Statistical Methods for Data Science

Summary

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.

Admission requirements

  1. Bachelor of Science in computer science or related subjects.
  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.

Syllabus

Syllabus for students autumn 2020

Course Code:
MA660E revision 1
Swedish name:
Matematisk statistik för data science
Level of specialisation
A1N
Main fields of study:
No main fields
Language:
English
Date of ratification:
25 March 2019
Decision-making body:
Faculty of Technology and Society
Enforcement date:
31 August 2020

Entry requirements

  1. Bachelor of Science in computer science or related subjects.
  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.

Specialisation and progression relative to the degree regulations

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).

Purpose

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.

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

Assessments

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.

Grading system

Excellent (A), Very Good (B), Good (C), Satisfactory (D), Pass (E) or Fail (U).

Course literature and other teaching materials

  • 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.

Other Information

The syllabus is a translation of a Swedish source text.

Contact

The education is provided by the Faculty of Technology and Society at the Materials Science and Applied Mathematics.

Further information

Application

31 August 2020 - 08 November 2020 Day-time 50% Malmö Schedule This course is offered as part of a program

Tuition fees

for non-EU students only

First instalment: 16000 SEK
Full tuition Fee: 16000 SEK