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Course Syllabus

Bachelor 2022/2023

Introduction into Python

Type: Compulsory course (International Bachelor's Programme in World Politics)
Area of studies: Public Policy and Social Sciences
Delivered by: Faculty of World Economy and International Affairs
Where: Faculty of World Economy and International Affairs
When: 2 year, 3, 4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Maksim Karpov, Alexandra Krasnokutskaya, Tatiana Perevyshina, Georgy K. Tarasenko
Language: English
ECTS credits: 4
Contact hours: 32

Course Syllabus

Full Syllabus

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 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 (particularities will be communicated at least one week in advance). Question types might be a multiple-choice or a short answer. The sum of all grades will count towards the final grade with a weight of 15%.
  • blocking Exam (Project Defence)
    At the end of the course, students will have to participate in the group project. Groups will consist of 2 students. They will have to gather data from the Internet via Python, write it to a file and then calculate some statistics. Students will have to submit their code and project description during the exam week and then defend it on the day of the exam. Students will be asked questions about the code they have submitted. The total grade will consist of a grade for the written part and a grade for the Q&A. All students in the group receive the Q&A grade based on the performance of the weakest student in the group (e.g. if one of the participants cannot answer any question, then the entire group gets a 0 for a defence part). Particularities of the project will be announced in the second part of the 4th module. The project grade will count towards the final grade with a weight of 10%.
  • non-blocking Final Test
    There will be a midterm test at the end of the third module and the test in the beginning of June. Both will be conducted via SmartLMS platform. The tests will consist of a quiz and a few problems. The midterm test will cover topics up to the Review I. The test will cover the entire course up to Review 2. For each test, a Mock Test will be published a few weeks in advance. If the test were to be conducted online the students will have to share their screens and turn on your camera during it. Failure to do so will result in grade 0 for the assignment. The grade for each test is from 0 to 10. The average of two tests will count towards the final grade with a weight of 30%.
  • non-blocking Take-home Problem sets
    There will be ten homework assignments with Python problems sets. Solutions should be submitted via SmartLMS platform and graded automatically. Each assignment will have its own deadline and will be graded from 0 to 10 points. The mean of all assignments will count towards the final grade with a weight of 15%.
  • non-blocking Midterm test
    There will be a midterm test at the end of the third module and the test in the beginning of June. Both will be conducted via SmartLMS platform. The tests will consist of a quiz and a few problems. The midterm test will cover topics up to the Review I. The test will cover the entire course up to Review 2. For each test, a Mock Test will be published a few weeks in advance. If the test were to be conducted online the students will have to share their screens and turn on your camera during it. Failure to do so will result in grade 0 for the assignment. The grade for each test is from 0 to 10. The average of two tests will count towards the final grade with a weight of 30%.
  • non-blocking Work in Class
    There will be mini-tasks during the seminars. The student needs to continue the snippet of code on a given task or answer the question. Semi-points and no points are allowed to assess the students' performance. The total grade will be normalised from the maximum in the group.
Interim Assessment

Interim Assessment

  • 2022/2023 4th module
    0.25 * Midterm test + 0.15 * Work in Class + 0.15 * Take-home Problem sets + 0 * Final Test + 0.3 * Exam (Project Defence) + 0.15 * Quizzes
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