- Fernandez-Granda, C. Probability and Statistics for Data Science, New York University, 2017
- Myers, W & Ye, W. Probability and statistics: for engineers and scientists. Prentice Hall, 2010.
EduSinglePage
Course offered as a part of a programme
Course, master’s level
7.5 credits
Malmö | daytime | 50%
1 September 2025 - 9 November 2025
Code for the course: MA660E
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.
This course is offered as part of programme:
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
Entry requirements
1. Bachelor of Science (at least 180 higher education credits) in computer science or related subjects such as mathematics, informatics, telecommunications, electrical engineering, physics.
2. Knowledge equivalent to English 6 at Swedish upper secondary level.
3. At least 15 credits in programming.
4. At least 7.5 credits in mathematics.
Course literature
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).
