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

FMSF90F, 7.5 högskolepoäng

Gäller från och med: Spring 2025
Beslutad av: Maria Sandsten
Datum för fastställande: 2022-10-28

Allmänna uppgifter

Avdelning: Mathematical Statistics
Kurstyp: Gemensam kurs, avancerad nivå och forskarnivå
Kursen ges även på avancerad nivå med kurskod: FMSF90
Undervisningsspråk: English

Syfte

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.

Mål

Kunskap och förståelse

För godkänd kurs skall doktoranden

• 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.

Färdighet och förmåga

För godkänd kurs skall doktoranden

• 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.

Värderingsförmåga och förhållningssätt

För godkänd kurs skall doktoranden

• 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.

Kursinnehåll

* 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.

Kurslitteratur

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

Kursens undervisningsformer

Undervisningsformer: Föreläsningar, laborationer, projekt

Kursens examination

Examinationsformer: Muntlig tentamen, skriftlig rapport. Passing grade on all written lab reports, passing grade on the final project report and oral presentation of the final project.
Betygsskala: Underkänd, godkänd
Examinator:

Antagningsuppgifter

Förkunskapskrav: Basic statistics
Förutsatta förkunskaper: Basic statistics, some programming experience.

Övrig information

Studied together with FMSF90.

Kurstillfällesinformation

Kontaktinformation och övrigt

Kursansvariga:
Hemsida: https://www.maths.lu.se/utbildning/civilingenjoersutbildning/matematisk-statistik-paa-civilingenjoersprogram