Бакалавриат 2025/2026

Python для анализа данных

Статус: Курс обязательный (Прикладной анализ данных)

Кто читает: Департамент больших данных и информационного поиска

Где читается: Факультет компьютерных наук

Когда читается: 1-й курс, 4 модуль

Охват аудитории: для своего кампуса

Преподаватели: Вознесенская Тамара Васильевна, Довгополый Иоанн Алексеевич, Копылов Иван Станиславович, Павлеева Мария Валерьевна, Пронина Анна Сергеевна, Рудаков Кирилл Александрович, Ярцев Михаил Сергеевич

Язык: английский

Кредиты: 4

Контактные часы: 60

Course Syllabus

Abstract

The course is designed to provide students with essential knowledge of the Python programming language, along with skills in data manipulation, visualization, and exploratory data analysis. Students will learn to utilize popular libraries such as Pandas, NumPy, Matplotlib and others to derive insights from data. The lectures and practical classes are closely interrelated. The lectures are primarily intended to introduce new topics and focus on theoretical aspects, whereas the practical classes are aimed at solving specific problems by writing programs in Python.

Learning Objectives

• In this course, students will learn programming in Python.
• One of the objectives of the course is to introduce students to the Python ecosystem and help them take their first steps in data analysis.
• During this course the students will develop algorithmic thinking as well.
• The students will study approaches and toolkits for the development of Python applications.

Expected Learning Outcomes

• Students will learn to select the most appropriate toolset for app development.
• Students will acquire skills in Python programming.
• Students will learn basic exploratory data analysis techniques.
• After completing this course, students will be able to conduct simple data analysis.

Course Contents

• Python Fundamentals
• Functions
• OOP in Python
• NumPy
• Pandas
• Data Visualization
• Introduction to Statistical Analysis
• Handling Dirty Data
• Regular Expressions (re module)
• Introduction to application development tools

Assessment Elements

• HW (contests)
  Home work in the form of solving contests.
• Midterm
  Control work in the form of a Contest.
• Activity
  Work in class.
• Exam (Project defence)
  Project defence.
• Seminar attendance
  Seminar attendance as a percentage of classes attended divided by 10.

Interim Assessment

• 2025/2026 4th module
  0.1 * HW (contests) + 0.1 * Seminar attendance + 0.15 * Activity + 0.25 * Midterm + 0.4 * Exam (Project defence)

Bibliography

Recommended Core Bibliography

• Schneider, D. I. (2016). An Introduction to Programming Using Python, Global Edition: Vol. Global edition. Pearson.

Recommended Additional Bibliography

• Pilgrim, M. (2009). Dive Into Python 3. New York: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=326208

Authors

• Voznesenskaia Tamara Vasilevna