Data Visualization for Data - Driven Dec...
Data Visualization for Data-Driven Decision Making
Instructor: Oladimeji Kazeem
Last Updated: 25-10-2026 | Language: English
What you'll learn
- ✔ Foundations of Data Visualization and Decision-Making
- ✔ Data Preparation, Summaries and Tabular Presentation
- ✔ Graphs, Charts and Statistical Plots
- ✔ Visual Storytelling with Images and Infographics
- ✔ Video Animation and Time-Based Data Visualization
- ✔ Interactive and Exploratory Data Visualization
- ✔ Decision Intelligence, Evaluation and Integrated Practical Project
Who this course is for
- Students and lecturers taking DTS 403
Seminar Class Schedule
7th November 2026 to 31st December 2026
Sat - 12:00 PM WAT
All times are in West African Time (WAT, UTC+1).
Lecture dates (7 lectures)
-
Nov
07
Sat
Foundations of Data Visualization and Decision-Making Next
Saturday, 7th November 2026 12:00 PM – 2:00 PM WAT Zoom
-
Nov
17
Tue
Data Preparation, Summaries and Tabular Presentation
Tuesday, 17th November 2026 1:00 PM – 3:00 PM WAT Zoom
-
Nov
20
Fri
Graphs, Charts and Statistical Plots
Friday, 20th November 2026 12:00 PM – 2:00 PM WAT Zoom
-
Nov
25
Wed
Visual Storytelling with Images and Infographics
Wednesday, 25th November 2026 12:00 PM – 2:00 PM WAT Zoom
-
Nov
28
Sat
Video Animation and Time-Based Data Visualization
Saturday, 28th November 2026 2:00 PM – 3:00 PM WAT Zoom
-
Dec
02
Wed
Interactive and Exploratory Data Visualization
Wednesday, 2nd December 2026 12:00 PM – 2:00 PM WAT Zoom
-
Dec
08
Tue
Decision Intelligence, Evaluation and Integrated Practical Project
Tuesday, 8th December 2026 1:00 PM – 3:00 PM WAT Zoom
Course Content
Description
Course description
Data Visualization for Data-Driven Decision Making introduces students to the principles, methods, tools and practical techniques used to transform raw data into meaningful visual information that supports evidence-based decisions. The course examines how data can be summarised, organised, analysed and presented to diverse audiences through tables, graphs, statistical plots, images, video animations and interactive visualisations.
Students will learn to select appropriate visualisation techniques based on data characteristics, analytical objectives, audience needs and decision contexts. Particular attention will be given to the interpretation of visual evidence, the ethical presentation of data, the avoidance of misleading representations, and the real-world implications of decisions informed by visual analytics.
The course combines conceptual foundations with laboratory exercises in which students create, evaluate and communicate visualisations using practical datasets and digital tools.
2. Learning outcomes
By the end of the course, students should be able to:
- Prepare and communicate data effectively: Transform raw data into clear, accurate and audience-appropriate visual representations.
- Select appropriate visualisation methods: Choose between tables, graphs, plots, images, animations and interactive visualisations based on the data and analytical purpose.
- Interpret and summarise data: Apply descriptive statistics, data summaries and exploratory visualisation techniques to identify patterns, trends, relationships and anomalies.
- Design meaningful visualisations: Produce readable, engaging and accessible visual outputs using appropriate visual encoding, labels, scales and layouts.
- Develop interactive and animated visualisations: Create visualisations that allow users to explore data through filtering, zooming, interaction and time-based animation.
- Support evidence-based decision-making: Interpret visual findings critically and explain their implications for real-world decisions.
- Evaluate the integrity of visual evidence: Identify misleading charts, inappropriate scales, biased presentation, uncertainty and limitations in the underlying data.