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

=====Instructors=====

  * **Andrea Pugnana**
    * Università di Pisa
    * [[https://andrepugni.github.io/]]
    * [[andrea.pugnana@di.unipi.it]]   
    * **Office hours:** Tuesdays h 11:00 - 12:00 or by appointment, at the Department of Computer Science, room 385/DO, or via Teams.

  * **Salvatore Ruggieri**
    * Università di Pisa
    * [[http://pages.di.unipi.it/ruggieri/]]
    * [[salvatore.ruggieri@unipi.it]]   
    * **Office hours:** Tuesdays h 16:00 - 18:00 or by appointment, at the Department of Computer Science, room 321/DO, or via Teams.

=====Hours and rooms=====

^  Day of Week  ^  Hour  ^  Room  ^ 
|  Monday  |  14:00 - 16:00  |  Fib-C  | 
|  Tuesday  |  14:00 - 16:00  |  Fib-A1  | 
|  Thursday  |  11:00 - 13:00  |  Fib-A1  | 

A [[https://teams.microsoft.com/l/team/19%3ADJ6an3j-cIPJMvtGKes7oJZEKToOyr48E2290_chI5k1%40thread.tacv2/conversations?groupId=62728cdb-6f88-4134-98ac-3e0282384b1c&tenantId=c7456b31-a220-47f5-be52-473828670aa1|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, but recordings of the lecture or of the previous years will be made available here for non-attending students. 

=====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=====

  * [[https://cran.r-project.org/|R]]
  * [[https://posit.co/download/rstudio-desktop/|R Studio]]

=====Preliminary program and calendar=====

  * [[https://unipi.coursecatalogue.cineca.it/insegnamenti/2024/52766_691581_77399/2022/52766/11357?coorte=2024&schemaid=9090|Preliminary program]].
  * [[https://didattica.di.unipi.it/en/master-programme-in-data-science-and-business-informatics/academic-calendar-2024-2025/|Calendar of lessons]].


=====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: **{{ :mds:sds:sds_sample1.pdf | sample1}}**, **{{ :mds:sds:sds_sample2.pdf | 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!**): [[https://esami.unipi.it/esami2/|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  ^
|  7/11/2025  |  16:00 - 18:00  |  FIB-D1  |   |
|  8/1/2026  |  16:00 - 18:00  |  TBD  |   |
|  29/1/2026  |  11:00 - 13:00  |  TBD  |   |


=====Student project=====

  * The project replaces the written part of the examination
  * {{:mds:sds:s4ds.project.2025.pdf |Project description and rules and Q&A}}.
  * [[http://131.114.72.230/sds/video/s4ds.project.2025.mp4|Recording of project description (.mp4)]]




=====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 [[https://start.unipi.it/en/help-ict/vpn/|unipi.it VPN]]. Alternatively, right click on the link and download the whole file, then watch it locally on your device using e.g. [[http://www.videolan.org/vlc/|VLC media player]].

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.  [[http://131.114.72.230/sds/video/sds01_20220215.mp4|rec01 (.mp4)]] | **[T]** Chpts. 1-3 {{:mds:sds:s4ds01.pdf|slides01 (.pdf)}}|
|02| 17/02 14-16 | Fib-C | R basics. [[http://131.114.72.230/sds/video/sds02_20220217.mp4|rec02 (.mp4)]] | **[R]** Chpts. 1,2.1-2.3 {{:mds:sds:s4ds02.pdf|slides02 (.pdf)}},  {{:mds:sds:s4ds02.r|script02 (.R)}}| 
|03| 20/02 11-13 | Fib-A1 | Bayes' rule and applications. [[http://131.114.72.230/sds/video/sds03_20220218.mp4|rec03 (.mp4)]] | **[T]** Chpt. 3 {{:mds:sds:s4ds03.pdf|slides03 (.pdf)}},  {{:mds:sds:s4ds03.r|script03 (.R)}}| 
|04| 24/02 14-16 | Fib-C | Discrete random variables. [[http://131.114.72.230/sds/video/sds04_20220222.mp4|rec04 (.mp4)]] | **[T]** Chpts. 4, 9.1, 9.2, 9.4 **[R]** Chpt. 3 {{:mds:sds:s4ds04.pdf|slides04 (.pdf)}},  {{:mds:sds:s4ds04.r|script04 (.R)}}| 
|05| 25/02 14-16 | Fib-A1 | Discrete random variables (continued). [[http://131.114.72.230/sds/video/sds05_20220224.mp4|rec05 (.mp4)]] |  |
|06| 27/02 11-13 | Fib-A1 | Recalls: derivatives and integrals. [[http://131.114.72.230/sds/video/sds06_20220225.mp4|rec06 (.mp4)]] | **[P]** Chpt. 1-8 {{:mds:sds:s4ds06.pdf|slides06 (.pdf)}},  {{:mds:sds:s4ds06.r|script06 (.R)}}|
|07| 03/03 14-16 | Fib-C | R data access and programming. [[http://131.114.72.230/sds/video/sds07_20220301.mp4|rec07 (.mp4)]] | **[R]** Chpt. 2.3,2.4 {{:mds:sds:s4ds07.zip|script07 (.zip)}} |
|08| 04/03 14-16 | Fib-A1 | Continuous random variables.[[http://131.114.72.230/sds/video/sds08_20220303.mp4|rec08 (.mp4)]] | **[T]** Chpts. 5, 9.2-9.4 **[R]** Chpt. 3 {{:mds:sds:s4ds08.pdf|slides08 (.pdf)}},  {{:mds:sds:s4ds08.r|script08 (.R)}}| 
|09| 06/03 11-13 | Fib-A1 | Expectation and variance. Computations with random variables.[[http://131.114.72.230/sds/video/sds09_20220304v2.mp4|rec09 (.mp4)]] | **[T]** Chpts. 7,8 {{:mds:sds:s4ds09.pdf|slides09 (.pdf)}},  {{:mds:sds:s4ds09.r|script09 (.R)}}|
|10| 10/03 14-16 | Fib-C | Expectation and variance. Computations with random variables (continued). Moments. Functions of random variables. [[http://131.114.72.230/sds/video/sds10_20220308v3.mp4|rec10 (.mp4)]] | **[T]** Chpts. 9-11 {{:mds:sds:s4ds10.pdf|slides10 (.pdf)}}, {{:mds:sds:s4ds10.zip|script10 (.zip)}} |
|11| 11/03 14-16 | Fib-A1 | Functions of random variables (continued). Distances between distributions.  [[http://131.114.72.230/sds/video/sds11_20240314.mp4|rec11 (.mp4)]] | {{:mds:sds:murphychpt6.pdf|Murphy's book}} Chpt. 6  {{:mds:sds:s4ds11.pdf|slides11 (.pdf)}}, {{:mds:sds:s4ds11.R|script11 (.R)}} |
|12| 13/03 11-13 | Fib-A1 | Simulation. [[http://131.114.72.230/sds/video/sds12_20220311v2.mp4|rec12 (.mp4)]] | **[T]** Chpts. 6.1-6.2 {{:mds:sds:s4ds12.pdf|slides12 (.pdf)}},  {{:mds:sds:s4ds12.r|script12 (.R)}} {{:mds:sds:s4ds12_sol07.r|script12_sol07 (.R)}}|
|13| 17/03 14-16 | Fib-C | Power laws and Zipf's law. [[http://131.114.72.230/sds/video/sds13_20220315.mp4|rec13 (.mp4)]] | [[https://arxiv.org/pdf/cond-mat/0412004.pdf | Newman's paper]] Sect I, II, III(A,B,E,F) {{:mds:sds:s4ds13.pdf|slides13 (.pdf)}},  {{:mds:sds:s4ds13.r|script13 (.R)}}|
|14| 18/03 14-16 | Fib-A1 | Law of large numbers. The central limit theorem. [[http://131.114.72.230/sds/video/sds14_20220317.mp4|rec14 (.mp4)]] | **[T]** Chpts. 13-14  {{:mds:sds:s4ds14.pdf|slides14 (.pdf)}}, {{:mds:sds:s4ds14.R|script14 (.R)}} |
|15| 20/03 11-13 | Fib-A1 | Graphical summaries. Kernel Density Estimation. [[http://131.114.72.230/sds/video/sds15_20220322.mp4|rec15 (.mp4)]] | **[T]** Chpt. 15, **[R]** Chpt. 4  {{:mds:sds:s4ds15.pdf|slides15 (.pdf)}}, {{:mds:sds:s4ds15.r|script15 (.R)}}|
|16| 24/03 14-16 | Fib-C | Numerical summaries.[[http://131.114.72.230/sds/video/sds16_20220324.mp4|rec16 (.mp4)]] | **[T]** Chpt. 16, **[R]** Chpt. 4 {{:mds:sds:s4ds16.pdf|slides16 (.pdf)}}, {{:mds:sds:s4ds16.r|script16 (.R)}} |
|17| 25/03 14-16 | Fib-A1 |Data preprocessing in R. Estimators.[[http://131.114.72.230/sds/video/sds17_20220325.mp4|rec17 (.mp4)]] | **[R]** Chpt. 10, **[T]** Chpts. 17.1-17.3{{:mds:sds:s4ds17.r|script17 (.R)}}, {{ :mds:sds:dataprep.r | dataprep.R}} |
|18| 27/03 11-13 | Fib-A1  | Unbiased estimators. Efficiency and MSE.[[http://131.114.72.230/sds/video/sds18_20220329.mp4|rec18 (.mp4)]] | **[T]** Chpts. 19, 20  {{:mds:sds:s4ds18.pdf|slides18 (.pdf)}}, {{:mds:sds:s4ds18.r|script18 (.R)}} |
|19| 31/03 14-16 | Fib-C | Maximum likelihood estimation.[[http://131.114.72.230/sds/video/sds19_20220331.mp4|rec19 (.mp4)]] | **[T]** Chpt. 21 {{ :mds:sds:s4dsln.pdf |}} Chpt. 1  {{:mds:sds:s4ds19.pdf|slides19 (.pdf)}}, {{:mds:sds:s4ds19.r|script19 (.R)}} |
|20| 01/04 14-16 | Fib-A1  | Linear regression. Least squares estimation.[[http://131.114.72.230/sds/video/sds20_20220405.mp4|rec20 (.mp4)]] | **[T]** Chpts. 17.4,22 **[R]** Chpt. 6 {{ :mds:sds:s4dsln.pdf |}} Chpt. 2 {{:mds:sds:s4ds20.pdf|slides20 (.pdf)}}, {{:mds:sds:s4ds20.r|script20 (.R)}} |
|21| 03/04 11-13 | Fib-A1  | Non-linear, and multiple linear regression.[[http://131.114.72.230/sds/video/sds21_20220407_edited.mp4|rec21 (.mp4)]] | **[R]** Chpt. 12.1,13,16.1-16.2 {{ :mds:sds:s4dsln.pdf |}} Chpt. 2 {{:mds:sds:s4ds21.pdf|slides21 (.pdf)}}, {{:mds:sds:s4ds21.R|script21 (.R)}} |
|22| 07/04 14-16 | Fib-C  | Issues with linear regression. Logistic regression.[[http://131.114.72.230/sds/video/sds22_20220408_edited.mp4|rec22 (.mp4)]] | **[R]** Chpt. 12.1,13,16.1-16.2 {{:mds:sds:s4ds22.pdf|slides22 (.pdf)}}, {{:mds:sds:s4ds22.zip|script22 (.zip)}} |
|23| 08/04 14-16 | Fib-A1  | Statistical decision theory.[[http://131.114.72.230/sds/video/sds23_20220412.mp4|rec23 (.mp4)]] | {{ :mds:sds:s4dsln.pdf |}} Chpt. 4 {{:mds:sds:s4ds23.pdf|slides23 (.pdf)}}, {{:mds:sds:s4ds23.r|script23 (.R)}} |
|24| 10/04 11-13 | Fib-A1  | Statistical decision theory (continued).[[http://131.114.72.230/sds/video/sds24_20220421.mp4|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.[[http://131.114.72.230/sds/video/sds26_20220422.mp4|rec26 (.mp4)]] | **[T]** Chpts. 23.1,23.2,23.4,24.3,24.4 {{ :mds:sds:s4dsln.pdf |}} Chpt. 3 {{:mds:sds:s4ds26.pdf|slides26 (.pdf)}}, {{:mds:sds:s4ds26.r|script26 (.R)}} |
|27| 17/04 11-13 | Fib-A1 | Confidence intervals (continued). Bootstrap and resampling methods.[[http://131.114.72.230/sds/video/sds27_20220426.mp4|rec27 (.mp4)]] | **[T]** Chpts. 18.1-18.3,23.3 {{:mds:sds:s4ds27.pdf|slides27 (.pdf)}}, {{:mds:sds:s4ds27.r|script27 (.R)}} |
|28| 24/04 11-13 | Fib-A1 | Bootstrap and resampling methods (continued).[[http://131.114.72.230/sds/video/sds28_20220428.mp4|rec28 (.mp4)]] |   |
|29| 28/04 14-16 | Fib-C | Hypotheses testing. One-sample tests of the mean and application to linear regression.[[http://131.114.72.230/sds/video/sds29_20220429.mp4|rec29 (.mp4)]]  | **[T]** Chpts. 25,26,27, **[R]** Chpts. 5.1,5.2 {{ :mds:sds:s4dsln.pdf |}} Chpt.3.3 {{:mds:sds:s4ds29.pdf|slides29 (.pdf)}}, {{:mds:sds:s4ds29.r|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. [[http://131.114.72.230/sds/video/sds30_2022mix.mp4|rec30 (.mp4)]] | {{:mds:sds:s4ds30.pdf|slides30 (.pdf)}}, {{:mds:sds:s4ds30.r|script30 (.R)}} |
|31| 05/05 14-16 | Fib-C | Two-sample tests of the mean and applications to classifier comparison. [[http://131.114.72.230/sds/video/sds31_2022mix.mp4|rec31 (.mp4)]] | **[T]** Chpt. 28, **[R]** Chpts. 5.3-5.7 {{:mds:sds:s4ds31.pdf|slides31 (.pdf)}}, {{:mds:sds:s4ds31.r|script31 (.R)}} |
|32| 06/05 14-16 | Fib-A1 | Multiple-sample tests of the mean and applications to classifier comparison.[[http://131.114.72.230/sds/video/sds32_2022mix.mp4|rec32 (.mp4)]] | **[R]** Chpt. 7 {{:mds:sds:s4ds32.pdf|slides32 (.pdf)}}, {{:mds:sds:s4ds32.r|script32 (.R)}} |
|33| 08/05 11-13 | Fib-A1 | Fitting distributions. Testing independence/association.[[http://131.114.72.230/sds/video/sds33_2022mix.mp4|rec33 (.mp4)]] | **[R]** Chpt. 8 {{ :mds:smd:ks.pdf | K-S}}, {{:mds:sds:s4ds33.pdf|slides33 (.pdf)}}, {{:mds:sds:s4ds33.r|script33 (.R)}} |
|s03| 12/05 14-16| Fib-C | //Mandatory seminar:// Introduction to causal modeling and reasoning. Speakers: I. Beretta and M. Cinquini. [[http://131.114.72.230/sds/video/sds_s03_20240507.mp4|rec_s03 (.mp4)]] | {{:mds:sds:s4ds_s03.pdf|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 [[http://131.114.72.230/sds/video/sds_s01_20220504.mp4|rec_s01 (.mp4)]]  | {{:mds:sds:s4ds_s01.pdf|slides_s01 (.pdf)}} [[https://statistics.fas.harvard.edu/files/statistics-2/files/statistical_paradises_and_paradoxes.pdf|Optional reading]] |
|s02| 04/05/2022 | Bias in statistics and causal reasoning (continued). Speaker: prof. Fabrizia Mealli [[http://131.114.72.230/sds/video/sds_s02_20220504.mp4|rec_s02 (.mp4)]]  |   |
|s03| 07/05/2024 and 12/05/2025 | Introduction to causal modeling and reasoning. Speakers: I. Beretta and M. Cinquini. [[http://131.114.72.230/sds/video/sds_s03_20240507.mp4|rec_s03 (.mp4)]] | {{:mds:sds:s4ds_s03.pdf|slides_s03 (.pdf)}}|

=====Past years=====

  * [[mds:sds:2023|Statistics for Data Science A.Y. 2023/24]]
  * [[mds:sds:2022|Statistics for Data Science A.Y. 2022/23]]