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====== Statistical Methods for Data Science A.Y. 2020/21 ======
=====Instructor=====
* Salvatore Ruggieri
* Università di Pisa
* http://pages.di.unipi.it/ruggieri/
* salvatore [dot] ruggieri [at] unipi [dot] it
* Office hours
* Tuesday h 14:00 - 17:00, Department of Computer Science, room 321/DO.
* Office hours only on appointment via Teams/Skype. Skype contact: salvatore.ruggieri
=====Classes=====
Dates are preliminary.
^ Day of Week ^ Hour ^ Room ^
| Tuesday | 16:00 - 18:00 | Teams Virtual Room |
| Wednesday| 9:00 - 11:00 | Teams Virtual Room |
=====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.
=====Software=====
* R
* R Studio
=====Preliminary program and calendar=====
* Preliminary program.
* Calendar of lessons.
=====Student project=====
* The project can be done in groups of at most 4 students.
* The project must be delivered (report + code) by end of July.
* The oral discussion must be done by the September session, and it will cover both the project and all topics of the course.
* The project replaces the written exam but students have to register for the written dates in order to fill the student's questionnaire.
* Groups ready to discuss send the project to the teacher plus availability time slots for oral discussion.
* * Project presentation slides and project info audio-video (.mp4).
=====Written exam=====
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. Written exam consists of open questions and exercises. Example written texts: sample1, sample2. Oral consists of critical discussion of the written part and of open questions and problem solving on the topics of the course.
Online exams: during the COVID-19 restrictions, the written part and the oral part will be online. For the written part, students will connect to a reserved Teams virtual room and will activate both microphone and web-cam. The text will be shared in the virtual room chat. Solutions will be written on sheet of papers. Each sheet will include name, surname, student id, and it will be signed. A photo of the sheets will be delivered to ruggieri [at] di [dot] unipi [dot] it at the end of the written part.
Registration to exams is mandatory (beware of the registration deadline!): register here
^ Date ^ Hour ^ Room ^ Notes ^
| 15/04/2021 | 16:00 - 18:00 | Online exam | Restrictions apply |
| 04/06/2021 | 16:00 - 18:00 | Online exam | |
| 25/06/2021 | 16:00 - 18:00 | Online exam | |
| 16/07/2021 | 16:00 - 18:00 | Online exam | |
=====Class calendar=====
^ ^ Date ^ Room ^ Topic ^ Learning material ^
|01| 16.02 16:00-18:00 | Teams | Introduction. Probability and independence. rec01 audio-video (.mp4)| [T] Chpts. 1-3 slides01 (.pdf)|
|02| 23.02 16:00-18:00 | Teams | R basics. rec02 audio-video (.mp4)| [R] Chpts. 1,2.1,2.2 slides02 (.pdf), script02 (.R)|
|03| 24.02 9:00-11:00 | Teams | Discrete random variables. rec03 audio-video (.mp4)| [T] Chpt. 4 [R] Chpt. 3 slides03 (.pdf), script03 (.R)|
|04| 02.03 16:00-18:00 | Teams | Recalls: derivatives and integrals. rec04 audio-video (.mp4)| [P] Chpt. 1-8 slides04 (.pdf), script04 (.R)|
|05| 03.03 9:00-11:00 | Teams | Continuous random variables. Simulation. rec05 audio-video (.mp4)| [T] Chpts. 5, 6.1-6.2 [R] Chpt. 3 slides05 (.pdf), script05 (.R)|
|06| 09.03 16:00-18:00 | Teams | Expectation and variance. Computations with random variables. rec06 audio-video (.mp4)| [T] Chpts. 7,8 slides06 (.pdf), script06 (.R)|
|07| 10.03 9:00-11:00 | Teams | R data access and programming. rec07 audio-video (.mp4)| [R] Chpt. 2.3,2.4 script07 (.zip) |
|08| 16.03 16:00-18:00 | Teams | Power laws and Zipf laws. rec08 audio-video (.mp4)| Newman's paper Sect I, II, III(A,B,E,F) slides08 (.pdf), script08 (.zip) |
|09| 17.03 9:00-11:00 | Teams | Moments, joint distributions, sum of random variables. rec09 audio-video (.mp4)| [T] Chpts. 9-11 slides09 (.pdf), script09 (.zip) |
|10| 23.03 16:00-18:00 | Teams | Law of large numbers. The central limit theorem. rec10 audio-video (.mp4)| [T] Chpts. 13-14 slides10 (.pdf), script10 (.R)|
|11| 24.03 9:00-11:00 | Teams | Project presentation. Graphical summaries. rec11 audio-video (.mp4)| [T] Chpt. 15 slides11 (.pdf), script11 (.R)|
|12| 30.03 16:00-18:00 | Teams | Numerical summaries. Data preprocessing in R. rec12 audio-video (.mp4)| [T] Chpt. 16, [R] Chpts. 4,10 slides12 (.pdf), script12 (.R), dataprep.R |
|XX| 31.03 9:00-11:00 | | No lesson on this date. Students work on the project on their own. | |
|13| 7.04 9:00-11:00 | Teams | Unbiased estimators. Efficiency and MSE. rec13 audio-video (.mp4)| [T] Chpts. 17.1-17.3, 19, 20 slides13 (.pdf), script13 (.R) |
|14| 13.04 16:00-18:00 | Teams | Maximum likelihood estimation. | [T] Chpt. 21 notes1.pdf slides14 (.pdf), script14 (.R) |
|15| 14.04 9:00-11:00 | Teams | Linear regression. Least squares estimation. | [T] Chpts. 17.4,22 [R] Chpts. 6,12.1 slides15 (.pdf), script15 (.R) |
|16| 20.04 16:00-18:00 | Teams | … | … |
=====Last year class calendar=====
^ ^ Date ^ Room ^ Topic ^ Learning material ^
|17| 28.04 16:00-18:00 | Distance-learning | Multiple, non-linear, and logistic regression. rec13 audio-video (.flv) | [R] Chpt. 13,16.1-16.2 notes2.pdf script15.R |
|18| 29.04 9:00-11:00 | Distance-learning | Confidence intervals: Gaussian, T-student, large sample method. rec14 audio-video (.flv) | [T] Chpts. 23.1,23.2,23.4, 24.3,24.4 script16.R |
|19| 05.05 16:00-18:00 | Distance-learning | Confidence intervals in linear regression. Empirical bootstrap. Application to confidence intervals. rec15 audio-video (.flv) | [T] Chpts. 18.1,18.2,23.3 notes2.pdf script17.R |
|20| 06.05 9:00-11:00 | Distance-learning | Parametric bootstrap. Hypotheses testing. rec16 audio-video (.flv) | [T] Chpts. 18.3,25 script18.R |
|21| 12.05 16:00-18:00 | Distance-learning | One-sample t-test and application to linear regression. rec17 audio-video (.flv) | [T] Chpts. 26-27, [R] Chpts. 5.1,5.2 notes2.pdf script19.R |
|22| 13.05 9:00-11:00 | Distance-learning | Goodness of fit: chi-square, K-S. Fitting power laws. rec18 audio-video (.flv) | K-S script20.R |
|23| 20.05 9:00-11:00 | Distance-learning| Hypotheses testing: F-test, comparing two samples. rec19 audio-video (.flv) | [T] Chpts. 28, [R] Chpts. 5.3-5.7 script21.R |
|24| 27.05 9:00-11:00 | Distance-learning | Project tutoring. rec20 audio-video (.flv) | |
=====Previous years=====
* Statistical Methods for Data Science A.Y. 2019/20
* Statistical Methods for Data Science A.Y. 2018/19
* Statistical Methods for Data Science A.Y. 2017/18
* Statistical Methods for Data Science A.Y. 2016/17