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Masters

Statistics for Data Science

University of Pisa
Pisa , Italy
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Tuition Fee
Not Available
Start Date
Not Available
Medium of studying
On campus
Duration
Not Available
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About Program
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Program Details
Degree
Masters
Major
Data Analysis | Data Science | Statistics
Area of study
Information and Communication Technologies | Mathematics and Statistics
Education type
On campus
Course Language
English
About Program

Program Overview

Statistics for Data Science (628PP) A.Y. 2024/25

Instructors

  • Andrea Pugnana
    • Universitŕ di Pisa
    • Office hours: Tuesdays 11:00 - 12:00 or by appointment, at the Department of Computer Science, room 385/DO
  • Salvatore Ruggieri
    • Universitŕ di Pisa
    • Office hours: Tuesdays 16:00 - 18:00 or by appointment, at the Department of Computer Science, room 321/DO

Hours and Rooms

Day of Week Hour Room
Monday 14:00 - 16:00 Fib-C
Tuesday 14:00 - 16:00 Fib-A1
Thursday 11:00 - 13:00 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.

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.
  • Selected chapters of other books for advanced topics.

Software

  • R
  • R Studio

Preliminary Program and Calendar

  • Preliminary program.
  • Calendar of lessons.

Exams

There are no mid-terms. The exam consists of a written part and an oral part. The written part consists of exercises and questions on the topics of the course. Each question is assigned a grade, summing up to 30 points.


  • Example written texts.
  • Students are admitted to the oral part if they receive a grade of at least 18 points.
  • 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.

Student Project

  • The project replaces the written part of the examination.
  • Project description and rules and Q&A.
  • Recording of project description.

Class Calendar

Lessons will NOT be live-streamed, but recordings of past years are available for non-attending students. | # | Date | Room | Topic | Mandatory Teaching Material | | --- | --- | --- | --- | --- | | 01 | 17/ | Fib-E | Introduction. Probability and independence. | [T] Chpts. 1-3 | | 02 | 17/ | Fib-C | R basics. | [R] Chpts. 1,2.1-2.3 | | ... | ... | ... | ... | ... | | 35 | 15/ | Fib-A1 | Project Q&A. | |


Seminars of Past Years

In some years, speakers were invited to give a seminar on advanced topics. | # | Date | Topic | Teaching Material | | --- | --- | --- | --- | | s01 | 04/05/2022 | Bias in statistics and causal reasoning. | slides_s01 (.pdf) Optional reading | | s02 | 04/05/2022 | Bias in statistics and causal reasoning (continued). | | | s03 | 07/05/2024 and 12/05/2025 | Introduction to causal modeling and reasoning. | slides_s03 (.pdf) |


Past Years

  • Statistics for Data Science A.Y. 2023/24
  • Statistics for Data Science A.Y. 2022/23
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