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Master’s Programme in Decision analysis and Data science

  • 120 credits

We run into decision problems almost everywhere and a proper handling of them is fundamental for a well-functioning society. Decisions that are not thought through and made by a “gut feeling” often lead to unfavourable consequences.

In this programme you will learn how to handle decision situations in a systematic way so that preferred consequences are probable.

After the programme you will be familiar with all the stages in decision making from selecting, gathering and processing background information, structuring of the problem and assessing the consequences to making the actual decision based on rational principles.

Two tracks: data science and decision and risk analysis

In the first year you will learn about both subject areas and in the second year you will specialise in either one.

The data science track covers how to extract knowledge from data by, for example, machine learning, big data analytics, data mining and time series. In terms of the decision making process, this is the beginning where the bases for decisions are found.

The decision and risk analysis track focuses on the later stages such as finding, evaluating and choosing alternatives but also how to deal with uncertain factors such as subjective values and future outcomes. You will also learn to spot the difference between rational and irrational arguments for decisions.

  • Programme overview

    You will find detailed course information, list of course literature, schedule and start date at courses and timetables. Select semester in the drop-down menu and search by course name.

    Year 1

    1st Semester

    Mandatory courses 4 x 7,5 credits

    Decision Support Methods 7,5 credits
    The course aims to provide basic knowledge on decision support methods and decision analysis, ability to structure and evaluate decision problems and to analyse and evaluate different solutions.

    Risk Management 7,5 credits
    The main focus is on examining how the authorities of a modern society copes with risks within their domains. Here special emphasis is placed on financial risks and physical hazards. The course presents probabilistic as well as non-probabilistic approaches to risk management. The rationale behind various types of risk analyses are discussed in some detail.

    Decision Theory 7,5 credits
    This course presents the basic ingredients of a decision-theoretic approach to decision problems. Moreover, the concepts of classical decision theory are introduced in some detail and various modern attempts at modifying the classical theory are discussed at some length. Here special emphasis is on presenting Super Soft Decision Theory which by and large has been developed at DSV.

    Programming for Data Science 7.5 credits
    You will learn the languages Python and R from the start as tools for data retrieval, data cleaning, data exploration, data visualization and predictive modelling. Note that you must have at least the grade C on this course to be able to select the data science track.

    2nd Semester

    Mandatory courses 4 x 7,5 credits

    Scientific Communication and Research Methodology 7,5 credits
    Computing as a discipline combines three academic traditions: the theoretical tradition, the scientific (experimental) tradition and the engineering tradition. Due to that combination, there is no clear methodological tradition in computer science. This course introduces how to design, implement and report a research study. The main focus of this course is research design and reporting. Students will learn how to align problem statement, aims, objectives, research questions, data collection and analysis and reporting into a coherent and logically flowing whole.

    Analysis of Bases for Decisions 7,5 credits
    This course prestents some useful tools from the theory of argumentation. The main focus is on applying these tools and the ones introduced in previous courses in detailed examinations of the bases for decisions made by political assemblies and authorities. You have the opportunity of selecting decisions that is of particular interest to you.

    Business Analytics 7,5 credits
    The course develops modelling skills for management contexts, for example finance, logistics, workforce scheduling, marketing, IT infrastructure and energy. Appropriate quantitative methods will be introduced with spreadsheet applications and case studies. The methods include linear and integer programming, network models, multi-period models, goal programming, simulation and project management. ​

    Logic 7,5 credits
    Normal analysis can compensate for the usual ambiguity of normal human reasoning and uncertainties of the human cognition. Furthermore, it can increase the understanding of human communication and assist in avoiding many misunderstandings and misconceptions in real life situations and facilitate the analysis of argumentation and decision-making. This course is an introduction to the principles of correct reasoning as they are manifested in various uses of languages. The course focuses on formal logic and practical applications thereof with the purpose of getting a better capacity to understand many fallacies in reasoning and to practice deductive thinking. To be able to do this, some knowledge of formal languages and rules of deduction is inevitable.

    Year 2

    3rd Semester

    The second year, the student select a track, the Decision and risk analysis track or the Data science track

    Decision and risk analysis track:

    Mandatory courses 2 x 7,5 credits and 1 x 15 credits

    Research Methodology for Computer and Systems Sciences (MMII) 7,5 credits
    The course deals with research strategies (case studies, experiments and survey), methods for data collection (questionnaires, interviews and observations) and software-based analysis (thematic, conversation and interaction analysis). Statistical and mathematical methods include descriptive and inferential statistics. Evaluation of data is included.

    Methodology of Decision Analysis with Advanced Applications 15 credits
    The course focuses on a specific application of a decision support methodology in a domain selected by each student. During the course, you will apply a modern method on a non-trivial decision problem and evaluate strengths, weaknesses and potential for further development of the method or how it can be applied.

    Risk and Decision Analysis: Special Problems 7,5 credits
    The course focuses on applications of risk and decision analytic methods in business and society. Formal risk analysis, uncertainty analysis and the risk analytic aspects of decision-making under risk and multi-attribute utility theory are central parts of the course.

    Data science track:

    Note that the student must have at least the grade C on the course Programming for Data Science 7,5 hp to be able to select the data science track.

    Mandatory courses 4 x 7,5 credits

    Research Methodology for Computer and Systems Sciences (MMII) 7,5 credits
    The course deals with research strategies (case studies, experiments and survey), methods for data collection (questionnaires, interviews and observations) and software-based analysis (thematic, conversation and interaction analysis). Statistical and mathematical methods include descriptive and inferential statistics. Evaluation of data is included.

    Data mining in Computer and System Sciences 7,5 credits
    As data is becoming more and more readily available, the need to analyse and make use of these large amounts of data is rapidly growing. Data mining deals with techniques that can find interesting and useful patterns in large volumes of data. This course covers basic concepts, techniques and algorithms in data mining combined with hands-on experimentation.

    Research topics in Data Science 7,5 credits
    The course introduces current research topics in data science, methods and techniques for collecting and organizing and analysing data with the purpose of extracting new knowledge. You will learn to identify and formulate research questions within the area, to choose and apply a research method, to plan and execute research studies, including data collection and analysis, and to present results and draw conclusions.

    Big Data with NoSQL Databases 7,5 credits
    The course discusses the motivations behind the development of Big Data and the technologies developed to handle the properties of Big Data. These can usually not be handled by traditional database management systems due to the volume, variation and speed of the data with which they are generated. Alternative forms of representation of data have therefore evolved within the NoSQL framework. The course addresses different approaches to NoSQL within Hadoop, which is a modular framework that allows distributed storage and analysis of large amounts of data. The course covers different data sources and types of data including streaming data. The course also deals with predictive modelling with large amounts of data and gives examples of some typical applications.

    4th Semester

    Master Thesis 30 credits
    Information regarding master thesis

  • How to apply

    Find answers to the most common questions regarding application, requirements and study format (distance or campus) here

    Selection process

    Additional eligibility criteria

    The selection of students is based on grades of academic courses.

    This means that you don’t have to submit recommendation letters or motivation letter when applying to this specific programme.

    Required supporting documentation

    Along with your supporting documents at, you are required to submit a separate form with a list of proof of specific entry requirements. Download the form below.

    • List the courses, from your uploaded transcript of courses, that you want to use to meet the specific requirements for this programme. 
    • Submit links to the course description and learning outcomes (and/or objectives) of each course stated on your University’s website. Or link to an official descriptive document of the course (for example a pdf). 
    • Upload the form along with your supporting documentation at

    Please note!

    • This form is for specific requirements only, to assist the admission board to navigate in your uploaded supporting documents. You need to submit all your supporting documentation, including general entry requirements (Bachelor’s degree, English proficiency etc.), as instructed, at
    • The department is not able to give advance notice regarding special requirements. Please, apply via and submit documents for the admission board to review.

    Courses that meet the requirements 

    Special requirements for this programme: 15 ECTS credits in Mathematics or Programming.

    In order to fulfil the entry requirement of 15 credits/ECTS in mathematics/programming, you need to have obtained at least 15 credits/ECTS in these subjects (combination of either or both), either as part of your previous education or independent courses.

    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.

    Mathematics: any mathematics course including mathematical reasoning and calculations, i.e. mathematical analysis, calculus, algebra, geometry, discrete mathematics, probability theory, differential equations.

    Please note that we only count in Mathematical statistics and not regular statistics (i.e. statistics that only involve the use of statistical tools and representation).

    Applicants with a bachelor’s degree certificate on Economics fulfil the eligibility criterion of 15 credits/ECTS in mathematics, as long as the whole degree is an Economics degree and not partially/consisting of independent courses in Economics.

    Programming: any programming language course (Python, C, C++, Java, Javascript etc.) 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.

    In addition, Database-related courses are acceptable as long as they involve implementation and not only using the database (i.e. queries).

    Download form:  Specific entry req. Decision analysis and Data science 120 credits (357 Kb)

  • Career opportunities

    After graduation, you will be qualified to assess and handle risks as well as form strategies. You will be able to provide decision makers with well-founded advice in tricky situations.

    Apart from academia and Ph.D. studies, graduates may pursue careers in a variety of organisations such as large and mid-size corporations, banks and financial institutes, public sector agencies and non-governmental organisations. 

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