Stockholm university
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Master’s Program in Data Science, Statistics and Decision Analysis

The programme focuses on how to draw smart conclusions from large amounts of data with the purpose of making well-informed decisions.

In today's society, massive amounts of data are generated at high speed. The data is also characterised by high variety and is becoming increasingly complex. We are constantly connected with computers and smart phones, while being surrounded by cameras and sensors that monitor and measure our lives constantly.

Automation and digitisation are becoming increasingly important in a large number of areas and industries, and most companies and authorities store large amounts of data about their customers, users and processes. For example, the analysis of large amounts of medical data is becoming increasingly important in healthcare.

The programme consists of courses in the following three subfields: data science, statistics and decision analysis. The program is a collaboration with the Department of Statistics.


Important about selection
The selection is made from the following three criteria:

  • Grades of academic courses,
  • mandatory motivation letter and
  • the relevance of previous studies in relation to the programme.

It is therefore very important to submit a motivation letter.
Find instructions for the motivation letter under “How to apply” below.

  • Programme overview

    Courses of 30 credits are given within each subfield. Courses from the three subfields alternate so that at least one course from each subfield is given each semester, for the first three semesters. The program ends with a master thesis in the fourth semester.

    Areas within data science include:

    • basic methods and algorithms in data analysis and data mining,
    • advanced methods and algorithms in machine learning and deep learning,
    • reinforcement learning and optimization,
    • ethical aspects of data science with a focus on explainable models and
    • programming and implementation of various algorithms with a focus on their application to various domains.

    Areas within statistics include:

    • introduction to data analysis, descriptive statistics, collection and handling of data,
    • the process of statistical analysis i.e., statistical modelling and inference,
    • Bayesian inference,
    • forecasting and decision making under uncertainty,
    • relationship between variables and how they can be used for prediction and
    • statistical programming in R.

    Areas within decision analysis include:

    • formal methods for handling bases for decisions in a structured way and with respect to uncertainty, finding decision alternatives and comparing the consequences of the decision alternatives even when there are several criteria and stakeholders and
    • risk analysis where possible negative consequences are identified and analysed within a business or organisation.

    All courses are either within the main field of computer and systems science (DSV) or statistics (STAT).

    Year 1

    1st semester
    Foundations of Data Science 7,5 credits (DSV)
    Decision Analysis I 7,5 credits (DSV)
    Statistics and data analysis for computer and systems sciences 15 credits (STAT)

    2nd semester
    Data mining 7,5 credits (DSV)
    Decision Analysis II 7,5 credits (DSV)
    Statistical theory and modelling 7,5 credits (STAT)
    Machine learning 7,5 credits (DSV)

    Year 2

    3rd semester
    Risk Analysis 7,5 credits (DSV)
    Bayesian learning 7,5 credits (STAT)
    Reinforcement learning 7,5 credits (DSV)
    Business Analytics 7,5 credits (DSV)

    4th semester
    Master thesis 30 credits

  • How to apply

    Required supporting documentation

    Motivation letter
    The letter shall include:

    • Tell us something about yourself. Who are you?
    • Motivate why you want to study this programme.
    • Describe how you fulfil the entry requirements of 7,5 credits in programming. How do I fulfil the specific requirements in programming? Find description below.

    Maximum one A4 page.
    Save the letter as “Motivation letter SDSBO”.
    Submit the letter together with your application at


    How do I fulfil the specific requirements in programming?
    Any programming language course (Python, C, C++, Java, JavaScript etc.) is acceptable as long as it includes hands-on programming (writing actual code). Any programming language type is acceptable (object-oriented, procedural, logic-based etc.) provided that the course content includes at least the basic knowledge to understand and use the language in practice.

    Please note again that the course needs to involve code implementation so courses which only include mark-up languages (i.e. HTML, XML), style sheet languages (i.e. CSS) are not counted in.

    Note: If you wish to include independent courses, remember that they need to be offered by an accredited University. Courses offered by online learning platforms (Coursera, Udemy etc.) are not counted in.

  • More information

    Admission round

    This program starts each autumn semester. 

    Please note that it is only possible to apply for this programme in the first admission round (mid-October to mid-January). The programme is not open for admission in the second admission round.

    Find answers to the most common questions regarding application and requirements.
    FAQ Master's programmes


    Find the degree awarded for this programme in the syllabus, either in the right sidebar (desktop) or below (mobile device).

    Please note, that you can only be awarded one bachelor’s degree, one master’s degree (60 credits) and one master’s degree (120 credits) in computer and systems sciences from our department.


    Research subjects at the participating departments with relevance to the program:

    AI and Data Science
    Bayesian Inference
    Risk and Decision Analysis
    Time series analysis

  • Career opportunities

    This program will provide opportunities in a variety of fields. For example, your future career could be as a:

    • Data Scientist: Apply your analytical skills to extract valuable insights from large datasets and develop machine learning models to solve complex problems.
    • Data Analyst: Use statistical techniques to interpret data, generate reports on your results and findings, and provide actionable recommendations to support business decisions.
    • Machine Learning Engineer: Design, implement, and optimize machine learning algorithms and models to enable automated decision-making and business analytics.
    • Research Scientist: Conduct advanced research in data science, statistics, and decision analysis, contributing to academic studies or pushing the boundaries of industry knowledge.
    • Data Consultant: Provide expert guidance to clients on data-driven strategies, assist in implementing data-driven solutions, and drive business growth through analytics.
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