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Bachelor
Econometrics and Data Science
Vergelijk

The programme

The first 2 academic years of the BSc Econometrics and Data Science are in common with the Bachelor's programme in Actuarial Science. After the 2nd year you specialise in either Econometrics or Data Science.

COURSES SEM 1 SEM 2 SEMESTER 1 SEMESTER 2 EC
  • Macroeconomics for AE
    Period 1
    6

    In this course you learn about important macroeconomic concepts that help analyse how the economy interacts with changes in government purchases, taxes, or money supply. With this knowledge, you’ll interpret events in macroeconomic history since WWII, especially through illustrations in lecture and tutorial groups.

  • Mathematics 1: Calculus
    Period 1
    6

    This course is an introduction to calculus at the academic level. You learn about basic topics from classical differential calculus and integration theory. The working classes will help you deepen theoretical insights through exercises and further applications.

  • Microeconomics for AE
    Period 2
    6

    In this course you learn to explain basic microeconomic concepts, how to model markets and behaviour, and perform a basic (mathematical) analysis of these. You also search academic sources to write a literature review on a microeconomic topic.

  • Probability Theory and Statistics 1
    Period 2
    6

    This course gives you a solid basis of probability theory and descriptive statistics, which provides you with an indispensable basis for many subsequent courses in the programme. In the lectures you will do theory, in the tutorials exercises with applications.

  • Introduction Econometrics and Actuarial Science
    Period 3
    6

    This course teaches you the basics of Econometrics and of general topics in the fields of Actuarial Science During computer lab sessions you learn how to implement calculations and will conduct a research project using R.

  • Finance for AE
    Period 4
    6

    This course is your introduction into modern finance. Central topics are the assessment and financing of investment projects. You also get acquainted with the fundamental relationship between risk and return by learning about modern portfolio theory and the capital asset pricing model (CAPM).

  • Mathematics 2: Linear Algebra
    Period 4
    6

    This course provides you with a solid basis of linear (matrix) algebra as indispensable knowledge for the remaining study in Econometrics and Actuarial science. You practice the theory through exercises and will also learn how to use computer software (R) to solve larger problems.

  • Probability Theory and Statistics 2
    Period 5
    6

    In this course we advance on the single variable distributions and focus on multivariate probabilistic models. You will learn the basics of hypothesis testing. Both approaches are at the core of econometric analysis. R will be used for coding.

  • Programming and Numerical Analysis
    Period 5
    6

    This course provides you with a solid basis of computer programming and numerical analysis, both indispensable skills in the fields of Econometrics and Actuarial Science. You develop so-called algorithmic thinking to design algorithms and translate these into computer language (R and Python).

  • Introduction Data Science: Data Preprocessing
    Period 6
    6

    This course covers the basics of how and when to perform data preprocessing. This essential step in any machine learning project is when you get your data ready for modelling with help of Python. Also, part of this course is the (preparation of) a presentation of a related scientific subject.

COURSES SEM 1 SEM 2 SEMESTER 1 SEMESTER 2 EC
  • Life Insurance Mathematics
    Period 1
    6

    In this course you learn about the models and calculations used by actuaries for valuing, pricing, and reserving in a life insurance and pension fund context.

  • Mathematics 3: Advanced Linear Algebra and Real Analysis
    Period 1
    6

    This course advances on Mathematics 2. You will learn about eigenvalues, orthogonalization, different matrix decompositions and applications in optimisation (quadratic forms). This theory will be valuable for data analysis later. You will use Python for calculations.

  • Mathematics 4: Multivariate Analysis
    Period 2
    6

    Multivariate analysis involves evaluating multiple variables to identify any possible association among them. In this course you learn about several advanced concepts in nonlinear analysis and how to apply them to solve small problems analytically, and large problems numerically (using Python).

  • Probability Theory and Statistics 3
    Period 2
    6

    In this advanced course in mathematical statistics you learn about several convergence notions for distributions and estimators. This is used to derive confidence intervals and statistical tests and their elementary properties. You learn how to derive generalized likelihood ratio tests. R is used for necessary coding.

  • Statistical Learning
    Period 3
    6

    The main idea in statistical learning theory is to build a model that can draw conclusions from data and make predictions. In this introductory-level course, you learn about its fundamental issues and challenges and will discuss popular statistical (machine) learning approaches.

  • Econometrics 1
    Period 4
    6

    In this course, you will learn how to set up proper models to quantify the relationship between (economic) variables using tools from linear algebra and mathematical statistics. You explore and learn how to apply the so-called multiple regression model.

  • Mathematical Economics 1
    Period 4
    6

    In this course, you will study determinants of small scale economic environments using a model-based approach. Using multivariate analysis, you will learn about both consumer behaviour (choice and risk attitude) and firm behaviour (types of competition). Special attention goes out to general equilibrium and game theory.

  • Econometrics 2
    Period 5
    6

    In this course, you learn about a number of fundamental concepts that are important for the interpretation of quantitative results. It also provides you with initial techniques and extensions for correct modelling of economic variables.

  • Empirical Project
    Period 6
    6

    During this course, you apply the knowledge you acquired during this bachelor in practice. We discuss scientific articles and the underlying theory, and critically evaluate assumptions and techniques. You work on a group research project, with individual presentation of the results.

  • Restricted-choice electives
    Period 5
    6
COURSES SEM 1 SEM 2 SEMESTER 1 SEMESTER 2 EC
  • Free-choice electives: Minor's programme/Studying abroad/Company Internship/Electives
    Period 1
    Period 2
    Period 3
    30

    In the 1st semester you can choose from several options: Minor programme, or Studying abroad, or Company Internship in combination with Electives, or Electives.

  • Specialisation Data Science: Text Retrieval and Mining
    Period 4
    6
  • Specialisation Data Science: Time Series Analysis
    Period 4
    6

    Time series analysis covers methods for analysing and forecasting data with temporal patterns. Topics in this course include time series models, seasonality, trend detection, and statistical techniques. We also explore practical applications in both finance and economics.

  • Specialisation Data Science: Reinforcement Learning
    Period 5
    6

    Reinforcement learning is an autonomous, self-teaching system that helps determine if an algorithm is producing a correct right answer or a reward indicating it was a good decision. In this introductory-level course we discuss different models (dynamical programming, SARSA and Q-learning models) and apply them using software (like Python).

  • Specialisation Econometrics: Mathematical Economics 2
    Period 4
    6

    You will familiarise yourself with advanced model-based concepts of industrial organisation. You will study for market structure and behaviour and the role of competition policy, using game theoretic concepts. Some keywords are: Cournot, Bertrand and Stackelberg competition, anticompetitive behaviour, mergers, tacit collusion, repeated games.

  • Specialisation Econometrics: Time Series Analysis
    Period 4
    6

    Time series analysis covers methods for analysing and forecasting data with temporal patterns. Topics in this course include time series models, seasonality, trend detection, and statistical techniques. We also explore practical applications in both finance and economics.

  • Specialisation Econometrics: Microeconometrics
    Period 5
    6
  • Bachelor's Thesis and Thesis Seminar
    Period 5
    Period 6
    12

    Is there a recent development or business idea that sparks your enthusiasm? While writing your thesis, you have the chance to explore it while simultaneously training your ability to independently conduct relevant and valuable research.

Compulsory course
Elective
Specialisation
  • Your study week

    Expanding your knowledge and at the same time developing your skills is key. That is why you will participate in a variety of teaching activities. Most of the courses are evaluated with one or more tests. This is usually a written examination, but it can also be an essay, a report, or a presentation.

    Lectures (8 hours)
    Lectures give an introductory overview into the course content. You will attend them together with your fellow students. You take notes and have the opportunity to ask questions.

    Also, you can expect guest lectures from experts working in a wide range of economic organisations and fields.

    Seminars (6 hours)
    During seminars you will discuss specific subjects from the lectures in smaller groups. Exercises and practice assignments will help you to become adept with the theory. There are two types of seminars, those with plenary sessions and the small scale groups where you will work individually.

    Practicals (2 hours)
    During practicals you learn how to work with various mathematical and statistical computer programmes. 

    Self-study (20 hours)
    During your study week, you spend time to study theory, go over lectures and seminars, and prepare for exams and presentations. 

  • Year 1: develop a solid foundation

    This year is all about your basic knowledge of mathematics, information science, probability theory, statistics and economics.

    • Familiarise yourself with the possible specialisations: Econometrics and Data Science (of the BSc in Econometrics and Data Science) and Actuarial Science (BSc in Actuarial Science, you take lectures together with students from this programme).
    • Examine case studies and learn to work with advanced mathematical and statistical software like R for programming.
  • Year 2: extend the foundation

    The 2nd year enhances your mathematical, statistical and research skills. You will start to apply these tools to econometrics and data science. You will take mandatory courses like Mathematical Economics, Econometrics 1&2 and Statistical Learning.

  • Year 3: extend your knowledge and specialise

    Specialisation

    In year 3 you will specialise and choose one of the 2 specialisations: 

    1. Econometrics
      If the government increases excise duties to raise the price of petrol, fewer people will use their cars. By modelling reality, econometricians attempt to prove such statements. These econometric models are used to forecast the economy and make recommendations on economic policy. There is a great demand for people with an understanding of economics who are capable of quantitative analysis and modelling. Econometrics trains people to do this.
    2. Data Science
      Nowadays firms collect enormous amounts of data, so here is a large demand for data scientists. This data contains valuable information to improve sales and profits. Contrary to econometrics, the focus is on doing predictions and not so much on understanding the underlying processes. Consequently, the emphasis is more on programming and finetuning tools than on the statistical background of the methods. Machine learning and AI are at the core of this track.

    First semester

    My Semester: customise your programme

    In the 3rd year you are offered the opportunity to design your own programme in the 1st semester. You can choose from a number of options:

    • Study a semester abroad; participate in the UvA Exchange programme.

    • Take a minor programme at the UvA or elsewhere. For the specialisations the chosen minor should be relevant and offer a valuable contribution to it.
    • Attend a special programme at the UvA: 3 compulsory courses plus an internship or 2 electives.
  • Thesis

    There is some coursework in semester 2 of year 3, but a large part will be devoted to conducting and reporting on your own research. Is there a particular recent development that sparks your enthusiasm or do you have a great idea of your own? Writing your thesis, you have the chance to explore it fully while simultaneously training your ability to independently conduct relevant research.

    Your thesis is the final requirement to be completed for your graduation. Under the supervision of our researchers, you will follow a clearly defined path that will lead to your graduation with a Bachelor's degree.

  • Watch the recording of the online information session
    Learn more about the Bachelor's programme in Econometrics and Data Science and in Actuarial Science. Our Programme directors explain what you can expect of these challenging Bachelor's programmes. Additionally our students share their experiences with this Bachelor’s, study association VSAE and student life in Amsterdam.
    Learn more about the Bachelor's programme in Econometrics and Data Science and in Actuarial Science. Our Programme directors explain what you can expect of these challenging Bachelor's programmes. Additionally our students share their experiences with this Bachelor’s, study association VSAE and student life in Amsterdam.
Additional options during your studies

Experience the study

Real-life case: battle against hunger and poverty

The availability of satellite imagery makes it possible to estimate crop yields on the basis of weather conditions and crop growth. Machine learning techniques are used to transform the imagery to useful data, that would be hard to get otherwise. In this way particularly vulnerable populations can be identified, and help by NGO’s like the WFP can be effectively targeted. In the 2nd year of your Bachelor’s you will learn how to identify relevant characteristics in various data resources, and how to use these to make reliable estimates and predictions.

Responsibility, sustainability and ethics integrated to the curriculum

In this Bachelor's programme, you’ll learn how to use mathematics, probability and statistics to quantify (financial) risks and solve problems in society or the business world. Social issues increasingly play a role in this. The study programme therefore regularly covers topics such as sustainability, ethics and social responsibility. For example, you will learn how to research how many people choose more sustainable ways of travelling if fuel excise duty is increased.

How are these themes integrated into the curriculum?

Through practical assignments, you will directly apply the knowledge you acquire during your studies to current topics in the media and real business cases. These topics are often related to ethics, corporate social responsibility and/or sustainability. This starts in the 1st year in Introduction Econometrics and Actuarial Science and Introduction Data Science. Furthermore, there is an explicit focus on ERS themes in courses such as Mathematical Economics 1 and 2, Econometrics 1 and 2, and Empirical Project.

Throughout this 3-year programme, themes related to sustainability, ethics and corporate social responsibility will remain important topics. 

Liselotte Siteur, student Econometrics and Data Science
Copyright: UvA / Economie en Bedrijfskunde
Data analysis, programming and statistics suit me down to the ground. It's like doing extremely advanced puzzles. You can get stuck sometimes, but once I find that solution, I'm over the moon. Liselotte Siteur, student Econometrics and Data Science Read about Liselotte's experiences with this Bachelor's
Frequently asked questions