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mds:smd:2019

Statistical Methods for Data Science A.Y. 2018/19

Instructor

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.

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

Preliminary program and calendar

Project

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. Registration to exams is mandatory: register here

Date Hour Room
13/1/2020 11:00 - 13:00 Fib-C1
5/2/2020 11:00 - 13:00 Fib-C1

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: chi-square, K-S. Fitting power laws. audio-video recording of the lesson K-S script17.R
23 28.05 9:00-11:00 A1 Hypotheses testing: F-test, comparing two samples. [T] Chpts. 28, [R] Chpts. 5.3-5.7 script18.R
24 29.05 11:00-13:00 I-Lab Project tutoring.

Previous years

mds/smd/2019.txt · Ultima modifica: 31/01/2020 alle 10:21 (8 settimane fa) da Salvatore Ruggieri