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.

• 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 universityadmissions.se

   

  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.

  Read our step-by-step guide on how to apply

• 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

  Degree

  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

  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.

• Contact

  prospectivestudent@dsv.su.se