Bachelor
2025/2026
Mathematical Statistics
Type:
Compulsory course (Data Science and Business Analytics)
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
2 year, 3, 4 module
Online hours:
10
Open to:
students of one campus
Language:
English
ECTS credits:
4
Contact hours:
88
Course Syllabus
Abstract
This course is designed to introduce students to the basic ideas and methods of statistics as well as the application of statistical methods in econometrics, data science and the social sciences. This course provides some of the analytical tools that are required by advanced courses of data science and machine learning. This course provides students with experience in the methods and applications of statistics to a wide range of theoretical and practical situations. The course is taught in English. Prerequisites are Calculus (functions of several variables, partial derivatives, integrals, maximum of functions), and elements of Linear algebra (vectors, matrices, linear equations).
Learning Objectives
- This course provides a rigorous, theory-grounded exposition of statistical inference, building upon the probabilistic foundations established in prerequisite studies. It aims to equip students with the formal principles and analytical techniques for drawing conclusions from data, including parameter estimation, hypothesis testing, and regression modeling. The curriculum emphasizes both the mathematical derivations underlying these methods and their practical application in data-driven decision-making, risk assessment, and predictive analytics.
Expected Learning Outcomes
- Formulate and conduct statistical hypothesis tests for one-sample, two-sample, and analysis of variance (ANOVA) scenarios, interpreting results in context.
- Derive and evaluate point estimators using methods of moments and maximum likelihood estimation, and analyze their properties through concepts of convergence (e.g., consistency, asymptotic distributions).
- Construct and analyze simple linear regression models, interpreting parameters, assessing fit, and understanding the underlying assumptions.
- Apply and interpret non-parametric statistical tests and Chi-square tests for goodness-of-fit and independence.
Course Contents
- Foundations of Statistical Inference: Hypothesis Testing
- Comparative Inference: Two-Sample Tests
- The Chi-Square Distribution: Tests for Categorical Data
- Analysis of Variance and The F-Distribution
- Non-Parametric Statistics
- Introduction to Regression: Simple Linear Model
- Data Collection and Representation
Interim Assessment
- 2025/2026 4th module0.5 * Exam + 0.2 * Homework module 3 + 0.1 * Homework module 4 + 0.2 * Spring Midterm
