Course Code: ANL501
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Synopsis

ANL501 Data Visualisation and Storytelling aims to equip students with the knowledge and skills to construct compelling data visualisations using a programming and reproducible approach. The course will delve into the core concepts of data visualisations grounded in the principles of Grammar of Graphics. Students will gain fundamental skills in R to acquire, transform and manage data for visualisation. Students will also become proficient in developing R programs to construct visualisations tailored for different data types including univariate and multivariate categorical and numerical data, time series data, and spatial data. Emphasis will be placed on the implementation of visualisation best practices in R in the context of reproducible workflow and the application of sound storytelling principles to create data-rich presentations for decision-making.
Level: 5
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Introduction to R and RStudio
  • R operations and programming
  • Data management in R
  • Principles of data storytelling and essential descriptive statistics
  • Introduction to R for data visualisation
  • The Grammar of Graphics and its components
  • Data preprocessing and data management with tidyverse
  • Reproducible workflow with RMarkdown
  • APIs for data acquisition
  • Visualising time series, numerical, categorical, and table data
  • Visualising spatial data
  • Storyboarding and best practices for data storytelling

Learning Outcome

  • Assess the data requirements for data visualisation
  • Compose data-rich visualisations based on the principles of Grammar of Graphics
  • Critique data stories constructively
  • Construct R programs for data visualisation
  • Create RMarkdown documents as part of a reproducible workflow
  • Design and implement a data visualisation workflow from data acquisition, data transformation, and then to data visualisation
  • Demonstrate understanding of descriptive statistics in the context of data visualisation
Part-time Programmes / CET Courses

Date and Duration

DayDateWeekTime
Tuesday4 Aug 2026007:00 pm - 10:00 pm
Tuesday11 Aug 2026107:00 pm - 10:00 pm
Tuesday18 Aug 2026207:00 pm - 10:00 pm
Tuesday25 Aug 2026307:00 pm - 10:00 pm
Tuesday1 Sep 2026407:00 pm - 10:00 pm
Tuesday8 Sep 2026507:00 pm - 10:00 pm
Saturday15 Sep 2026607:00 pm - 10:00 pm


Target Audience

Executives interested analytics.

 

Relevance of Course to employment/upskilling/reskilling

ANL501 Data Visualisation and Storytelling supports employment, upskilling, and reskilling by training learners to turn data into clear visuals and compelling narratives for decision-making. These skills are widely demanded across analytics and business roles, strengthening professionals’ ability to communicate insights to diverse stakeholders and drive action in data-driven organisations.


Admissions pre-requisite

Refer to graduate CET admissions and also include Admission Eligibility Criteria for Graduate CET Modular Courses (Admission Eligibility Criteria for Graduate CET Modular Courses).

 

Schedule

TimeAgendaDeliverables
Session 0

Pre-Lecture

  • 19:00 – 19:30 Getting Started with RStudio and R
  • 19:30 – 20:30 Introduction to R Programming
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Basic R Operations
PCQ Due:
Before Session 1
Session 1

Introduction to R

  • 19:00 – 19:30 Introduction to the Course
  • 19:30 – 20:30 R Programming
  • 20:30 – 20:45 Break
  • 20:45 – 21:30 Data Management in Base R
  • 21:30 – 22:00 Suggested Practices in Data Storytelling
-
Session 2

Data Management and Data Storytelling Principles

  • 19:00 – 20:30 Data Management with Tidyverse
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Principles of Data Storytelling and the Grammar of Graphics
-
Session 3

Getting Started with ggplot2

  • 19:00 – 20:30 Getting Started with Data Visualisation Aesthetics and Settings
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Other Layers in the Grammar of Graphics
-
Session 4

Visualising Distributions and Trends

  • 19:00 – 20:30 Plotting Distributions: Bar Charts, Histograms, and Density Plots
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Plotting Trends: Time Series Plots
TMA Due:
Before Session 5
Session 5

Enhancing Data Storytelling with Data Preprocessing, Comparison Plots, Labels and Annotations

  • 19:00 – 19:30 Comparing Distributions: Boxplots and Other Comparison Plots
  • 19:30 – 20:30 Plotting Tables: Column Plots and Data Preprocessing
  • 20:30 – 20:45 Break
  • 20:45 – 22:00 Enhancing Visualisations for Data Storytelling: Labels and Annotations
-
Session 6

Visualisation Across Space: Chloropleth Maps and Spatial Plots, and Further Issues in Data Visualisation

  • 19:00 – 19:30 Choropleth Map
  • 19:30 – 20:30 Spatial Visualisation with ggmap
  • 20:30 – 20:45 Break
  • 20:45 – 21:15 Introduction to RMarkdown, Storyboarding
  • 21:15 – 22:00 In-Class Assessment and Evaluation
ECA Due:
2-weeks after Session 6

 

Assessments

Assignments, Online Test, Others, Written Exam, PARTICIPATION

 

Trainer info

Dr Willie Low Wai Lup is an executive and AI transformation leader with over 20 years of experience driving technology go-to-market, marketing and digital innovation across the Asia-Pacific region. He has held regional leadership roles at technology multinationals, including Ciena, Oracle, and Citrix. Dr. Low specializes in optimizing enterprise marketing workflows, digital strategy, and demand generation. He is a dedicated mentor within the SUSS alumni network and leads the SUSS Alumni Mentorship Program. He holds a Doctor of Business Administration from the Singapore University of Social Sciences, an MBA from the University of Oxford, and a Master of Science and Bachelor of Science (First Class Honors) from the National University of Singapore.

Dr. Karl Wu Ka Yui holds a PhD in Statistics and has over 18 years of teaching experience in statistics, data analytics, and time series forecasting. His research interests include joint modelling of mean and dispersion, bivariate ordinal data, and the analysis of non-linear time series with heteroscedasticity. He is also an experienced R user and has published two R packages on CRAN to date. Prior to joining academia, he worked in marketing research and lived and worked in Germany, Hong Kong, and Denmark before relocating to Singapore to join the Singapore University of Social Sciences (SUSS).


Course Completion requirements

Participants are required to achieve at least 75% attendance and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.

  • Participants are required to complete all surveys and feedbacks related to the course
  • The course fees are reviewed annually and may be revised. The University reserves the right to adjust the course fees without prior notice.
  • Singapore University of Social Sciences reserves the right to amend and/or revise the above schedule without prior notice

 

Course Fees, payment and refund policy

 International Participants Singapore Citizens (below 40yrs), Permanent Residents Singapore Citizens (40yrs and above) SkillsFuture Mid - Career Enhanced Subsidy1Enhanced Training Support for SMEs2 (Singaporean and PRs)
Course Fees (A) $3,168.00 $2,640.00 $2,640.00 $2,640.00
SSG Grant (70%) (B)  $1,848.00 $1,848.00 $1,848.00
Nett Course fees (A) - (B) = (C) $3,168.00 $792.00 $792.00 $792.00
9% GST on Nett course fees (D) $285.12 $71.28 $71.28 $71.28
Total nett course fees payable including GST (C) + (D) $3,453.12 $863.28 $863.28 $863.28
Less additional funding if eligible under various schemes (F) $-   $-   $528.00 $528.00
Total nett course fees payable including GST, after additional funding form the various schemes (E) - (f) = (H) $3,453.12 $863.28 $335.28 $335.28
1 Mid-Career Enhanced Subsidy: Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fees.
2 Enhanced Training Support for SMEs: SME-sponsored employees (Singapore citizens and PRs) aged 21 and above may enjoy subsidies up to 90% of the course fees.

 

For the various payment mode, please refer here.
For the refund policy, please refer here. 

 

For clarification, please contact the SUSS Academy via the following:
Telephone: +65 6248 0263
Email: [email protected]