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SOCI3001

Data Skills and Visualisation for Computational Social Science

Offer semester
Lecture time
Lecture venue
Credits awarded

1st semester

Wednesday

12:00-14:50

CPD-LG.59

6

  • In today’s increasingly data-driven world, it is often said that “data is the new oil.” Just like oil, data is a valuable resource, but it must be refined and processed to reveal its true potential. This course builds the foundational data skills and visualisation techniques necessary for modern social research, providing the tools to transform raw information into compelling insights about human behaviour and societal trends.

     

    The course begins with an introduction to R, a popular open-source programming language, and the RStudio environment. Students will learn the key principles for effective data handling: how to write code to import, clean, and reshape messy real-world data into structured formats. A core focus is placed on reproducible research, ensuring that data processes are transparent, professional, and easy to share.

     

    As the course progresses, the focus shifts to data storytelling. Students will move beyond basic charts to master the principles of effective visualisation, create animated plots, and build interactive web applications, all in R. Through hands-on exercises, participants will gain the practical skills needed to turn complex datasets into interactive "data products," enabling them to communicate findings to both academic and public audiences with clarity and impact.

     

    No prior experience in programming is required.      


    This is a Communication-intensive Courses (CiC)


    1. Implement reproducible data management workflows in RStudio to import, clean, and reshape messy, real-world social data into structured formats

    2. Design clear, accurate, and audience-appropriate static visualisations, animated plots, and maps using the grammar of graphics framework in R to reveal trends in human behaviour

    3. Acquire social science data programmatically from web platforms and utilize string manipulation tools to clean and structure text-based variables

    4. Build functional interactive web applications (data products) that enable diverse audiences to dynamically explore and interpret social data insights

  • Tasks

    Weighting

    Weekly In-class Exercises 

    20%

    Take-Home Assignments

    20%

    Examination

    60%


  • All key readings and recommended materials will be uploaded on Moodle.


    Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund. R for Data Science, 2nd Edition Released June 2023. Publisher(s): O'Reilly Media, Inc.

Offer Semester
Lecture Day
Lecture Time
Venue
Credits awarded
1st semester
Wednesday
12:00-14:50
CPD-LG.59
6

Professor, HKU-100 Scholar

Prof Guy Abel
Course co-ordinator and teachers
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