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SMT203

Computational Social Science: Principles and Applications

1 CreditsBoth

Description

We use mobile devices any time to access the internet, read the news, watch videos, search for nearby restaurants, chat with friends, and leave posts on social networking sites. These online interactions leave massive digital footprints which enable us to understand, and ultimately influence human behavior and social dynamics: what and why we like, hate, believe, behave, and engage. Computational Social Science is an exciting and emerging field that sits at the intersection of computer science, statistics, and social science. This course provides a hands-on introduction to the ideas and methods of Computational Social Science. We will discuss questions and problems across various domains of social science including politics, economics, and health and will learn how new online data sources and computational methods are being used to tackle those problems. Through exploring computational social science methods and their use in social sciences today, this course helps students to engage with questions on research design. Also, students will have the opportunity to try their hand at analyzing big data from various sources such as #covid-19 Tweets, Data.gov.sg, etc.

Requisites

Prerequisites: IS111/SMT111/CS101/COR-IS1704 & IS112/IS105 & IS217/MGMT108/CS105 - Pre-req

Co-requisites: None

Anti-requisites: None

Attributes

Department: SCIS

Course Level: Undergraduate

Tracks: IS/T4BS: Smart-City Management & Technology Track

Areas: Advanced Business Technology Major Business Options Econ Major Rel/Econ Options IS Depth Electives Smart-City Mgmt & Tech Core (Intake 2019 to 2021) Smart-City Mgmt & Tech Core (Intake 2022 onwards) Smart-City Mgmt &Tech Core (Intake 2018 & earlier) Social Sciences/PLE Major-related Technology & Entrepreneurship

Learning Outcomes

1) To gain insights of what are the major questions in various domains in social science, such as politics, health, economics, media study, psychology, etc 2) To understand new possibilities of how to study society and human behavior with modern research tools 3) To understand the measures and methods used for network analysis and apply it for social science question 4) To apply text analysis and NLP techniques such as sentiment analysis, topic modeling, or word embeddings to understand individual and collective behaviors 5) To apply machine learning to find hidden patterns from social data 6) To gain some basic knowledge about causal inference 7) To develop research designs, including define problems and research questions, find and collect right data, and apply some basic analytic methods to answer the questions 8) To understand advantages and limitations in using online data for social problems

Graduate Learning Outcomes

Disciplinary Knowledge, Multidisciplinary Knowledge, Interdisciplinary Knowledge, Critical thinking & problem solving, Innovation and enterprising skills, Collaboration and leadership, Communication, Understanding of sustainability issues, Resilience

Competencies

Computational Modelling, Data Visualisation, Text Analytics and Processing, Security Education and Awareness, Content Management

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