Bachelor
2025/2026
Introduction to Data Science
Type:
Compulsory course (International Business)
Area of studies:
Management
Delivered by:
Big Data and Information Retrieval School
Where:
Graduate School of Business
When:
3 year, 1 module
Open to:
students of one campus
Instructors:
Бакшук Матвей Вячеславович
Language:
English
ECTS credits:
3
Contact hours:
28
Course Syllabus
Abstract
The course provides students with a basic knowledge of statistics and data analysis techniques. The course consists of three parts. In the first part we will talk about general ideas of statistics and data analysis, mainly discussing descriptive statistics and basic data manipulations. In the second part of the course we will move towards inferential statistics and hypothesis testing. In the third part, we will apply machine learning techniques for data analysis. All the course practice will be conducted in Python. There are 3 credits for this course.
Learning Objectives
- Via this course, students will acquire a solid basis in data manipulation and visualization.
Expected Learning Outcomes
- After this session, students should be able to: - Apply numerical techniques for describing and summarizing data - Identify, compute, and interpret descriptive statistical summary measures - Differentiate between the measures of central tendency, dispersion, and relative standing
Course Contents
- Introduction
- Data Basics
- Graphical Descriptive Techniques
- Numerical Descriptive Techniques
- Data Collection and Sampling Theory
- Probability
- Midterm exam
- Index numbers
- Measures of Central Location
- Descriptive statistics: System of variables
- Analysis of Variance
- Hypothesis Testing Framework
- Discrete Probability Distributions
- Time series
- Continuous Probability Distributions
- Estimation
- Sampling Distributions
- Inference for Numerical Data
- Global Statistical System
- Descriptive statistics: Qualitative and Quantitative Data.
- Regression Analysis
Bibliography
Recommended Core Bibliography
- Elementary statistics : a step by step approach, Bluman, A. G., 2018
- Frederick J Gravetter, Larry B. Wallnau, Lori-Ann B. Forzano, & James E. Witnauer. (2020). Essentials of Statistics for the Behavioral Sciences, Edition 10. Cengage Learning.
- James, G. et al. An introduction to statistical learning. – Springer, 2013. – 426 pp.
Recommended Additional Bibliography
- Boris Mirkin. (2011). Core Concepts in Data Analysis: Summarization, Correlation and Visualization (Vol. 2011). Springer.
- Döbler, M., & Grössmann, T. (2019). Data Visualization with Python : Create an Impact with Meaningful Data Insights Using Interactive and Engaging Visuals. Packt Publishing.
- Frederick J Gravetter, Lori-Ann B. Forzano, & Tim Rakow. (2021). Research Methods For The Behavioural Sciences, Edition 1. Cengage Learning.