Бакалавриат 2020/2021

Научно-исследовательский семинар "Аналитическая социология и большие данные"

Статус: Курс по выбору (Социология и социальная информатика)

Направление: 39.03.01. Социология

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Когда читается: 2-й курс, 1-4 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Александров Даниил Александрович, Волченко Олеся Викторовна, Иванюшина Валерия Александровна

Язык: английский

Кредиты: 4

Контактные часы: 42

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

• 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

• 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

• 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

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