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Course Syllabus

Bachelor 2024/2025

Introduction into Python

Type: Compulsory course (International Program 'International Relations and Global Studies')
Area of studies: International Relations
Delivered by: Faculty of World Economy and International Affairs
Where: Faculty of World Economy and International Affairs
When: 2 year, 3 module
Mode of studies: offline
Open to: students of one campus
Language: English
ECTS credits: 4

Course Syllabus

Full SyllabusAsk Question

Abstract

The Python programming language is one of the easiest to learn and popular programming languages. The aim of the course is to learn the basic constructs of the Python language, which will be useful in solving a wide range of problems - from data analysis to the development of new software products. The course provides the necessary foundation for mastering more specialized areas of the Python language, such as machine learning, statistical data processing, data visualization, and many others. The course offers a large number of programming tasks, arranged in order of increasing complexity, which allows you to consolidate the studied material in practice.
Learning Objectives

Learning Objectives

  • Students achieve excellent results by doing a considerable amount of practical exercises both in class and at home and taking part in group projects
Expected Learning Outcomes

Expected Learning Outcomes

  • Know and differentiate basic Python data types. Choose the correct data types based on the problem in hand
  • Know and understand basic Python syntax
  • Load and use additional Python modules
  • Use Python for routine tasks automation
  • Use Python to read and write structured and unstructured files
  • Write their own functions
  • Use Jupyter Notebook or similar program
Course Contents

Course Contents

  • Intro and logistics. Anaconda and Jupyter Notebook. First program.
  • Data types: integers and strings. Input and output. Strings formatting.
  • Data types: floating-point numbers and boolean. Logical operators. Conditionals.
  • While loop.
  • Data types: lists and tuples. For loop.
  • For Loop (2nd Part)
  • Methods I (Strings)
  • Methods II (Lists)
  • Review I.
  • MIDTERM
  • Data types: sets and dictionaries.
  • Nested Structures
  • Functions
  • Working with text files in Python.
  • Review II
  • TEST
Assessment Elements

Assessment Elements

  • non-blocking Seminar Participation
    To get full mark for the participation, a student needs to actively participate in the class discussions, to demonstrate familiarity with assigned readings and lecture material, to comment on a home assignment, including being prepared to answer the questions that the instructor may pose.
  • non-blocking In-class Assignments
    There will be in-class assignments with Python problems sets. Solutions should be submitted via Smart LMS platform and graded automatically.
  • non-blocking Quizzes
    There will be short in-class quizzes distributed throughout the course. Each quiz will take 5-10 minutes and will cover the material of the previous weeks. Question types might be a multiple-choice or a short answer.
  • non-blocking Control Work
    The control work will be conducted via Smart LMS platform. The test will consist of 10 coding problems. The control work will cover topics up to the Nested Structures. A Mock Test will be published a few weeks in advance. The grade for the test is from 0 to 10.
  • blocking Exam
    The oral survey of studied topics of programming in Python until working with files included.
Interim Assessment

Interim Assessment

  • 2024/2025 3rd module
    min(0.3 * Seminar Participation + 0.1 * In-class Assignments + 0.1 * Quizzes + 0.2 * Control Work + 0.3 * Exam, 8). Remark: In accordance with the Regulations for Interim and Ongoing Assessments of Students at HSE University, grades awarded on the basis of interim assessment outcomes of the discipline-prerequisites for the independent exam on digital competency may not exceed 8 points.
Bibliography

Bibliography

Recommended Core Bibliography

  • Lutz, M. (2008). Learning Python (Vol. 3rd ed). Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=415392
  • Python for data analysis : data wrangling with pandas, numPy, and IPhython, Mckinney, W., 2017

Recommended Additional Bibliography

  • Taieb, D. (2018). Data Analysis with Python : A Modern Approach. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1993344

Authors

  • Karpov Maksim Evgenevich
  • Zakharova Elizaveta Sergeevna
  • Sanochkin Yuriy Ilich