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Course Syllabus

2021/2022

Introduction to Collection and Analysis of "Big data"

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Optional course (faculty)
Delivered by: School of Sociology
Where: Faculty of Social Sciences
When: 1, 2 module
Open to: students of all HSE University campuses
Instructors: Alina Arslanova
Language: English
ECTS credits: 4
Contact hours: 44

Course Syllabus

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 R studio. 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 R studio packages will be used for data analysis. Basic knowledge of quantitative sociological methods is required. Familiarity with R studio is very helpful but not required. To run R studio, install it or use cloud version (freely available at: https://www.rstudio.com/products/rstudio/download/).
Learning Objectives

Learning Objectives

  • Know basic methods of collecting nonreactive data in social sciences
  • Know different types of big data in social sciences
  • Use skills to collect online data (Wikipedia, YouTube, etc).
  • Use skills to analyze textual data
Expected Learning Outcomes

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 R 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
Course Contents

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
  • Introduction to Deep Learning in Python
  • Sequence modeling
  • Introduction to Transformers
Assessment Elements

Assessment Elements

  • non-blocking Quizzes
  • non-blocking Homework 1
  • non-blocking Homework 2
  • non-blocking Homework 3
  • non-blocking Homework 4
  • non-blocking Homework 5
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.15 * Homework 5 + 0.15 * Homework 4 + 0.15 * Homework 1 + 0.3 * Homework 2 + 0.1 * Quizzes + 0.15 * Homework 3
Bibliography

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
  • Beysolow, T. (2018). Applied Natural Language Processing with Python : Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1892182
  • Hajba G.L. Website Scraping with Python: Using BeautifulSoup and Scrapy / G.L. Hajba, Berkeley, CA: Apress, 2018.
  • Jeremy Howard, & Sylvain Gugger. (2020). Deep Learning for Coders with Fastai and PyTorch. O’Reilly Media.
  • Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, & B. K. Tripathy. (2020). Deep Learning : Research and Applications. De Gruyter.
  • 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.