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mds:smd:start

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.

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

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. 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: chi-square, K-S. Fitting power laws. audio-video recording of the lesson 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

mds/smd/start.txt · Ultima modifica: 22/05/2019 alle 12:47 (36 ore fa) da Salvatore Ruggieri