Delivered at:: School of Sociology

Course type:: Compulsory course

When:: 1 year, 1 module

Instructor

Mikhaylova, Oxana

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

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

Course Contents

Introduction to Big data
Introduction to R
Data scraping in R
Introduction to text mining and network analysis in R

Assessment Elements

Homework_1
Homework_2
Homework_3
Homework_4
Test_1
Homework_5
Homework_6
Homework_7
Homework_8
Test_2

Interim Assessment

2023/2024 1st module
0.1 * Homework_1 + 0.05 * Homework_2 + 0.15 * Homework_3 + 0.15 * Homework_4 + 0.1 * Homework_5 + 0.05 * Homework_6 + 0.15 * Homework_7 + 0.15 * Homework_8 + 0.05 * Test_1 + 0.05 * Test_2

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

Master’s Programme 'Complex Social Analysis'

Introduction to Collection and Analysis of "Big data"