Бакалавриат
2025/2026
Введение в Data Science
ID 980847
Статус:
Курс обязательный (Международный бизнес)
Направление:
38.03.02. Менеджмент
Где читается:
Факультет компьютерных наук
Когда читается:
3-й курс, 1 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Бакшук Матвей Вячеславович
Язык:
английский
Кредиты:
3
Контактные часы:
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
Recommended Additional Bibliography
- An Introduction to Statistical Learning, with Applications in Python, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, Springer Nature Switzerland AG 2023, 978-3-031-38747-0, published: 30 June 2023