|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:
Extra-lessons refreshing such notions may be planned in the first part of the course.
The following are mandatory text books:
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
|1/6/2023||11:00 - 13:00||Fib-M1|
|22/6/2023||11:00 - 13:00||Fib-M1|
|12/7/2023||11:00 - 13:00||Fib-M1|
<! -- | 16/3/2023 | 14:00 - 16:00 | M1 | [[https://didattica.di.unipi.it/en/appelli-straordinari/|Extra-ordinary exam]] | -->
A Teams channel will be used to post news, Q&A, and other material related to the course.
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 unipi.it 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.
|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.|
In some years, speakers were invited to give a seminar on advanced topics. Here it is a list of seminars held in past years.
|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)|
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.