Кто читает:: Департамент социологии

Статус:: Курс по выбору

Когда читается:: 4-й курс, 1-3 модуль

Преподаватели

Александров Даниил Александрович

Волченко Олеся Викторовна

Иванюшина Валерия Александровна

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. The seminar is intended for students who have previously attended research seminars of the BA in Sociology and Social Informatics in the previous years.

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

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

Introduction to analytical sociology and applications
Introduction to analytical sociology and applications
Sources of big data; quality of data
Sources of big data; quality of data
Research design for the big data analysis
Research design for the big data analysis
Studying stratification and intergenerational mobility using big data
Studying stratification and intergenerational mobility using big data
Social movements analysis using big data
Social movements analysis using big data
Presentation of the research results
Educational research using big data
Health research using big data
Ethical issues of the big data research

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.
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.
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.
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.
Participation in perusall discussion

Interim Assessment

2019/2020 4th module
0.3 * In-class assignments + 0.4 * Participation in class discussions + 0.3 * Presentation of the individual project
2020/2021 4th module
0.4 * Participation in class discussions + 0.3 * Presentation of the individual project + 0.3 * In-class assignments
2021/2022 3rd module
0.2 * Final exam + 0.2 * In-class assignments + 0.2 * Participation in perusall discussion + 0.2 * Presentation of their individual project + 0.2 * Participation in class discussions

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

Бакалаврская программа «Социология и социальная информатика»

Контакты

Коронавирус Live

Research Seminar “Analytical Sociology and Big Data”