• CM121A Analytics I (7.5 credits)
• CM143A Analytics II (7.5 credits)
• CM152A Mathematical Statistics (7.5 credits)

The course is part of the degree requirements for a Master of Science in Engineering in Computer Science and Engineering (specialisation Applied Data Science)

The course offers an in-depth study of probability and statistics that is relevant for further studies in data science. The aim of the course is for the student to develop a deeper understanding of stochastic processes and the ability to apply statistical inference in handling large datasets.

The course contains the following components:

• Probability theory: functions of random variables, central moments, generating functions and convergences of random variables
• Bayesian statistics: Bayes theorem, posterior distribution, maximum-likelihood method (MLE), maximum-A posterior estimation (MAP)
• One-factor experiment design, data collection and data quality
• In-depth regression analysis: weighted least squares method, autoregression, non-linear and logistic regression

Advanced use of statistical software

Knowledge and understanding

Upon completion of the course, the student shall be able to:

1. explain statistical data analysis concepts,

2. describe properties and basic laws of distributions of and relations between random variables, and

3. describe appropriate statistical models used in data analysis in data science.

Competence and skills

Upon completion of the course, the student shall be able to:

4. independently plan and carry out data collection and validate the validity of collected data, and

5. carry out a complete data analysis of collected data using computer programs and statistical models used in data science, and interpret the results from different aspects, such as gender equality.

Judgement and approach

Upon completion of the course, the student shall be able to:

6. critically review and evaluate statistical results published in scientific journals, and

7. reflect on individual responsibility for the correct application of statistics.

Lectures, computer lab sessions, seminars, and self-study.

The following are required to pass the course

• passing grade on written examination (4.5 credits, UA) (Intended learning outcomes 1–4, 6)
• passing grades on seminars (1.0 credits, Pass/Fail) (Intended learning outcomes 6, 7)
• passing grades on lab session work (2.0 credits, Pass/Fail) (Intended learning outcomes 1, 4, 5)

For all assessments, the materials must be presented in a manner that makes it possible to discern individual performance.

The final grade corresponds to the grade of the written examination.

• Koski, T. (2020) Probability calculus for data science, Studentlitteratur.
• Mendenhall, W. & Sincich, T. (2019) Regression analysis: A second course in statistics (8th edition), Pearson.
• Walpole, R. E. and Myers, R. (2016) Probability and statistics for engineers & scientists (9th edition), Pearson.

Compendium from the department.

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).

If a course is no longer offered, or has undergone significant changes, the students must be offered two opportunities for re-examination based on the syllabus that applied at the time of registration, for a period of one year after the changes have been implemented. The syllabus is a translation of a Swedish source text.

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format. The syllabus is a translation of a Swedish source text.