Introduction to Python for Data Science

2024/2025

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Департамент экономики

Статус:

Курс обязательный

Когда читается:

2-й курс, 1, 2 модуль

Преподаватели

Волкова Юлия Михайловна

Ефимов Константин Дмитриевич

Маничев Геннадий Геннадьевич

Омеляш Ксения Васильевна

Юртаева Яна Николаевна

Course Syllabus

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 create scripts for automating processes
• Students can read and understand simple scripts

Course Contents

• Topic 1. Basic of Python programming
• Topic 2. Boolean data type and IF conditions
• Topic 3. Strings and string methods
• Topic 4. Lists, Tuples and its methods
• Topic 5. WHILE loops
• Topic 6. FOR loops
• Topic 7. Text files
• Topic 8. Dictionaries, sets
• Topic 9. Nested data structures. Sorting
• Topic 10. Functions
• Topic 11. Functions 2
• Topic 12. Recap

Assessment Elements

• Regular tests
  Paper-based, closed book regular tests are conducted every week at the beginning of the seminar. The duration of each test is approximately 5-10 min. The grade is announced no later than a day before the next class. Cannot be retaken
• Homeworks
  Given out every week 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. Realized through Smart LMS platform, moodle sandbox environment. The grade is published no later than 5 workdays after the deadline.
• Graded Seminar
  Graded Seminar tasks covers all topics from seminars. Smart LMS platform, moodle sandbox environment. All questions are divided into three sections: 1. Practical section I, where students are expected to solve simple tasks with the solution that takes only a few lines; 2. Practical section II, where students are expected to solve more complex tasks that require writing code in Python; 3. Practical section III, where students will have to solve more challenging tasks compared to those in Practical Section II. The length is 2 academic hours (1h 20m). Maximum sum of points for the Graded Seminar is 10. Graded Seminar cannot be retaken regardless of the reason for absence.
• Mid-term Exam
  Mid-term covers all topics from the Syllabus (the first module). Mid-term consists of several paper-based tasks. The Mid-term is open-book: any amount of paper-based materials is allowed (printed or hand-written). During the Mid-term the cheating is strongly prohibited: the use of additional electronic resources/devices are prohibited; talking to the other students is also prohibited. In case of the rules violation the student gets zero points for the Mid-term. The length is 2 academic hours (1h 20m). The maximum grade is 10.
• Exam
  Exam is not blocking. Exam covers all topics from the Syllabus. The length is 2 academic hours (1h 20m). 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 the cheating is strongly prohibited: the use of additional electronic resources/devices are prohibited; talking to the other students is also prohibited. In case of the rules violation the student gets zero points for the Exam.

Interim Assessment

• 2024/2025 2nd module
  The final grade for the course is calculated using the formula: 0.1 ∗ regular tests + 0.2 ∗ mid-term exam + 0.3 ∗ homework + 0.1 ∗ graded seminar + 0.3 ∗ exam, where regular tests - the arithmetic mean of all the tests' scores received during the course with the exception of one worst-case test score (maximum 10 points); mid-term exam - score recieved for the mid-term exam (maximum 10 points); homework - the arithmetic mean of all the homeworks' scores received during the course (maximum 10 points); graded seminar- score recieved for the final graded seminar (is held at the end of the course; maximum 10 points); exam - score recieved for the final exam.

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

• Manichev Gennadii Gennadevich
• BRODSKAYA NATALYA NIKOLAEVNA
• Volkova Iuliia Mikhailovna