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Аспирантура 2020/2021

Прикладной анализ данных в Python

Статус: Курс по выбору
Направление: 39.06.01. Социологические науки
Когда читается: 2-й курс, 1 семестр
Формат изучения: без онлайн-курса
Язык: русский
Кредиты: 4

Программа дисциплины

Аннотация

Python is one of the most popular and rapidly developing programming languages. A clear syntax which facilitates learning and a plethora of built-in and third-party libraries made Python especially popular among academics and researchers of all kinds. Python has already been the first-choice language in Machine Learning and Data Science for a while, but as far as Social Sciences are becoming more digitally-oriented it is getting in demand by sociologists, economists, linguists, and other social researchers. This course is created for PhD students who want to learn how to solve real-world data-related problems with Python programming environment but have no experience in programming. The course syllabus covers most of Python functionality from basics syntax to the modern libraries for machine learning and data analysis.
Цель освоения дисциплины

Цель освоения дисциплины

  • Being able to write Python programs covering basic need of data scientist.
Планируемые результаты обучения

Планируемые результаты обучения

  • Able to evaluate and revise learned scientific methods and methods of activity.
  • Able to store and manage data.
  • Able to retrieve data from open statistical databases, archives, and other public sources.
  • Able to analyze and visualize data with Python.
  • Able to independently master new research methods, change the scientific and production profile of their activity.
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Python for data analysis: overview
    a. Installing python b. Console and Python interpreter c. Command prompt and modules/scripts d. Editor
  • Scientific computing with Python: managing data, code, and result
    a. Scientific computing: the three “R”s b. Organizing a computational project c. Virtual environment
  • Writing code: statements and modules
    a. Statements, expressions, objects, operator
  • Data wrangling: numpy and pandas
  • Data collection: web scraping and APIs
  • Modeling: scikit-learn
  • Python for Social Network Analysis
Элементы контроля

Элементы контроля

  • неблокирующий Test 1. Statements, expressions, objects, operator
  • неблокирующий Exam
  • неблокирующий Test 2. Data: collecting and wrangling
  • неблокирующий Test 1. Statements, expressions, objects, operator
  • неблокирующий Exam
  • неблокирующий Test 2. Data: collecting and wrangling
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (I семестр)
    0.4 * Exam + 0.3 * Test 1. Statements, expressions, objects, operator + 0.3 * Test 2. Data: collecting and wrangling
Список литературы

Список литературы

Рекомендуемая основная литература

  • 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

Рекомендуемая дополнительная литература

  • Bernard, J. (2016). Python Recipes Handbook : A Problem-Solution Approach. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174476
  • Downey, A. (2012). Think Python. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=477161
  • Hajba G.L. Website Scraping with Python: Using BeautifulSoup and Scrapy / G.L. Hajba, Berkeley, CA: Apress, 2018.