Нереактивные и большие данные в социальных науках: методы и подходы

Бакалавриат 2021/2022

Статус: Курс обязательный (Социология)

Направление: 39.03.01. Социология

Кто читает: Департамент социологии

Где читается: Факультет социальных наук

Когда читается: 4-й курс, 1 модуль

Формат изучения: с онлайн-курсом

Охват аудитории: для своего кампуса

Преподаватели: Арсланова Алина Раильевна

Язык: английский

Кредиты: 4

Контактные часы: 36

Full Syllabus

Abstract

The growth of Internet penetration and the possibility of collecting and analyzing big data have produced new challenges and have offered new opportunities for researchers and official statistics. Within several years nonreactive and big data has become the main trend in the social sciences. Nonreactive methods include nonparticipant observation and analysis of digital fingerprints such as likes or shares, as well as private documents such as blogs, social media profiles and comments, or public online documents such as mass media materials. This course will give an introduction to key quantitative approaches to the collection of nonreactive data in social sciences. The course is taught in the form of lectures, seminars, and individual work using Jupyter notebook. All teaching is conducted in English. The goal of the course is to introduce the opportunities of nonreactive and big data for social scientists and learn basic methods and tools to collect nonreactive data. Within the course some Python packages will be used for data analysis. Basic knowledge of quantitative sociological methods is required. Familiarity with Python is very helpful but not required. To run Jupyter notebook, install Anaconda (freely available at: https://www.anaconda.com/products/individual#Downloads). This course uses some materials and tasks from https://www.datacamp.com/ (free access is provided to all students)

Learning Objectives

Know basic methods of collecting nonreactive data in social sciences
Know different types of big data in social sciences
Skills to collect online data (Wikipedia, YouTube, etc).
Skills to anaylze textual data

Expected Learning Outcomes

Have skills to analyze textual data
Have skills to scrap online data through various APIs, automatization of actions in browser, and etc
Have skills to write Python code for basic data analysis tasks
Know basic concepts of Big data, its opportunities, limitations, and relevance to social sciences
Know basic concepts of reactive and nonreactive data, its opportunities, limitations, and applications in social sciences
Know basic concepts of Python programming language

Course Contents

Introduction to Python
Basic data manipulation in Python
Basic Text Processing
Web-scrapping
Client server architecture and request response: work with APIs
Distributional semantics and topic modeling

Assessment Elements

Quizzes
Homework 1
Homework 2
Homework 3
Homework 4

Interim Assessment

2021/2022 1st module
0.15 * Homework 1 + 0.3 * Homework 3 + 0.3 * Homework 2 + 0.1 * Quizzes + 0.15 * Homework 4

Bibliography

Recommended Core Bibliography

Bengfort, B., Bilbro, R., & Ojeda, T. (2018). Applied Text Analysis with Python : Enabling Language-Aware Data Products with Machine Learning. Beijing: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1827695
Bird, S., Loper, E., & Klein, E. (2009). Natural Language Processing with Python. O’Reilly Media.
Hajba G.L. Website Scraping with Python: Using BeautifulSoup and Scrapy / G.L. Hajba, Berkeley, CA: Apress, 2018.
Vanderplas, J. T. (2016). Python Data Science Handbook : Essential Tools for Working with Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1425081

Recommended Additional Bibliography

Eric Matthes. (2019). Python Crash Course, 2nd Edition : A Hands-On, Project-Based Introduction to Programming: Vol. 2nd edition. No Starch Press.
Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2020). Data Mining for Business Analytics : Concepts, Techniques and Applications in Python. Newark: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2273611

Course Syllabus