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

FMSF90F, 7.5 credits

Valid from: Spring 2025
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 supervised and unsupervised learning. Issues arising from fitting and evaluating multiple models as well as the methods relationship to linear 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 supervised and unsupervised learning.
• Explain the importance of evaluating models based on their predictive ability.

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 use common methods for supervised and unsupervised learning.
• be able to draw conclusions regarding a data material, based on results from classification and regression methods.
• be able to use common method for evaluation of predictive ability on out-of-sample data.
• 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 unsupervised and supervised learning such as: clustering; hierarchical clustering; and regression and decision tree methods for classification and regression problems. * Methods for model selection and validation such as: bootstrap, split of data into training and test, and cross-validation.

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 formats: Oral exam, written report. Passing grade on all written lab reports, passing grade on the final project report and oral presentation of the final project.
Grading scale: Failed, pass
Examiner:

Admission Details

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

Further Information

Studied together with FMSF90.

Course Occasion Information

Contact and Other Information

Course coordinators:
Web page: https://www.maths.lu.se/utbildning/civilingenjoersutbildning/matematisk-statistik-paa-civilingenjoersprogram