[ 951SMDSSPDK20 ] KV (*)Statistical Principles of Data Science

(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
6 ECTS M1 - Master 1. Jahr Statistik Andreas Futschik 3 SSt Johannes Kepler Universität Linz
Quellcurriculum Masterstudium Statistics 2021W
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.

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

Big data and large scale inference: big "n" vs. big "p"; introduction to and application of a specific advanced statistical method such as, for example, sparse modelling and lasso; random forests, boosting, shrinkage and empirical Bayes.

Beurteilungskriterien (*)Homework plus written exam.
Abhaltungssprache Englisch
Literatur (*)Bradley Efron and Trevor Hastie: Computer Age Statistical Inference. Cambridge University Press 2016.
Lehrinhalte wechselnd? Ja
Äquivalenzen (*)951SMDSSPDK17: KV Statistical Principles of Data Science (6 ECTS)
Teilungsziffer 25
Zuteilungsverfahren Zuteilung nach Vorrangzahl