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

FMSF90F, 7.5 högskolepoäng

Gäller från och med: Spring 2023
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 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.

Mål

Kunskap och förståelse

För godkänd kurs skall doktoranden

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

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 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.

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 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.

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

Examinationsform: Inlämningsuppgifter. 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.
Betygsskala: Underkänd, godkänd
Examinator:

Antagningsuppgifter

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

Kurstillfällesinformation

Kontaktinformation och övrigt

Kursansvariga:
Hemsida: http://www.ctr.maths.lu.se/utbildning/matematisk-statistik/