Singapore University of Social Sciences

Data Visualisation and Storytelling

Data Visualisation and Storytelling (ANL501)

Applications Open: 01 April 2020

Applications Close: 31 May 2020

Next Available Intake: July 2020

Course Types: Modular Graduate Course, SkillsFuture Series

Language: English

Duration: 6 months

Fees: $2200 View More Details on Fees

Area of Interest: Business Administration

Schemes: Lifelong Learning Credit (L2C), Resilience 2020

Funding: SkillsFuture

School/Department: School of Business


Synopsis

ANL501 Data Visualisation and Storytelling aims to equip students with the knowledge and skills to acquire, measure, monitor, visualise and report business performance in a scalable and reproducible manner.Students will learn essential concepts, skills, and techniques in probability theory, statistics, MySQL, Python, R, and data storytelling. Emphasis will be placed on learning and implementing visualisation best practices in R as part of a reproducible workflow. Students will also learn core MySQL, Python, and R skills to acquire and prepare data for visualisation from a variety of data sources. Finally, students will learn how to apply storytelling techniques to create impactful data-rich presentations tailored for senior management decision-making.

Level: 5
Credit Units: 5
Presentation Pattern: Every July

Topics

  • Introduction to R and RStudio
  • Essential concepts in probability and statistics
  • Visusalisation in R
  • Introduction to MySQL
  • Reproducible workflow with RMarkdown
  • Working with spatial datasets
  • Introduction to Python
  • Working with Python and MySQL
  • Integrating Python, MySQL, and R
  • Telling stories through data-rich presentations
  • Common pitfalls in data visualisation
  • Best practices for data storytelling

Learning Outcome

  • Assess the data requirements for business performance metrics
  • Compose effective data-rich presentations for a senior management audience
  • Critique data stories constructively
  • Construct R programs to visualise data
  • Create RMarkdown documents as part of a reproducible workflow
  • Design and implement a full stack from data acquisition to analytics and then to visualisation with Python, MySQL, and R
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