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.

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.

2nd Semester

Mandatory courses 4 x 7,5 credits

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.

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.

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 or exchange studies (information regarding exchange studies).

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 or exchange studies (information regarding exchange studies).

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

FAQ - DSV application

Selection process

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 univeristyadmissions.se, 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. Upload the form along with your supporting documentation at universityadmissions.se

Please note!

  • Swedish applicants: This form is required for Swedish applicants, as well as international applicants, when applying for this programme
  • 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 universityadmissons.se
  • The department is not able to give advance notice regarding requirements. You must apply via the central admission’s web platform (universityadmissions.se) and upload documents for the admission board to review.
  • You need to fill out one form for each of the programmes you are applying for.

Tip: You are able to fill out the form online if you use Acrobat reader.

Download form: Specific entry req. Decision analysis and Data science (195 Kb)

Please use Example_form.pdf for reference: Example_form (175 Kb)

Employment market and career

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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