Python Programming and Data Processing

2025/2026

Academic Year

ENG

Instruction in English

4

ECTS credits

Delivered at:

International College of Economics and Finance

Course type:

Elective course

When:

1 year, 2 semester

Instructors

Акиншин Анатолий Анатольевич

Bessonova, Irina

Kirianov, Vladislav

Рафаелян Георгий Робертович

Course Syllabus

Abstract

a.Pre-requisitesPrimary school knowledge in computer scienceb.AbstractIn the modern, highly technological world computer skills have become essential for specialists in all fields. Programming in particular has gone beyond its traditional borders of being just a prerogative of IT specialists, turning into an element of computer literacy. In the last 10 years programming languages and tools have evolved significantly, which now enables people even without a solid technical background to successfully master related skills.The main part of the course is focused on programming and data processing techniques using the Python language. It is complemented by a blended part on Excel, featuring data processing techniques that can be useful in later ICEF courses and economics-related applications.

Learning Objectives

• Although based on a particular toolset (Python), the course aims to give a broad perspective of what can be done using a modern general-purpose programming language.
• On course completion, students should be: • able to work with information: to find, evaluate and use information from various sources, necessary to solve scientific and professional problems (including those on the basis of a systematic approach)
• • capable of working in a team
• • able to solve analytical and research problems with modern technical means and information technology;
• • able to use modern technical means and information technologies for solving communicative tasks;

Expected Learning Outcomes

• Work with basic data structures of programming languages
• Apply several techniques of automated data acquisition including API queries, methods of processing structured and unstructured data

Course Contents

• Topic 1. Python Language Basic
• Topic 2. Logical data type and conditional statements
• Topic 3. For Loop and While loop
• Topic 4. Data structures
• Topic 5. Methods
• Topic 6. Nested data structures. Sorting
• Topic 7. Functions
• Topic 8. Text files and tables

Assessment Elements

• Exam: In-class assignment
  In order to get a passing grade for the course, the student must sit (all parts) of the examination.
• Class Activity
• Home assignments

Interim Assessment

• 2025/2026 2nd semester
  0.7 * Exam: In-class assignment + 0.15 * Class Activity + 0.15 * Home assignments

Bibliography

Recommended Core Bibliography

• Learning Python : [covers Python 2.5], Lutz, M., 2008
• Python и анализ данных : первичная обработка данных с применением pandas, NumPy и Jupiter, Маккинни, У., 2023
• Python и анализ данных, Маккинни, У., 2015
• Маккинни, У. Python и анализ данных / У. Маккинни , перевод с английского А. А. Слинкина. — 2-ое изд., испр. и доп. — Москва : ДМК Пресс, 2020. — 540 с. — ISBN 978-5-97060-590-5. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/131721 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.
• Маккинни, У. Python и анализ данных. Первичная обработка данных с применением pandas, NumPy и Jupiter : справочник / У. Маккинни , перевод с английского А. А. Слинкина. — 3-е изд. — Москва : ДМК Пресс, 2023. — 536 с. — ISBN 978-5-93700-174-0. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/348086 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.

Recommended Additional Bibliography

• Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017

Authors

• Bessonova Irina Anatolevna
• Akinshin Anatolii Anatolevich