-legislation;
• be aware of the possible impact, threats and risks of big data for privacy;
• understand that not all data may be necessary to use and that technologies (such as anonymisation and pseudonymisation) of data can help avoid or minimise the impact, risks and threats for the data subjects and the parties processing the data;
• be aware of the available methods and standards to design privacy-friendly systems and services (Privacy by Design);
• understand the (international) principles and values concerning use of AI;
• understand the OECD and EC guidelines on using Artificial Intelligence;
• understand related concepts of transparancy, explainability and fariness of AI;
• understand how these concepts can be ensured in the context of organisational use of data and AI, applying a legal and ethical perspective to data and AI in real-life cases;
• form a view/opinion of an organisation, a customer and a government/regulator on the use of (big) data and AI.

After this course, the student should be able to: Understand principles of privacy and data protection; Identify the possible risks of Big Data for privacy; Perform a law/ethics compliance scan; Understand technologies to minimize privacy risks; Design privacy-friendly systems and services; Understand that proper communication and transparency is key.

Amongst emerging technologies robotics and artificial intelligence are
prominent both in terms of existing as well as expected use in society.
These technologies are special, because they come close to how we humans
function. At this moment both robots and artificial intelligence are
primarily used for specific tasks (playing games, surgery, self-driving
cars), but developments are moving fast. What exactly the future brings
is difficult to tell, but no one denies the potential and risks related
to robotics and artificial intelligence. Not surprisingly, in the legal
and policy arena an active discussion is going on related to legal and
ethical issues. These are the issues addressed in this course. The
legal angle includes both existing law and the need for new law. If new
law is needed, discussion will also be on how this new law should be
drafted. For instance, presently the European Parliament is analyzing if
maybe some time in the future we may need some sort of legal personality
for robots, and Harari is even fantasying about legal personhood for
algorithms. Ethics can apply to both the development and use of robots
and artificial intelligence. In this course ethics is primarily used to
either constrain the application of existing law or to guide the
drafting of new law.

Applications that are covered in this course include softbots, the
internet of (robot) things, ambient technology, autonomous intelligent
vehicles, and social robots (care and sex).

People are central to the success of organizations, but they are also the most difficult asset to manage. Therefore, managers require a strong understanding of how to lead and manage individuals and groups in order to effectively solve organizational problems and maximize long term performance. As a manager, you need to develop the skills and talents of employees, and motivate them to achieve strategic goals. You also need to effectively combine employees in teams, enhance collaboration and reduce conflicts. In addition, you help to build, strengthen, or change the organizational culture. You need to make things happen, and often under challenging and changing conditions or timeframes. In order to do this successfully, managers need to be able to diagnose and analyze problems, make effective evidence-based decisions, influence and motivate others, and manage their teams. This course aims to prepare you to make more effective managerial decisions and increase your impact as a manager.

This course is an introduction to the key elements of leading and managing people and focuses on frameworks, theories and tools that help to understand how to lead and manage people effectively. At the end of this course, students are expected to be able to:
Understand and explain important theories, frameworks, and research evidence related to leading and managing people effectively
Apply these theories, frameworks, and research evidence to people-related business problems
Diagnose and analyze problems, and make effective evidence-based decisions as a manager

This course concerns robots that can adjust and improve their behaviour
over time.

The course has a strong hands-on flavour. After two introductory
lectures students have to develop and implement the learning method of
their choice in simulation. In particular, adequate robot controllers
have to be learned autonomously for two tasks, maze navigation and food
collection. After testing and tuning the methods in simulation, the best
learned robot controller must be ported to a real Thymio and the real
world performance compared with that observed in simulation.

Vast amounts of data have been collected through genomics initiatives. They provide a golden opportunity to research the secrets of life, to understand more of its complexities, to improve quality of life and to conquer major diseases. Converting this huge volume of data into real understanding is the basic challenge of Bioinformatics research.

After successfully completing this course, the student
– has a working knowledge of the concepts of matrix algebra and finite-dimensional linear algebra, such as echelon form, LU-decomposition, linear independence and determinants;
– is familiar with the general theory of finite-dimensional vector spaces, in particular with the concepts of basis and dimension;
– is familiar with the concepts of eigenvalues and eigenvectors, diagonalization and singular value decomposition and can apply these concepts in basic applications in
discrete time dynamical systems;
– has working knowledge of the concepts of inner product spaces and matrices acting in inner product spaces, including orthogonal projections and diagonalization of symmetric matrices.

Logic is an interdisciplinary and international two-year Master’s programme at the University of Amsterdam (UvA) that focuses on the central role of logic as a mediator between the sciences and the humanities. It is an incredibly complex and fascinating programme, intended for students who want to relate traditional fundamental research in the formal sciences to a wide variety of applications, ranging from Information Sciences to Linguistics and Philosophy. It is a programme for highly motivated students from all over the world, who are able to work in a inspiring and demanding environment, both individually and in groups.

The course objective is to obtain a good knowledge and understanding of the most important logical systems: propositional logic, predicate logic and modal logic. The students learn to use these systems to model data, knowledge and actions. An important aspect of the course is the ability to reason using these logics and reason about these logics: what can and what can not be expressed with a logic system, and what are the differences between the systems with respect to expressive power or the existence of decision procedures.