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Course Syllabus

Bachelor 2021/2022

Research Seminar “Analytical Sociology and Big Data”

Type: Elective course (Sociology and Social Informatics)
Area of studies: Sociology
Delivered by: Department of Sociology
Where: Saint-Petersburg School of Social Sciences
When: 3 year, 1-4 module
Mode of studies: offline
Open to: students of all HSE University campuses
Instructors: Daniil A. Alexandrov, Valeria A. Ivaniushina, Olesya Vólchenko
Language: English
ECTS credits: 4
Contact hours: 42

Course Syllabus

Full SyllabusAsk Question

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

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

Expected Learning Outcomes

  • 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
  • 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
  • employ major sociological concepts as instruments of sociological research
  • learn basic methodological principles and major methods applicable for big data analysis
  • read and discuss journal articles and book chapters; participate in group research projects; give presentations on their research projects and topics of their interest
  • understand modern features and issues of big data analytics
  • use basic rules of statistical inference
Course Contents

Course Contents

  • Introduction to analytical sociology and applications
  • Sources of big data; quality of data
  • Research design for the big data analysis
  • Studying stratification and intergenerational mobility using big data
  • Social movements analysis using big data
  • Presentation of the research results
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.
  • 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.
  • 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.
  • non-blocking Participation in class discussions
  • non-blocking Written 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 thesis and should represent a solid presentation of research framework, literature review, data description and preliminary analysis.
  • non-blocking Final exam
    The core aim of the exam is to test students' ability to deliver the general idea of the final thesis to different audiences. The exam will consist of 3 tasks. Students will have 90 min. to complete the tasks in a written form (online or in a computer class).
  • 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.
  • non-blocking Participation in perusall discussion
Interim Assessment

Interim Assessment

  • 2020/2021 4th module
    0.4 * Participation in class discussions + 0.3 * In-class assignments + 0.3 * Presentation of the individual project
  • 2021/2022 4th module
    0.3 * Presentation of the individual project + 0.3 * Participation in class discussions + 0.2 * Participation in perusall discussion + 0.2 * In-class assignments
  • 2022/2023 3rd module
    0.25 * Presentation of the individual project + 0.25 * Participation in class discussions + 0.25 * Final exam + 0.25 * Written assignments
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

Authors

  • IVANYUSHINA VALERIYA ALEKSANDROVNA
  • VOLCHENKO OLESYA VIKTOROVNA
  • TSVETKOVA EKATERINA ANDREEVNA
  • ALEKSANDROV DANIIL ALEKSANDROVICH