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====== Statistical Methods for Data Science A.Y. 2018/19 ======
=====Instructor=====
* Salvatore Ruggieri
* Università di Pisa
* http://pages.di.unipi.it/ruggieri/
* ruggieri [at] di [dot] unipi [dot] it
* Office hours
* Tuesday h 14:00 - 17:00, Department of Computer Science, room 321/DO.
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<p style=“color:#FF0000”;>Due to unforeseen problems the lesson of 21 May 2019 is canceled. Sorry for the inconvenience. The lesson will be recovered on 22 May 2019 h. 11-13 in room Seminari Ovest at the Dept of Computer Science.</p>
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=====Classes=====
^ Day of Week ^ Hour ^ Room ^
| Monday | 14:00 - 16:00 | Fib-N1 |
| Tuesday | 9:00 - 11:00 | Fib-A1 |
=====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.
=====Text Books=====
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.
=====Project=====
* Project can be done in groups of at most 3 students.
* Project must be completed by end of July, including oral discussion (on project and all topics of the course).
* Project replace the written exam but students have to register for the written dates in order to fill the student's questionnaire.
* Project presentation slides.
* Google Drive project directory (accessible only to authorized students)
=====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. Oral consists of critical discussion of the written part and of open questions and problem solving on the topics of the course.
Registration to exams is mandatory: register here
^ Date ^ Hour ^ Room ^
| 18/6/2019 | 9:00 - 11:00 | Fib-N1 |
| 2/7/2019 | 16:00 - 18:00 | Fib-L1 |
| 24/7/2019 | 16:00 - 18:00 | Fib-L1 |
=====Class calendar=====
^ ^ Date ^ Room ^ Topic ^ Learning material ^
|1| 18.02 14:00-16:00 | N1 | Introduction. Probability and independence. | [T] Chpts. 1-3 |
|2| 19.02 9:00-11:00 | A1 | R basics. | [R] Chpts. 1,2.1,2.2 slides script1.R |
|3| 26.02 9:00-11:00 | A1 | Discrete random variables. | [T] Chpt. 4 [R] Chpt. 3 script2.R |
|4| 4.03 14:00-16:00 | N1 | Continuous random variables. Simulation. | [T] Chpts. 5, 6.1-6.2 [R] Chpt. 3 script3.R |
|5| 5.03 9:00-11:00 | A1 | Expectation and variance. R data access. | [T] Chpt. 7 [R] Chpt. 2.4 script4.R |
|6| 12.03 9:00-11:00 | A1 | Recalls: derivatives and integrals. | [P] Chpt. 1-8 scriptMath.R |
|7| 13.03 11:00-13:00 | I-Lab | Power laws and Zipf laws. | Newman's paper Sects. I,II,IIIA,IIIB,IIIE,IIIF
script5.R |
|8| 18.03 14:00-16:00 | N1 | Zipf laws. Project presentation. | |
|9| 19.03 9:00-11:00 | A1 | R programming. | [R] Chpt. 2.3 exercise.R script6.R |
|10| 20.03 11:00-13:00 | I-Lab | Computations with random variables. Joint distributions. | [T] Chpts. 8-9 script7.R |
|11| 25.03 14:00-16:00 | N1 | Covariance. Sum of random variables. | [T] Chpts. 10-11 |
|12| 26.03 9:00-11:00 | A1 | Law of large numbers. The central limit theorem. | [T] Chpts. 13-14 script8.R |
|13| 8.04 14:00-16:00 | N1 | Graphical summaries. | [T] Chpt. 15 script9.R |
|14| 9.04 9:00-11:00 | A1 | Numerical summaries. Data preprocessing in R. Q&A on the project. | [T] Chpt. 16, [R] Chpts. 4,10 script10.R, dataprep.R |
|15| 15.04 14:00-16:00 | N1 | Unbiased estimators. Efficiency and MSE | [T] Chpts. 17.1-17.3, 19, 20 script11.R |
|16| 16.04 9:00-11:00 | A1 | Maximum likelihood. Fisher information. | [T] Chpt. 21 notes1.pdf|
|17| 29.04 14:00-16:00 | N1 | Linear, polynomial, and non-linear regressions and least squares. | [T] Chpts. 17.4,22 [R] Chpts. 6,12.1,16.1-16.2 script12.R |
|18| 30.04 9:00-11:00 | A1 | Confidence Intervals: Gaussian, T-student, large sample method. | [T] Chpts. 23.1,23.2,23.4,24.3,24.4 script13.R |
|19| 7.05 9:00-11:00 | A1 | Empirical bootstrap. Application to confidence intervals. | [T] Chpts. 18.1,18.2,23.3 script14.R |
|20| 8.05 11:00-13:00 | I-Lab | Parametric bootstrap. Hypotheses testing. | [T] Chpts. 18.3,25 script15.R |
|21| 20.05 14:00-16:00 | N1 | One-sample t-test and application to linear regression. | [T] Chpts. 26-27, [R] Chpts. 5.1,5.2 script16.R |
|22| 22.05 11:00-13:00 | Sem.Ovest | Goodness of fit: K-S, chi-square. Fitting power laws. | K-S script17.R |
| | 27.05 14:00-16:00 | N1 | NO LESSON ON THIS DATE (EU ELECTIONS) | |
|23| 28.05 9:00-11:00 | A1 | … | … |
|24| 29.05 11:00-13:00 | I-Lab | Project tutoring. | |
=====Previous years=====
* Statistical Methods for Data Science A.Y. 2017/18
* Statistical Methods for Data Science A.Y. 2016/17