Course Syllabus for

# Data Analysis: Statistical Learning and Visualization with Project Dataanalys: statistisk inlĂ¤rning 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

## Aim

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.

## Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• Describe different ways of aggregating, summarising and visualising data.
• Explain the principles of dimension reduction.
• Explain the principles of supervised and unsupervised learning.

Competences and Skills

For a passing grade the doctoral student must

• be able to wrangle, present and visualise data to highlight important features in a complex data material.
• be able to perform dimension reduction and imputation of missing data.
• be able to use common methods for classification, supervised and unsupervised learning.
• use methods for classification and statistical learning to draw conclusion regarding a data material.
• present the analysis and conclusions of a practical problem in a written report.

Judgement and Approach

For a passing grade the doctoral student must

• Reflect over the limitations of the chosen model and method, as well as alternative solutions.
• Reflect over the possible issues with fitting multiple models to the same data material.

## 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.