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Regular version of the site
Bachelor 2019/2020

Research Seminar “Analytical Sociology and Big Data”

Type: Elective course (Sociology and Social Informatics)
Area of studies: Sociology
When: 2 year, 1-4 module
Mode of studies: Full time
Language: English
ECTS credits: 4

Course Syllabus

Abstract

The purpose of the course is to provide students with skills necessary for conducting social research based on big data analysis. 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.
Learning Objectives

Learning Objectives

  • 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.
Expected Learning Outcomes

Expected Learning Outcomes

  • understand modern features and issues of big data analytics
  • 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
  • use basic rules of statistical inference
  • employ major sociological concepts as instruments of sociological research
  • read and discuss journal articles and book chapters; participate in group research projects; give presentations on their research projects and topics of their interest
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
  • Educational research using big data
    General idea of education research. Big data sources. Example article
  • Health research using big data
    General idea of health research. Big data sources. Example article
  • Ethical issues of the big data research
    General ethical principles. Ethical issues in big data research
  • 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 the individual project
    Presentation of the individual project includes final presentation on the topic of student’s course work and should represent a solid presentation of research framework, literature review, data description, data analysis and main conclusions.
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.3 * In-class assignments + 0.4 * Participation in class discussions + 0.3 * Presentation of the individual project