Прикладной анализ данных в Python

Аспирантура 2019/2020

Статус: Курс по выбору

Направление: 39.06.01. Социологические науки

Кто читает: Департамент социологии

Когда читается: 2-й курс, 1 семестр

Формат изучения: без онлайн-курса

Преподаватели: Кольцова Елена Юрьевна

Язык: английский

Кредиты: 4

Контактные часы: 28

Full Syllabus

Abstract

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.

Learning Objectives

Being able to write Python programs covering basic need of data scientist.

Expected Learning Outcomes

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.

Course Contents

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
Data preprocessing and modeling: scikit-learn
Python for Social Network Analysis

Assessment Elements

Test 1. Statements, expressions, objects, operator
Exam
Test 2. Data: collecting and wrangling

Interim Assessment

Interim assessment (1 semester)
0.4 * Exam + 0.3 * Test 1. Statements, expressions, objects, operator + 0.3 * Test 2. Data: collecting and wrangling

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

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.

Course Syllabus