Course syllabus autumn 2020
Course syllabus autumn 2020
Title
Statistical Methods for Data Science
Swedish title
Matematisk statistik för data science
Course code
MA660E
Credits
7.5 credits
Grading scale
UA / Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U)
Language of instruction
English
Decision-making body
Faculty of Technology and Society
Syllabus approval date
2019-03-25
Syllabus valid from
2020-08-31
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.
Level
Advanced level
No main field.
Progression level
A1N
Progression level in relation to degree requirements
The course is part of the programme Computer Science: Applied Data Science, master’s programme, and can be included in the master's degree in computer science (120 credits).
Course objectives
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 contents
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
Learning outcomes
Knowledge and understanding
For a passing grade the student shall be able to:
- Describe summary measurements for describing data sets, such as position, dispersion and dependency measurements
- Explain basic concepts and laws in probability theory and inference theory
- Describe basic statistical models used in data science
Competence and abilities
For a passing grade the student shall be able to:
- set up appropriate stochastic models and use these for calculating summary measurements and probabilities.
- choose appropriate methods for analysing data collected from an experiment or network.
- verbally and in writing describe and discuss information, problem and solutions in dialogue with different groups.
Evaluation abilities and approach
For a pass grade the student shall be able to:
- analyse and critically review data analyses for data collected in published reports and articles.
- demonstrate insight into the role of data analysis in the digital society and people’s responsibility for how it is used.
Learning activities
Lectures, computer laboratories, seminars
Assessment
The course is examined by:
- Written examination (3.5 credits, assessed with UA)
- Oral presentation at seminars (2.0 credits, assessed with UG)
- Laboratory work (2.0 credits, assessed with UG)
An A-E pass requires that all parts have been completed and passed. The final grade is based on the written examination.
Course literature and other study material
- 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.
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).
Interim rules
When a course is no longer given, or the contents have been radically changed, the student has the right to re-take the examination, which will be given twice during a one year period, according to the syllabus which was valid at the time of registration.
Additional information
The syllabus is a translation of a Swedish source text.