About the course

The purpose of the course is for the student to develop the ability to use methods from mathematical statistics (probability theory and inference theory) to understand random variations and identify patterns in the data collected. The student also achieves general basic understanding of the field of data science.

Course content

The course contains the following elements:

- Introduction to data science
- Dispersion measurement
- Conditional probability, Bayes’ theorem
- Distribution of stochastic variables
- Central limit theorems.
- Confidence interval
- Hypothesis testing
- Regression analysis
- Data analytics software

Syllabus and course literature

You can find a list of literature in the syllabus, along with other details about the course.

Entry requirements and selection

Entry requirements

Bachelor of Science in computer science or related subjects.

Knowledge equivalent to English 6 at Swedish upper secondary level.

At least 15 credits in programming.

At least 7.5 credits in mathematics.


University credits completed 100%

Course evaluation

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).


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