NMFP406 Data Science 1
- Timetable
- Thursday 12:20 K3
- SIS official site
Exam
- Two home projects (1st one - the first half of semester, 2nd one - the end of semester)
- Oral part composed of the projects' defence and additional questions
Lectures
- 19. 2. 2026 - Robust regression
- 26. 2. 2026 - Home self-study
- 5. 3. 2026 - Logistic regression
- 12. 3. 2026 - Exact logistic regression and Probit regression
- 19. 3. 2026 - Home self-study
- 26. 3. 2026 - Home self-study
- 2. 4. 2026 - Multinomial logistic regression and Ordinal logistic regression
- 9. 4. 2026 - Poisson regression and Negative binomial regression
- 16. 4. 2026 - Zero-truncated Poisson regression, Zero-truncated NB regression, Zero-inflated Poisson regression, and Zero-inflated NB regression
- 23. 4. 2026 - Survival data analysis
- 30. 4. 2026 - Proportional hazards
- 7. 5. 2026 - Tobit regression, Truncated regression, and Interval regression
- 14. 5. 2026 - Generalized linear mixed models
- 21. 5. 2026 - Mixed effects logistic regression