Бакалавриат
2026/2027
Анализ данных и методы искусственного интеллекта в задачах обработки текстов
ID 1197736
Статус:
Курс по выбору (Иностранные языки и межкультурная коммуникация)
Где читается:
Факультет компьютерных наук
Когда читается:
3-й курс, 2, 3 модуль
Онлайн-часы:
50
Охват аудитории:
для своего кампуса
Преподаватели:
Чернова Стефания Вячеславовна
Язык:
английский
Кредиты:
3
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
- Analysis of Variance
- Regression Analysis
- Descriptive statistics: System of variables
- Descriptive statistics: Qualitative and Quantitative Data.
Interim Assessment
- 2026/2027 3rd moduleMIN(8; 0.25 × Class participation + 0.25 × Independent Work + 0.25 × Tests + 0.25 × Written test)
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