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Course Syllabus

Bachelor 2022/2023

Nonreactive and Big Data in the Social Sciences: Methods and Approaches

Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Compulsory course (Sociology)
Area of studies: Sociology
Delivered by: School of Sociology
Where: Faculty of Social Sciences
When: 4 year, 1 module
Mode of studies: distance learning
Online hours: 20
Open to: students of one campus
Instructors: Oxana Mikhaylova
Language: English
ECTS credits: 4
Contact hours: 36

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
  • Skills to collect online data (Wikipedia, YouTube, etc).
  • Skills to anaylze textual data
Expected Learning Outcomes

Expected Learning Outcomes

  • Have skills to analyze textual data
  • Have skills to scrap online data through various API, automatization of actions in browser etc
  • Know basic concepts of Big data, its opportunities, limitations, and relevance to social sciences
  • Know basic concepts of R programming language
Course Contents

Course Contents

  • Introduction to the course and the basics of harvest package
  • Cleaning data, making basic analytics and vizualizing
  • YouTube and Reddit scraping
  • Textual analysis
Assessment Elements

Assessment Elements

  • non-blocking Homework
  • non-blocking Test
  • non-blocking Homework
  • non-blocking Homework
  • non-blocking Homework
Interim Assessment

Interim Assessment

  • 2022/2023 1st module
    0.15 * Homework + 0.15 * Homework + 0.3 * Homework + 0.3 * Homework + 0.1 * Test
Bibliography

Bibliography

Recommended Core Bibliography

  • Big Data analytics with R : utilize R to uncover hidden patterns in your Big Data, Walkowiak, S., 2016
  • Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019
  • Text analysis for the social sciences : methods for drawing statistical inferences from texts and transcripts, , 1997

Recommended Additional Bibliography

  • Big data in complex and social networks, , 2017