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
Tuesday6 Jan 2026007:00 pm - 10:00 pm
Tuesday13 Jan 2026107:00 pm - 10:00 pm
Tuesday20 Jan 2026207:00 pm - 10:00 pm
Tuesday27 Jan 2026307:00 pm - 10:00 pm
Tuesday3 Feb 2026407:00 pm - 10:00 pm
Tuesday10 Feb 2026507:00 pm - 10:00 pm
Saturday21 Feb 2026612:00 pm - 03:00 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
-
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

  • 12:00 – 12:30 Choropleth Maps
  • 12:30 – 13:30 Spatial Visualisation with ggmap
  • 13:30 – 13:45 Break
  • 13:45 – 14:15 Introduction to RMarkdown
  • 14:15 – 15:00 Storyboarding and/or In-class assessment
ECA Due:
2-weeks after Session 6

 

Assessments

Assignments, Online Test, Others, Written Exam, PARTICIPATION

 

Trainer info

A/P Nicholas Sim is Associate Professor and Director of the University Research Office at the Singapore University of Social Sciences (SUSS). He previously served as Head of Programme for the Graduate Programmes in Analytics and Visualisation in the SUSS School of Business. His research is in applied econometrics, with a focus on the development and application of methods for causal inference and measurement. His work examines questions in development economics and political economy with particular emphasis on policy-relevant empirical analysis, and draws on advanced econometric techniques including quasi-experimental designs and panel data methods to study institutional quality, governance, and socio-economic outcomes. Prior to joining SUSS, Dr Sim was a faculty member at the University of Adelaide’s School of Economics, where he chaired the PhD programme and served as Associate Dean in the Faculty of Professions. He holds a PhD in Economics from Boston College and a Bachelor’s degree from the National University of Singapore, where he graduated with First Class Honours and was awarded the Economic Society of Singapore Gold Medal.

Dr Ren Jing is a Senior Lecturer with the School of Business at the Singapore University of Social Sciences (SUSS). Her research spans interdisciplinary areas including artificial intelligence, recommendation systems, user-generated content analysis, and fintech. She teaches both theoretical and practical modules on AI across undergraduate and graduate programmes, covering domains such as Business Analytics, Finance, Analytics & Visualisation, and AI for Business. She holds a B.Sc. Eng. and M.Sc. Eng. from Hefei University of Technology with First Class Honours, and a Ph.D. in Information Systems and Data Science from Singapore Management University.

 

 

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]