Бакалавриат
2025/2026
Анализ данных
Статус:
Курс по выбору (Иностранные языки и межкультурная коммуникация)
Где читается:
Факультет компьютерных наук
Когда читается:
3-й курс, 1, 2 модуль
Онлайн-часы:
50
Охват аудитории:
для своего кампуса
Преподаватели:
Карпов Максим Евгеньевич
Язык:
английский
Контактные часы:
24
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
- Discrete Probability Distributions
- Continuous Probability Distributions
- Sampling Distributions
- Estimation
- Hypothesis Testing Framework
- Inference for Numerical Data
- Analysis of Variance
- Regression Analysis
- Descriptive statistics: System of variables
- Descriptive statistics: Qualitative and Quantitative Data.
- Measures of Central Location
- Time series
- Index numbers
- Global Statistical System
- Midterm exam
- Social statistics. Demography 1.
- Social statistics. Demography 2.
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.