Магистратура 2024/2025

Python для анализа данных

Статус: Курс обязательный (Магистр по наукам о данных)

Кто читает: Департамент больших данных и информационного поиска

Когда читается: 1-й курс, 2 модуль

Охват аудитории: для своего кампуса

Преподаватели: Быков Кирилл Валерьевич, Карпов Максим Евгеньевич

Язык: английский

Course Syllabus

Abstract

The course "Python for Data Analysis" is aimed at gaining basic knowledge and skills for processing, visualization and statistical analysis of data, as well as further completion of more specialized courses in this field (for example, machine learning). Students will learn how to solve problems of parsing, preprocessing and data visualization using standard and external Python libraries. The course will also cover the basics of object-oriented programming.

Learning Objectives

• Gaining skills in data processing and analysis using Python libraries.

Expected Learning Outcomes

• • Be able to formulate an analytical task and implement its execution in Python.
• • Be able to collect, pre-process, and visualize data and output descriptive statistics.
• • Be able to scrape information from varous web-sites and parse it into tables.

Course Contents

• Data visualization
• Data parsing
• Object-oriented programming
• Data processing

Assessment Elements

• Home Assignments
  Bi-Weekly issued home assignments at the Smart IMS platform.
• Test
  There will be a synchronous test with online proctoring at Smart LMS. The duration of the test is 1 hour.
• Project
  The project is evaluated according to the developed criteria. The project is conducted in a group of 2-3 students. There will be a project defense at the exam session.

Interim Assessment

• 2024/2025 2nd module
  0.2 * Home Assignments + 0.4 * Project + 0.4 * Test

Bibliography

Recommended Core Bibliography

• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925

Recommended Additional Bibliography

• McKinney, W. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822

Authors

• Ахмедова Гюнай Интигам кызы