Inhalt

[ 951SMDSSPDK17 ] KV (*)Statistical Principles of Data Science

Versionsauswahl
Es ist eine neuere Version 2021W dieser LV im Curriculum Masterstudium Computer Science 2023W vorhanden.
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
6 ECTS M1 - Master 1. Jahr Statistik Helga Wagner 3 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Statistics 2017W
Ziele (*)Students know basic concepts and tools of statistics for data analysis. They can apply methods designed for big data and high dimensional inference and know about pitfalls to avoid in data analysis
Lehrinhalte (*)Basic concepts of statistics: estimation, testing, prediction and classification, clustering basic statistical tools: frequentist vs. Bayesian inference; common statistical models; model selection and model averaging

big data and large scale inference: big "n" vs. big "p"; sparse modelling and Lasso; Random forests, boosting, shrinkage and empirical Bayes;

pitfalls: correlation vs. causation; all models are wrong; garbage in - garbage out; common sources of bias; Simpson's paradoxy and the perils of aggregating data; data mining, multiple hypothesis testing and the false discovery rate ; curse of dimensionality, spurious correlation, incidental endogeneity

Beurteilungskriterien (*)Homework plus written exam.
Abhaltungssprache Englisch
Literatur (*)Bradley Efron and Trevor Hastie: Computer Age Statistical Inference. Cambridge University Press 2016.
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Präsenzlehrveranstaltung
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