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Бакалаврская программа «Социология и социальная информатика»

Research Seminar “Analytical Sociology and Big Data”

2022/2023
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс по выбору
Когда читается:
4-й курс, 1-3 модуль

Course 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

Learning Objectives

  • to provide students with skills necessary for conducting social research based on big data analysis
Expected Learning Outcomes

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

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

Assessment Elements

  • non-blocking Participation in class discussions
  • non-blocking In-class assignments
    In-class assignments grade will be calculated as an average score for all types of written activities during the seminars.
  • non-blocking 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.
  • non-blocking 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

  • 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

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