Course syllabus

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

FMSF90, 7.5 credits, G2 (First Cycle)

Valid for: 2024/25
Faculty: Faculty of Engineering LTH
Decided by: PLED I
Date of Decision: 2024-04-16
Effective: 2024-05-08

General Information

Main field: Technology Depth of study relative to the degree requirements: First cycle, in-depth level of the course cannot be classified
Elective for: C4-adv, F4, Pi4, R4
Language of instruction: The course will be given in 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.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must

Judgement and approach
For a passing grade the student must

Contents

Examination details

Grading scale: TH - (U, 3, 4, 5) - (Fail, Three, Four, Five)
Assessment:

The final grade is determined by the final project. Passing grade on all written lab reports and attendance at half of the scheduled labs.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Modules
Code: 0124. Name: Computer Lab 1.
Credits: 2.0. Grading scale: UG - (U, G). Assessment: Reporting of the lab The module includes: Data handling and visualisation.
Code: 0224. Name: Computer Lab 2.
Credits: 2.0. Grading scale: UG - (U, G). Assessment: Reporting of the lab The module includes: Continuous prediction (regression)
Code: 0324. Name: Project.
Credits: 3.5. Grading scale: TH - (U, 3, 4, 5). Assessment: Written and oral project presentation. The module includes: Classification and synthesis of the entire course.

Admission

Admission requirements:

Assumed prior knowledge: A basic course in mathematical statistics and knowledge in linear algebra.
The number of participants is limited to: 50
Selection: Completed university credits within the program. (Note that only credits which according to Ladok have been included in the program before the selection process count. For students taking master's programmes 180 credits corresponding to a bachelor's degree are added.) Priority is given to students enrolled on programmes that include the course in their curriculum. Among these students place is guaranteed to those in the specialisation on Riskmodellering at Risk, säkerhet och krishantering education.
Kursen överlappar följande kurser: FMSF86 FMAN45 EDAN96

Reading list

Contact

Course coordinator: Linda Hartman, linda.hartman@matstat.lu.se
Director of studies: Johan Lindström, studierektor@matstat.lu.se
Course administrator: Susann Nordqvist, expedition@matstat.lu.se
Course homepage: https://www.maths.lu.se/utbildning/civilingenjoersutbildning/matematisk-statistik-paa-civilingenjoersprogram/

Further information

Given in parallell with FMSF86. Only one of the courses FMSF86 and FMSF90 may be included in a degree. The course overlaps with EDAN96.