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.

 

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

In this course probabilistic as well as non-probabilistic approaches to risk management are presented, and the rationale behind various types of risk analyses are discussed in some detail. The main focus, however, 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.

Decision Theory 7,5 credits

In this course, the basic ingredients of a decision-theoretic approach to decision problems are presented. 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 computer science students to the principles of scientific research: how to design, implement, and report a research study. The main focus of this course is in 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

In this course some useful tools from the Theory of Argumentation are presented but 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. The student here has the opportunity of selecting decisions that is of particular interest to her.

Programming for Data Science 7.5 credits

In this course the languages Python and R are taught from the beginning as tools for data retrieval, data cleaning, data exploration, data visualization and predictive modelling. Note that the student must have at least the grade C on this course to be able to select the data science track.

Business Analytics 7,5 credits

This course develops modelling skills for management contexts, e.g., 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

Formal 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.

 
3rd Semester

The second year, the student select a track:
Decision and risk analysis track | 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

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, the student 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

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. The student 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 representations 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 in Computer and Systems Sciences 30 credits

Information regarding Master Thesis