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Statistics for Data Science (628PP) A.Y. 2022/23



Day of Week Hour Room
Wednesday 9:00 - 11:00 Fib-C
Thursday 11:00 - 13:00 Fib-C
Friday 14:00 - 16:00 Fib-C


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


Preliminary program and calendar


There are no mid-terms. The exam consists of a written part and an oral part. The written part consists of exercises on the topics of the course. Each question is assigned a grade, summing up to 30 points. Students are admitted to the oral part if they receive a grade of at least 18 points. The written part consists of open questions and exercises. Example written texts: sample1, sample2. 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.

Registration to exams is mandatory (beware of the registration deadline!): register here

Date Hour Room Notes
22/6/2023 11:00 - 13:00 Fib-M1
12/7/2023 11:00 - 13:00 Fib-M1
5/9/2023 11:00 - 13:00 Fib-A1

Student project

Teams channel

A Teams channel will be used to post news, Q&A, and other material related to the course.

Class calendar

Lessons will be NOT be live-streamed, but recordings of past years are available here for non-attending students.

To watch the recordings online, you must be connected to the VPN. Alternatively, right click on the link and download the whole file, then watch it locally on your device using e.g. VLC media player.

Slides and R scripts might be updated after the classes to align with actual content of lessons and to correct typos. Be sure to download the updated versions.

# Date Room Topic Teaching material
01 22/02 9-11 Fib-C Introduction. Probability and independence. rec01 (.mp4) [T] Chpts. 1-3 slides01 (.pdf)
02 23/02 11-13 Fib-C R basics. rec02 (.mp4) [R] Chpts. 1,2.1-2.3 slides02 (.pdf), script02 (.R)
03 24/02 14-16 Fib-E Bayes' rule and applications. rec03 (.mp4) [T] Chpt. 3 slides03 (.pdf), script03 (.R)
04 01/03 9-11 Fib-C Discrete random variables. rec04 (.mp4) [T] Chpts. 4, 9.1, 9.2, 9.4 [R] Chpt. 3 slides04 (.pdf), script04 (.R)
05 02/03 11-13 Fib-C Discrete random variables (continued). rec05 (.mp4)
06 03/03 14-16 Fib-C Recalls: derivatives and integrals. rec06 (.mp4) [P] Chpt. 1-8 slides06 (.pdf), script06 (.R)
07 08/03 9-11 Fib-C R data access and programming. rec07 (.mp4) [R] Chpt. 2.3,2.4 script07 (.zip)
08 09/03 11-13 Fib-C Continuous random variables.rec08 (.mp4) [T] Chpts. 5, 9.2-9.4 [R] Chpt. 3 slides08 (.pdf), script08 (.R)
09 10/03 14-16 Fib-C Expectation and variance. Computations with random variables.rec09 (.mp4) [T] Chpts. 7,8 slides09 (.pdf), script09 (.R)
10 15/03 9-11 Fib-C Expectation and variance. Computations with random variables (continued).rec10 (.mp4)
11 16/03 11-13 Fib-C Moments. Functions of random variables.rec11 (.mp4) [T] Chpts. 9-11 slides11 (.pdf), script11 (.zip)
12 17/03 14-16 Fib-C Simulation. rec12 (.mp4) [T] Chpts. 6.1-6.2 slides12 (.pdf), script12 (.R) script12_sol07 (.R)
13 22/03 9-11 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 23/03 11-13 Fib-C Law of large numbers. The central limit theorem. rec14 (.mp4) [T] Chpts. 13-14 slides14 (.pdf), script14 (.R)
15 24/03 14-16 Fib-C Graphical summaries. Kernel Density Estimation. rec15 (.mp4) [T] Chpt. 15, [R] Chpt. 4 slides15 (.pdf), script15 (.R)
16 29/03 9-11 Fib-C Numerical summaries.rec16 (.mp4) [T] Chpt. 16, [R] Chpt. 4 slides16 (.pdf), script16 (.R)
17 30/03 11-13 Fib-C Data preprocessing in R. Estimators.rec17 (.mp4) [R] Chpt. 10, [T] Chpts. 17.1-17.3script17 (.R), dataprep.R
18 31/03 14-16 Fib-C Unbiased estimators. Efficiency and MSE.rec18 (.mp4) [T] Chpts. 19, 20 slides18 (.pdf), script18 (.R)
19 05/04 9-11 Fib-C Maximum likelihood estimation.rec19 (.mp4) [T] Chpt. 21 sdsln.pdf Chpt. 1 slides19 (.pdf), script19 (.R)
20 06/04 11-13 Fib-C Linear regression. Least squares estimation.rec20 (.mp4) [T] Chpts. 17.4,22 [R] Chpt. 6 sdsln.pdf Chpt. 2 slides20 (.pdf), script20 (.R)
21 12/04 9-11 Fib-C Non-linear, and multiple linear regression.rec21 (.mp4) [R] Chpt. 12.1,13,16.1-16.2 sdsln.pdf Chpt. 2 slides21 (.pdf), script21 (.R)
22 13/04 11-13 Fib-C Issues with linear regression. Logistic regression.rec22 (.mp4) [R] Chpt. 12.1,13,16.1-16.2 slides22 (.pdf), script22 (.zip)
23 14/04 14-16 Fib-C Statistical decision theory.rec23 (.mp4) sdsln.pdf Chpt. 4 slides23 (.pdf), script23 (.R)
24 19/04 9-11 Fib-C Statistical decision theory (continued).rec24 (.mp4)
25 20/04 11-13 Fib-C Statistical decision theory (continued). Project presentation. See student project
26 21/04 14-16 Fib-C Confidence intervals: mean, proportion, linear regression.rec26 (.mp4) [T] Chpts. 23.1,23.2,23.4,24.3,24.4 sdsln.pdf Chpt. 3 slides26 (.pdf), script26 (.R)
27 26/04 9-11 Fib-C Bootstrap and resampling methods.rec27 (.mp4) [T] Chpts. 18.1-18.3,23.3 slides27 (.pdf), script27 (.R)
28 27/04 11-13 Fib-C 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 sdsln.pdf Chpt.3.3 slides29 (.pdf), script29 (.R)
30 3/05 9-11 Fib-C One-sample tests of the mean and application to linear regression (continued).rec30 (.mp4)
31 4/05 11-13 Fib-C Two-sample tests of the mean and applications to classifier comparison.rec31 (.mp4) [T] Chpts. 28, [R] Chpts. 5.3-5.7 slides31 (.pdf), script31 (.R)
32 5/05 14-16 Fib-C Two-sample tests of the mean and applications to classifier comparison (continued).rec32 (.mp4)
33 10/05 9-11 Fib-C Multiple-sample tests of the mean and applications to classifier comparison.rec33 (.mp4) [R] Chpt. 7 slides33 (.pdf), script33 (.R)
34 11/05 11-13 Fib-C Fitting distributions. Testing independence/association.rec34 (.mp4) [R] Chpt. 8 K-S, slides34 (.pdf), script34 (.R)
35 12/05 14-16 Fib-C Fitting distributions. Testing independence/association (continued). Project Q&A.
36 17/05 9-11 Fib-C 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 Room Topic Teaching material
s01 04/05/2022 9-11 Gerace+Teams Bias in statistics and causal reasoning. Speaker: prof. Fabrizia Mealli rec_s01 (.mp4) slides_s01 (.pdf) Optional reading
s02 04/05/2022 11-13 Gerace+Teams Bias in statistics and causal reasoning (continued). Speaker: prof. Fabrizia Mealli rec_s02 (.mp4)

Past years

This course of 9 ECTS replaces an older 6 ECTS version: Statistical Methods for Data Science A.Y. 2020/21 (500PP). The 6 ECTS version is discontinued. Students having the 6 ECTS version in their study plan can still take the 6 ECTS version exam for the A.Y. 2021/22, 2022/23 and 2023/24. However, there will no specific project for the 6 ECTS version.

mds/sds/start.txt · Ultima modifica: 16/06/2023 alle 13:41 (3 mesi fa) da Salvatore Ruggieri