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In the Econometrics and Data Science Bachelor’s we train you to use mathematical and statistical methods to help solve problems in society or in the business world. With your expertise, you can advise organisations on the effect of their strategies and business operations. Or help businesses use extensive data analysis to shape their strategies. You will examine case studies and learn to work with advanced software. After the 2nd year, you can choose between 2 specialisations: Econometrics or Data Science.

The programme

The first 2 academic years of the Bachelor's 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.

Dutch or English?

If you are a Dutch-speaking student, you can also opt for our Dutch track. Both programmes are identical in terms of level and content. If you’re looking for a smoother transition to a fully English-taught programme, the Dutch track could be the right choice for you.

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 will 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.

  • Programming and Numerical Analysis
    Period 3
    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).

  • 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.

  • Introduction Econometrics and Actuarial Science
    Period 5
    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.

  • 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.

  • 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.

Compulsory course
Elective
Specialisation
Hi, I'm Camiel! I'm a Bachelor’s student in Econometrics and Data Science from the Netherlands. Got questions about studying at the UvA? Get in touch. Chat with Camiel
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 will 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. Sustainability also plays a significant role in the insurance industry. Due to climate change, weather conditions are becoming more extreme, leading to damages in many areas. It has become a relevant issue to use data to estimate which damages can be insured and what the corresponding premiums should be. There may also be uninsurable risks that can motivate policyholders to operate more sustainably. Data specialists often work with personal data, where privacy issues almost inevitably come into play. Ethics also come into focus, for example, when ethnic background should not be considered in the research. The challenge here is to process the data in advance to avoid unwanted biases.

How are these themes integrated into the curriculum?

Throughout this 3-year bachelor's programme, you will directly apply the knowledge gained during your studies to current problems and real-life business cases, which often revolve around these themes.

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