Research Seminar “Analytical Sociology and Big Data”

Bachelor 2020/2021

Type: Elective course (Sociology and Social Informatics)

Area of studies: Sociology

Delivered by: Department of Sociology

Where: Saint-Petersburg School of Social Sciences

When: 4 year, 1-3 module

Mode of studies: offline

Instructors: Daniil A. Alexandrov, Valeria A. Ivaniushina, Olesya Vólchenko

Language: English

ECTS credits: 3

Contact hours: 30

Full Syllabus

Abstract

During the course different features of analytical approach towards big data will be covered as well as a variety of examples of reports and articles relevant for the field. During the course students are expected to read and discuss journal articles and book chapters; participate in group research projects; give presentations on their research projects and topics of their interest.

Learning Objectives

to provide students with skills necessary for conducting social research based on big data analysis

Expected Learning Outcomes

understand modern features and issues of big data analytics; should learn basic methodological principles and major methods applicable for big data analysis
be able to apply the methods of analytical sociology and social statistics to the analysis of big data; to use basic rules of statistical inference; to employ major sociological concepts as instruments of sociological research
be able to read and critically discuss articles from the field of the big data analysis and conduct empirical research using different sources of the data

Course Contents

Introduction to analytical sociology and applications
Basic principles of analytical sociology. Key authors in the field of analytical sociology
Sources of big data; quality of data
Typology of data sources. Principles of data collection. Big data quality assessment
Literature review: basic principles and search for the articles
Basic principles. Logic of literature review. Sources of literature
Operationalization of theoretical concepts and measurement
Operationalization. Measurement principles in sociology
Research design for the big data analysis
Typology of research designs. Most common research designs for big data researches
Studying stratification and intergenerational mobility using big data
General idea of social stratification analysis. Big data sources. Example article
Social movements analysis using big data
General idea of social movements analysis. Big data sources. Example article
Presentation of the research results
General principles of good presentation. Practical session

Assessment Elements

Participation in class discussions
In-class assignments
In-class assignments grade will be calculated as an average score for all types of written activities during the seminars.
Presentation of their individual project
Presentation of the individual project includes final presentation on the topic of student’s thesis and should represent a solid presentation of research framework, literature review, data description and preliminary analysis.
Final exam
Final exam will consist of a set of questions related to student’s thesis. Answer to all questions will be cross-graded by several instructors and the final grade for the exam will be calculated as an average score for all grades for all exam items. The grade for the final exam is rounded according to algebra rules.

Interim Assessment

Interim assessment (3 module)
0.25 * Final exam + 0.25 * In-class assignments + 0.25 * Participation in class discussions + 0.25 * Presentation of their individual project

Bibliography

Recommended Core Bibliography

Van Rijmenam, M. (2014). Think Bigger : Developing a Successful Big Data Strategy for Your Business. New York: AMACOM. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=686831

Recommended Additional Bibliography

Manzo, G. (2014). Analytical Sociology : Actions and Networks. Hoboken: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=714658

Course Syllabus