Abbreviation FT / PT
MSocSc(SDA) FT / PT

Social Data Analytics (SDA) refers to the analysis of large or complex data that arise from human and social interactions, with a greater emphasis on social sciences data and theories. The MSocSc in the field of SDA meets the increasing demand from undergraduate students for acquiring the technical and analytical skills necessary to leverage modern computational modeling methods and large naturalistic datasets to uncover how people think, decide, and act in groups, markets, and states.  It is designed to provide skills that are highly sought by both industry and PhD programmes.

A lot of social sciences data requires insights that combine data science methods, tools and techniques with disciplinary knowledge from social sciences theories and concepts.  In the private, public and academic sectors, there is a strong demand in the job market for social scientists who are also trained in data science.  The MSocSc in the field of SDA programme pools computational research expertise across the Faculty of Social Sciences to offer students quantitative, computational, and data analytical skills and the insight to apply them appropriately and innovatively to social sciences questions and prepare students for careers in academic research, NGO and governmental agency work, business, advertising, and marketing, and other jobs across the knowledge economy that require integrating social sciences domain expertise with large datasets in computational systems.

Mode of Attendance

Full Time (one year)
Part Time (two years)

Medium of Instruction

English

Programme Entrance Requirements

Applicants shall preferably have a Bachelor's degree in one of the fields of social sciences: Anthropology, Cognitive Science, Communication, Economics, Education Studies, Ethnic Studies, Linguistics, Political Science, Psychology, Sociology, Urban Studies and Planning, or a closely related field; or a Bachelor's degree in Mathematics, Computer Science, or a related field with an additional major/minor or substantial advanced coursework in one or more social sciences domains; and preferably have pre-existing training in statistics and/or formal logic or prior experience with one or more technical domains, including programming, statistics, formal logic, calculus, linear algebra, etc.

Programme Admissions Advisor(s)

Professor Tarani Chandola
Email: chandola@hku.hk

Contacts

Tel: 3917 7416
Email: msda@hku.hk
Website: https://www.socsc.hku.hk/sda/

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