Statistics and Data Analysis
A.Y. 2024/2025
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 cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
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
- Simple regression models
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
- Simple regression models
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
The exam consists of a written test, which allows for a grade up to 30/30 cum laude, structured in open-ended theory questions and free-response exercises, having similar content and difficulty to those covered during classroom exercises. A third part of the exam, is optional and is based on the Python language in which knowledge of specific libraries for numerical computation of simple probability and statistics problems, and simulations of simple probabilistic models, in a form similar to what was shown in class, is required. The allotted time is 2 hours. During the written test, consultation of the form and statistical tables made available by the lecturer on the Ariel website is allowed, as well as the use of the calculator. Consultation of other texts or notes is not allowed.
INF/01 - INFORMATICS - University credits: 6
Practicals: 36 hours
Lessons: 24 hours
Lessons: 24 hours
Professor:
Grossi Giuliano
Shifts:
Turno
Professor:
Grossi GiulianoProfessor(s)