Delivered at:: Department of Economics

Course type:: Compulsory course

When:: 2 year, 2, 3 module

Instructors

Гупенко Александр Александрович

Manichev, Gennadii

Петрыкин Матвей Борисович

Рассадина Дарья Олеговна

Full Syllabus Ask Question

Abstract

Python is an interpreted high-level general-purpose programming language. It is a simple language for beginners to learn, though it is powerful enough for writing large applications. This 2-module course is an introduction to the Python programming language. The average time to complete this course depends on the student’s background. To complete the course, students are supposed to have mathematical skills at the high school level. Students’ academic performance is evaluated using programming assignments in the form of weekly homeworks and graded seminars and written assignments in the form of one midterm and a final exam. The examples and problems used in this course cover basic syntax rules, file input and output, creation of custom user functions. This course does not provide lectures, all theoretical material is given during seminars.

Learning Objectives

Understand basic syntax rules, data types, built-in constructions
Use Python to create own functions and work with files
Form a basis for further use of Python as a means of data analysis
Master the common data science Python libraries: pandas, requests

Expected Learning Outcomes

Student can explain basic principles of Python programming language.
Student can write scripts for automating processe.
Student can read and understand simple scripts
Student can find syntax and logical errors in scripts
Student can perform basic data analysis using Python scripts

Course Contents

Topic 1. Introduction
Topic 2. Conditions and Boolean Algebra
Topic 3. Ordered collections — part 1
Topic 4. Ordered collections — part 2
Topic 5. The WHILE loop
Topic 6. The FOR loop
Topic 7. Loops, nested data structures, sorting
Topic 8. Unordered collections
Topic 9. Functions — part 1
Topic 10. Functions — part 2
Topic 11. Working with files
Topic 12. Pandas — part 1
Topic 13. Pandas — part 2
Topic 14. JSON, requests
Topic 15. Recap

Assessment Elements

Midterm
Midterm covers all topics from the Syllabus (the first module material). Midterm consists of several paper-based tasks. The midterm is open-book: any amount of paper-based materials is allowed (printed or hand-written). During the midterm cheating is strongly prohibited: no additional electronic resources/devices; no talking to peers. In case of the rules violation the student gets zero points for the midterm. Duration: 2 academic hours (1h 20m). The maximum grade is 10.
Homework
Given out after corresponding seminars. Students have one calendar week to complete the assignment. Each Homework cannot be retaken regardless of the reason for absence. The maximum grade for each Homework is 10, including tasks that check an outstanding student performance. Homeworks are implemented in the SmartLMS system. The grade is published no later than 5 workdays after the deadline.
Graded Seminars
Given out during seminars. Students individually complete the assignment during the seminar, and submit it no later than the end of the class. The format of the control element is offline. If the student is not present at the class in person during the control element, but has completed an attempt to pass the control element, a score of "0" is given for the corresponding control element. Each seminar cannot be retaken regardless of the reason for absence. The maximum grade for each graded seminar is 10, including tasks that check an outstanding student performance. Realized through SmartLMS. The grade is published no later than 5 workdays after the deadline. The list of sources allowed for use: • Online translators and dictionaries (with the exception of using built-in image translation functions and built-in chatbot modules with generative artificial intelligence, large language models, etc.) • Searching for information through search engines and usage of specialized websites (including Python documentation and libraries studied in the discipline) • Printed and handwritten notes or copies of lectures and seminars • Lecture files in .ipynb format List of prohibited sources: • Opening and/or usage of messengers, regardless of device and purpose • Opening and/or usage of chatbots with generative artificial intelligence, deep thinking etc. • Presence and/or usage of smartphones (in accordance with clauses 3.5.4.1. and 3.5.4.6. of the Student Internal Regulations at National Research University Higher School of Economics) • Other sources not allowed above and leading to violations of student's duties under sub-paragraphs 3.5 of the Student Internal Regulations at National Research University Higher School of Economics
Exam
Exam covers all topics from the Syllabus. Exam consists of several paper-based tasks. The exam is open-book: any amount of paper-based materials is allowed (printed or hand-written). During the exam cheating is strongly prohibited: no additional electronic resources/devices; no talking to peers. In case of the rules violation the student gets zero points for the exam. Duration: 2 academic hours (1h 20m). Maximum grade is 10.

Interim Assessment

2025/2026 3rd module
Total = 0.2 * Midterm + 0.01 * HW_1 + 0.01 * HW_2 + 0.01 * HW_3 + 0.01 * HW_4 + 0.01 * HW_5 + 0.01 * HW_6 + 0.01 * HW_7 + 0.01 * HW_8 + 0.01 * HW_9 + 0.01 * HW_10 + 0.1 * S_1 + 0.1 * S_2 + 0.5 * Exam; Where: - Midterm — the score received for the midterm (maximum 10 points); - HW_n — the score received for the homework of the corresponding week of the course (maximum 10 points for each); - S_1 is the arithmetic mean of the grades received for the evaluated seminars during the second module minus one lowest grade (maximum 10 points); - S_2 is the arithmetic mean of the grades received for the evaluated seminars during the third module minus one lowest grade (maximum 10 points); - Exam — grade obtained for the final exam (maximum 10 points).

Bibliography

Recommended Core Bibliography

9781491912140 - Vanderplas, Jacob T. - Python Data Science Handbook : Essential Tools for Working with Data - 2016 - O'Reilly Media - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1425081 - nlebk - 1425081

Recommended Additional Bibliography

9781785284571 - Romano, Fabrizio - Learning Python - 2015 - Packt Publishing - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1133614 - nlebk - 1133614

Authors

Volkova Iuliia Mikhailovna
BRODSKAYA NATALYA NIKOLAEVNA
Manichev Gennadii Gennadevich

Bachelor’s Programme 'International Bachelor's in Business and Economics'

Contacts:

Introduction to Python for Data Science