Statistics and Data Analysis
A.Y. 2023/2024
Learning objectives
The course aim at introducing the fundamentals of descriptive statistics, probability and parametric inferential statistics.
Expected learning outcomes
Students will be able to carry out basic explorative analyses and inferences on datasets, they will know the main probability distributions and will be able to understand statistical analyses conducted by others; moreover, they will know simple methods for the problem of binary classification, and will be able to evaluate their performances. The students will also acquire the fundamental competences for studying more sophisticated techniques for data analysis and data modeling.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
This course provides an introduction to the fundamental concepts of Probability and Inferential Statistics and points to their most relevant applications in Computer Science.
Main topics are:
- Introduction to Statistics and Data Analysis
- Probability
- Random Variables and Probability Distributions
- Mathematical Expectation
- Discrete Probability Distributions
- Continuous Probability Distributions
- Functions of Random Variables
- Fundamental Sampling Distributions and Data Descriptions
- One- and Two-Sample Estimation Problems
- Hypotheses Testing
Main topics are:
- Introduction to Statistics and Data Analysis
- Probability
- Random Variables and Probability Distributions
- Mathematical Expectation
- Discrete Probability Distributions
- Continuous Probability Distributions
- Functions of Random Variables
- Fundamental Sampling Distributions and Data Descriptions
- One- and Two-Sample Estimation Problems
- Hypotheses Testing
Prerequisites for admission
Students shall have passed the exam of "Matematica del continuo"; besides that, having passed the exam of "Matematica del discreto" is strongly suggested.
Teaching methods
Lectures on theoretical foundations and class-based problem solving activities and practice based on stats libraries of Python.
Teaching Resources
Lecture notes, exercises and simulations with theory books, exercises and exams available on the
teaching website MyAriel (https://myariel.unimi.it)
teaching website MyAriel (https://myariel.unimi.it)
Assessment methods and Criteria
Written examination assessing the theory through open questions, the ability to solve exercises concerning main topics of probability and statistics treated along the course and an optional part based on Python. The difficulty of problems is paired to those that have been discussed and solved in lectures. The examination is to be completed in two hours time. The student is allowed to use statistical tables and the compendium of main formulas available for download from the course web site, together with a hand-held calculator. Other books and notes are not allowed.
INF/01 - INFORMATICS - University credits: 6
Practicals: 36 hours
Lessons: 24 hours
Lessons: 24 hours
Professor:
Grossi Giuliano
Educational website(s)
Professor(s)