Бакалавриат
2025/2026
Нереактивные и большие данные в социальных науках: методы и подходы
Статус:
Курс обязательный (Социология)
Кто читает:
Департамент социологии
Когда читается:
4-й курс, 1 модуль
Онлайн-часы:
20
Охват аудитории:
для своего кампуса
Преподаватели:
Михайлова Оксана Рудольфовна
Язык:
английский
Course Syllabus
Abstract
This course provides a comprehensive introduction to advanced quantitative and computational methods for social science research, with a strong emphasis on practical data analysis using R. Students will progress through four core modules that reflect the contemporary workflow of digital social research: (1) data management and basic analytics in R; (2) modern data wrangling and visualization with the tidyverse; (3) advanced regression, multilevel modeling, and unsupervised learning for analyzing social hierarchies and latent structures; and (4) digital data collection and computational text analysis, including web scraping, social media mining, sentiment analysis, topic modeling, and semantic network analysis.
Throughout the course, students will gain hands-on experience with real and simulated datasets, learning to extract, clean, and analyze both tabular and unstructured data from online sources such as websites, Reddit, and YouTube. The curriculum integrates statistical modeling, machine learning, and natural language processing techniques, equipping students to address complex sociological questions in the era of big data. Emphasis is placed on reproducible research practices, ethical data collection, and the interpretation of analytical results for substantive social science inquiry. By the end of the course, students will be able to independently design, implement, and communicate sophisticated data-driven research projects using state-of-the-art tools and methods in R.
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 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
- Introduction to R Studio and Basic Data Operations for Sociological Research
- Data Manipulation and Visualization with Tidyverse
- Advanced Regression and Multilevel Modeling for Social Hierarchies
- Digital Data Collection and Advanced Text Analysis
Assessment Elements
- Laboratory work
- In-class assignment
- Test on reading material
- Discussion of reading materials
- Attendance
Interim Assessment
- 2025/2026 1st module0.1 * Attendance + 0.2 * Discussion of reading materials + 0.4 * In-class assignment + 0.2 * Laboratory work + 0.1 * Test on reading material
Bibliography
Recommended Core Bibliography
- A practical guide to scientific data analysis, Livingstone, D., 2010
- Advanced statistics in research : reading, understanding, and writing up data analysis results, Hatcher, L., 2013
- Big Data analytics with R : utilize R to uncover hidden patterns in your Big Data, Walkowiak, S., 2016
- Data analysis for social science : a friendly and practical introduction, Llaudet, E., 2023
- Data analysis for the social sciences : integtating theory and practice, Bors, D., 2018
- Long, J. D., & Teetor, P. (2019). R Cookbook : Proven Recipes for Data Analysis, Statistics, and Graphics: Vol. Second edition. O’Reilly Media.
- 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