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Statistics for Data Science (628PP) A.Y. 2024/25

Instructors

Hours and rooms

Day of Week Hour Room
Tuesday 14:00 - 16:00 Fib-C
Wednesday 9:00 - 11:00 Fib-C1
Thursday 9:00 - 11:00 Fib-C1

Pre-requisites

Students should be comfortable with most of the topics on mathematical calculus covered in:

  • [P] J. Ward, J. Abdey. Mathematics and Statistics. University of London, 2013. Chapters 1-8 of Part 1.

Extra-lessons refreshing such notions may be planned in the first part of the course.

Mandatory Teaching Material

The following are mandatory text books:

  • [T] F.M. Dekking C. Kraaikamp, H.P. Lopuha, L.E. Meester. A Modern Introduction to Probability and Statistics. Springer, 2005.
  • [R] P. Dalgaard. Introductory Statistics with R. 2nd edition, Springer, 2008.
  • selected chapters of other books for advanced topics

Software

Preliminary program and calendar

Exams

There are no mid-terms. The exam consists of a written part and an oral part. The written part consists of exercises and questions on the topics of the course. Each question is assigned a grade, summing up to 30 points. Example written texts: sample1, sample2. Students are admitted to the oral part if they receive a grade of at least 18 points. The oral part consists of critical discussion of the written part and of open questions and problem solving on the topics (both theory and R programming) of the course. In particular, students must demonstrate to be able to summarize both the theory and the software related to any of the lessons using the slides and R scripts of the lessons.

Registration to exams is mandatory (beware of the registration deadline!): register here. The dates below are only for the written test (normal exam). Dates for project discussion are included in the project description.

Date Hour Room Notes

Student project

  • The project replaces the written part of the examination
  • Project description and rules and Q&A will be published here in April.

Class calendar

A Teams channel is used to post news, notes, Q&A, and other stuff related to the course.

The lectures will be only in presence and will NOT be live-streamed. Recordings from previous years are available for non‑attending students (see previous years section); however, these materials may not fully correspond to the content taught in the current academic year.

Material of future lessons refer to the last academic year. Slides and R scripts might be updated after the classes to align with actual content of current year and to correct typos. Be sure to download the updated versions.

# Date Room Topic Mandatory teaching material
01 17/02 11-13 Fib-E Introduction. Probability and independence. rec01 (.mp4) [T] Chpts. 1-3 slides01 (.pdf)
02 17/02 14-16 Fib-C R basics. rec02 (.mp4) [R] Chpts. 1,2.1-2.3 slides02 (.pdf), script02 (.R)
03 20/02 11-13 Fib-A1 Bayes' rule and applications. rec03 (.mp4) [T] Chpt. 3 slides03 (.pdf), script03 (.R)
04 24/02 14-16 Fib-C Discrete random variables. rec04 (.mp4) [T] Chpts. 4, 9.1, 9.2, 9.4 [R] Chpt. 3 slides04 (.pdf), script04 (.R)
05 25/02 14-16 Fib-A1 Discrete random variables (continued). rec05 (.mp4)
06 27/02 11-13 Fib-A1 Recalls: derivatives and integrals. rec06 (.mp4) [P] Chpt. 1-8 slides06 (.pdf), script06 (.R)
07 03/03 14-16 Fib-C R data access and programming. rec07 (.mp4) [R] Chpt. 2.3,2.4 script07 (.zip)
08 04/03 14-16 Fib-A1 Continuous random variables.rec08 (.mp4) [T] Chpts. 5, 9.2-9.4 [R] Chpt. 3 slides08 (.pdf), script08 (.R)
09 06/03 11-13 Fib-A1 Expectation and variance. Computations with random variables.rec09 (.mp4) [T] Chpts. 7,8 slides09 (.pdf), script09 (.R)
10 10/03 14-16 Fib-C Expectation and variance. Computations with random variables (continued). Moments. Functions of random variables. rec10 (.mp4) [T] Chpts. 9-11 slides10 (.pdf), script10 (.zip)
11 11/03 14-16 Fib-A1 Functions of random variables (continued). Distances between distributions. rec11 (.mp4) Murphy's book Chpt. 6 slides11 (.pdf), script11 (.R)
12 13/03 11-13 Fib-A1 Simulation. rec12 (.mp4) [T] Chpts. 6.1-6.2 slides12 (.pdf), script12 (.R) script12_sol07 (.R)
13 17/03 14-16 Fib-C Power laws and Zipf's law. rec13 (.mp4) Newman's paper Sect I, II, III(A,B,E,F) slides13 (.pdf), script13 (.R)
14 18/03 14-16 Fib-A1 Law of large numbers. The central limit theorem. rec14 (.mp4) [T] Chpts. 13-14 slides14 (.pdf), script14 (.R)
15 20/03 11-13 Fib-A1 Graphical summaries. Kernel Density Estimation. rec15 (.mp4) [T] Chpt. 15, [R] Chpt. 4 slides15 (.pdf), script15 (.R)
16 24/03 14-16 Fib-C Numerical summaries.rec16 (.mp4) [T] Chpt. 16, [R] Chpt. 4 slides16 (.pdf), script16 (.R)
17 25/03 14-16 Fib-A1 Data preprocessing in R. Estimators.rec17 (.mp4) [R] Chpt. 10, [T] Chpts. 17.1-17.3script17 (.R), dataprep.R
18 27/03 11-13 Fib-A1 Unbiased estimators. Efficiency and MSE.rec18 (.mp4) [T] Chpts. 19, 20 slides18 (.pdf), script18 (.R)
19 31/03 14-16 Fib-C Maximum likelihood estimation.rec19 (.mp4) [T] Chpt. 21 s4dsln.pdf Chpt. 1 slides19 (.pdf), script19 (.R)
20 01/04 14-16 Fib-A1 Linear regression. Least squares estimation.rec20 (.mp4) [T] Chpts. 17.4,22 [R] Chpt. 6 s4dsln.pdf Chpt. 2 slides20 (.pdf), script20 (.R)
21 03/04 11-13 Fib-A1 Non-linear, and multiple linear regression.rec21 (.mp4) [R] Chpt. 12.1,13,16.1-16.2 s4dsln.pdf Chpt. 2 slides21 (.pdf), script21 (.R)
22 07/04 14-16 Fib-C Issues with linear regression. Logistic regression.rec22 (.mp4) [R] Chpt. 12.1,13,16.1-16.2 slides22 (.pdf), script22 (.zip)
23 08/04 14-16 Fib-A1 Statistical decision theory.rec23 (.mp4) s4dsln.pdf Chpt. 4 slides23 (.pdf), script23 (.R)
24 10/04 11-13 Fib-A1 Statistical decision theory (continued).rec24 (.mp4)
25 14/04 14-16 Fib-C Statistical decision theory (continued). Project presentation.
26 15/04 14-16 Fib-A1 Confidence intervals: mean, proportion, linear regression.rec26 (.mp4) [T] Chpts. 23.1,23.2,23.4,24.3,24.4 s4dsln.pdf Chpt. 3 slides26 (.pdf), script26 (.R)
27 17/04 11-13 Fib-A1 Confidence intervals (continued). Bootstrap and resampling methods.rec27 (.mp4) [T] Chpts. 18.1-18.3,23.3 slides27 (.pdf), script27 (.R)
28 24/04 11-13 Fib-A1 Bootstrap and resampling methods (continued).rec28 (.mp4)
29 28/04 14-16 Fib-C Hypotheses testing. One-sample tests of the mean and application to linear regression.rec29 (.mp4) [T] Chpts. 25,26,27, [R] Chpts. 5.1,5.2 s4dsln.pdf Chpt.3.3 slides29 (.pdf), script29 (.R)
30 29/04 14-16 Fib-A1 One-sample tests of the mean and application to linear regression (continued). Classifier performance metrics in R. rec30 (.mp4) slides30 (.pdf), script30 (.R)
31 05/05 14-16 Fib-C Two-sample tests of the mean and applications to classifier comparison. rec31 (.mp4) [T] Chpt. 28, [R] Chpts. 5.3-5.7 slides31 (.pdf), script31 (.R)
32 06/05 14-16 Fib-A1 Multiple-sample tests of the mean and applications to classifier comparison.rec32 (.mp4) [R] Chpt. 7 slides32 (.pdf), script32 (.R)
33 08/05 11-13 Fib-A1 Fitting distributions. Testing independence/association.rec33 (.mp4) [R] Chpt. 8 K-S, slides33 (.pdf), script33 (.R)
s03 12/05 14-16 Fib-C Mandatory seminar: Introduction to causal modeling and reasoning. Speakers: I. Beretta and M. Cinquini. rec_s03 (.mp4) slides_s03 (.pdf)
34 13/05 14-16 Fib-A1 Fitting distributions. Testing independence/association (continued). Project Q&A.
35 15/05 11-13 Fib-A1 Project Q&A.

Seminars of past years

In some years, speakers were invited to give a seminar on advanced topics. Here it is a list of seminars held in past years.

# Date Topic Teaching material
s01 04/05/2022 Bias in statistics and causal reasoning. Speaker: prof. Fabrizia Mealli rec_s01 (.mp4) slides_s01 (.pdf) Optional reading
s02 04/05/2022 Bias in statistics and causal reasoning (continued). Speaker: prof. Fabrizia Mealli rec_s02 (.mp4)
s03 07/05/2024 and 12/05/2025 Introduction to causal modeling and reasoning. Speakers: I. Beretta and M. Cinquini. rec_s03 (.mp4) slides_s03 (.pdf)

Past years

mds/sds/tmp.1768593212.txt.gz · Ultima modifica: 16/01/2026 alle 19:53 (15 ore fa) da Salvatore Ruggieri
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