Основы программирования на Python

Бакалавриат 2022/2023

Статус: Курс обязательный (Международная программа по мировой политике)

Направление: 41.03.06. Публичная политика и социальные науки

Кто читает: Факультет мировой экономики и мировой политики

Где читается: Факультет мировой экономики и мировой политики

Когда читается: 2-й курс, 3, 4 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Карпов Максим Евгеньевич, Краснокутская Александра Львовна, Перевышина Татьяна Олеговна, Тарасенко Георгий Константинович

Язык: английский

Кредиты: 4

Контактные часы: 32

Full Syllabus Ask 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

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

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

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

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%.
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%.
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%.
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%.
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%.
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

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

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
Королева Анастасия Романовна

Course Syllabus