- 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.
Statistical Methods for Data Science
Freestanding course
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 a programme:
Computer Science: Applied Data Science, Master's Program (Two-Year)
Course content
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.
Selection
University credits completed 100%
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).
