Master
2024/2025
Introduction to Collection and Analysis of "Big data"
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Compulsory course (Complex Social Analysis)
Area of studies:
Sociology
Delivered by:
School of Sociology
Where:
Faculty of Social Sciences
When:
1 year, 1 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Oxana Mikhaylova
Master’s programme:
Complex Social Analysis
Language:
English
ECTS credits:
3
Course 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
- 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
- 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
- The student will know the fundamental concepts of big data, its opportunities, and limitations in the context of social research.
- The student will establish a comprehensive ethical framework for social media research.
- The student will master the basics of R and develop computational thinking skills for application in the social sciences.
- The student will perform comprehensive cleaning and preprocessing of consumer behavior data.
- The student will develop advanced skills in collecting data from web sources and APIs using R.
- The student will implement the YouTube API for academic research on video content and comments.
- The student will be able to apply computational methods for analyzing textual data and social networks.
- The student will master bibliometric methods and visualization tools for analyzing academic research.
- The student will develop expertise in bibliometric analysis using R tools and specialized software.
- The student will be able to integrate multiple methods and understand the full cycle from research to publication.
- The student will understand the entire publication process from manuscript preparation to final publication.
Course Contents
- Introduction to Big data
- Introduction to R
- Data scraping in R
- Introduction to text mining and network analysis in R
Bibliography
Recommended Core Bibliography
- Big data : a revolution that will transform how we live, work and think, Mayer-Schonberger, V., 2013
- Data mining with R : learning with case studies, Torgo, L., 2017
- R в действии : анализ и визуализация данных в программе R, Кабаков, Р. И., 2014
Recommended Additional Bibliography
- ggplot2 : elegant graphics for data analysis, Wickham, H., 2009