SubjectsSubjects(version: 944)
Course, academic year 2023/2024
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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
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
Annotation -
Last update: doc. RNDr. Martin Branda, Ph.D. (05.12.2020)
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.
Literature -
Last update: doc. RNDr. Michal Pešta, Ph.D. (05.12.2020)

[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.

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


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

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.

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