Course Syllabus for

Data Analysis: Statistical Learning and Visualization with Project
Dataanalys: statistisk inlärning och visualisering med projekt

FMSF90F, 7.5 credits

Valid from: Spring 2023
Decided by: Maria Sandsten
Date of establishment: 2022-10-28

General Information

Division: Mathematical Statistics
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: FMSF90
Teaching language: English


The course begins with an overview of basic data wrangling and visualisation. With a focus on the student's ability to identify and illustrate important features of the data. Then important methods in statistical learning are introduced. Emphasis is given to dimension reduction, supervised and unsupervised learning. Issues arising from fitting multiple models (i.e. multiple testing) as well as the methods relationship to regression are discussed. Computer based labs and projects form an important part of the learning activities. The course concludes with a project where the students will select suitable methods to analyze a given data material.


Knowledge and Understanding

For a passing grade the doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must

Course Contents

* Basic methods for data handling and common visualisation methods for data * Methods for data reduction such as Principal Component Analysis (PCA) and their use for imputation of missing data. * Methods for unsupervised and supervised learning/classification such as: Support Vector Machines (SVM), clustering (K-means), hierarchical clustering, simpler regression methods, and methods for decision trees (bagging, boosting, and random forests). * Multiple testing and common solutions such as Benjamini-Hochberg and Bonferroni.

Course Literature

James, G., Witten, D., Hastie, T. & Tibshirani, R.: An Introduction to Statistical Learning: with Applications in R. Springer, 2021. ISBN 9781071614174.

Instruction Details

Types of instruction: Lectures, laboratory exercises, project

Examination Details

Examination format: Written assignments. The course is examined using four projects. Three projects covering specific parts of the course and one final project using components from the entire course. The students are encouraged to bring their own data.
Grading scale: Failed, pass

Admission Details

Admission requirements: Basic statistics
Assumed prior knowledge: Basic statistics, some programming experience.

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page:

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