Data Visualisation and Storytelling | CET Courses

Singapore University of Social Sciences

Data Visualisation and Storytelling (ANL501)

Applications Open: 01 May 2023

Applications Close: 15 June 2023

Next Available Intake: July 2023

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)

Funding: SkillsFuture

School/Department: School of Business

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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 semester

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
Demonstrate understanding of descriptive statistics in the context of data visualisation