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Data Science 1 - NMFP406

Title:	Data Science 1
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2022
Semester:	summer
E-Credits:	3
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English, Czech
Teaching methods:	full-time

Guarantor:	doc. RNDr. Michal Pešta, Ph.D.
Class:	M Mgr. FPM M Mgr. FPM > Povinné
Classification:	Mathematics > Financial and Insurance Math.
Pre-requisite :	{At least one courses in linear regression models}
Incompatibility :	NMFM404
Interchangeability :	NMFM404
Is incompatible with:	NMFM404
Is interchangeable with:	NMFM404

Opinion survey results Examination dates Noticeboard

Annotation -

Machine learning + statistical inference = statistical learning. Methods of statistical learning for categorical (unordered and ordered), nominal (discrete and continuous), truncated, censored, extremal, and clustered data. Modeling (not only) financial, economic, and insurance processes. Practical exercises and problems from econometrics, model testing, parameter estimation, prediction in stochastic models and their diagnostics. Computational demanding stochastic techniques.

Last update: Branda Martin, doc. RNDr., Ph.D. (05.12.2020)

Aim of the course -

To explain basics of statistical learning (machine learning + statistical inference = statistical learning).

Applications to finance and insurance.

Last update: Pešta Michal, doc. RNDr., Ph.D. (26.05.2025)

Course completion requirements -

Successfully pass the exam.

Last update: Pešta Michal, doc. RNDr., Ph.D. (26.05.2025)

Literature -

[1] Peter Dalgaard: Introductory statistics with R. Birkhäuser, 2002.

[2] David W. Hosmer and Stanley Lemeshow: Applied Logistic Regression (Second edition). Wiley, 2000.

[3] John H. Maindonald, John Braun: Data analysis and graphics using R: an example-based approach (Third edition). Cambridge University Press, 2010.

[4] David Ruppert: Statistics and finance: An introduction. Springer, 2004.

[5] William N. Venables, Brian D. Ripley: Modern applied statistics with S. Birkhäuser, 2002.

[6] Gareth James, Diana Witten, Trevor Hastie, and Robert Tibshirani: An Introduction to Statistical Learning (with Applications in R). Springer, 2013.

[7] Christian Bluhm, Ludger Overbeck, and Christoph Wagner: Introduction to Credit Risk Modeling (Second edition). Chapman & Hall, 2010.

Last update: Pešta Michal, doc. RNDr., Ph.D. (05.12.2020)

Teaching methods -

Lecture.

Last update: Zichová Jitka, RNDr., Dr. (03.06.2022)

Requirements to the exam -

Two home projects (1st project consisting of two reports - the first half of semester, 2nd project consisting of the third report and two revisions of the first and second annotated reports - the end of semester).

Oral part of the exam composed of the projects' defence and additional questions.

Last update: Pešta Michal, doc. RNDr., Ph.D. (26.05.2025)

Syllabus -

1. Robust regression. 2. Logistic regression. 3. Multinomial regression. 4. Poisson regression. 5. Truncated and inflated data. 6. Survival analysis. 7. Analysis of panel/longitudinal data.

Last update: Pešta Michal, doc. RNDr., Ph.D. (05.12.2020)

Entry requirements -

Fundamentals of mathematical statistics and linear regression.

Last update: Pešta Michal, doc. RNDr., Ph.D. (26.05.2025)