See also: SyllabusSyllabus
See also: Lectures · Grading
Subject Code: DD325
Course Title: Data Visualization
Jan to May 2025
Course Objective: Learn to use data as a design material to exhibit, explore, explain, ex... · LecturesLectures
See also: Syllabus · Grading
Tuesdays
10am - 12:30pm Lectures + Studio
1pm - 2pm Lunch Break
2pm - 5pm Presentations + Exercises
Lecture 1 - Data and Information
7 Janua...
Data Visualization Course Grading Plan
Assessment and Grading Breakdown
Your performance in this course will be evaluated across the following components, each designed to assess your participation, understanding, and practical application of data visualization concepts discussed in the class.
| # | Component | Weightage (%) |
|---|---|---|
| 1 | Attendance and Class Participation | 10% |
| 2 | Class Exercises | 40% |
| 3 | Midsem Project | 20% |
| 4 | Midsem Exam | 5% |
| 5 | Final Project | 20% |
| 6 | Final Exam | 5% |
1. Attendance and Class Participation (10%)
Attendance reflects your consistent engagement with the course and is recorded for both the Morning Session (10:00 AM - 12:30 PM) and the Afternoon Session (1:30 PM - 3:30 PM) each Tuesday.
- Attendance cut-off times: 10:30 AM for the morning session and 1:30 PM for the afternoon session.
I have an open door policy and if you need to take a break or leave, feel free to do so. The penalty is that you don't get the attendance credit and don't learn from the material. No distinction is made between excused and unexcused absences. Late arrivals will not receive attendance credit but can still participate in class activities.
2. Class Exercises (40%)
Throughout the course, you will do in-class activities and deliver presentations about tools, techniques, and your own work during the course.
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You will present visualizations, techniques, and analyses related to course content during designated weeks, showcasing your ability to communicate data insights effectively.
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Each submission will be evaluated on the basis of Content Quality, Creativity/Originality, Presentation Skills, Time Management, Audience Interaction, etc. as applicable.
I will grade each of these submissions out of 8 and consider the best 5 scores.
- Exercise - Data Visualisation ToolsExercise - Data Visualisation Tools
Activity Details
This page lists a number of Data Visualisation tools. Pick one of these tools using the signup sheet shared on the class group and prepare a 5 minute presentation on it. You can c... - Exercise - Data SelfieExercise - Data Selfie
We are all producing data all the time!
Phone calls
Emails/Whatsapp messages
Health/fitness data
Instagram likes
Screen time/App usage Attendance marking
Location sharing/history
Br... - Exercise - Three Ways of VisualisingExercise - Three Ways of Visualising
Reference Material
See Which Chart to Use for a list of reference material that show the different types of visualisations and the types of charts that can be used to represent them.
Instruct... - Exercise - Critique and RedesignExercise - Critique and Redesign
Reference Material
See Which Chart to Use for a list of reference material that show the different types of data and the types of charts that can be used to represent them.
Instructions
Crea... - Exercise - Self Visualising InformationExercise - Self Visualising Information
Data doesn’t always need to be collected and processed to be seen—it often visualises itself in the physical world. By observing our surroundings, we can uncover naturally occurring data visualisat... - Exercise - Style a MapExercise - Style a Map
Find maps or other images that inspire you
Create a moodboard. Some sources could be:
David Rumsey Historical Map Collection
OldMapsOnline
Create Color Palette inspired by the colors you... - Exercise - Dashboard DesignExercise - Dashboard Design
I didn't want to give everyone in the class the exact same exercise, and I want everyone to have a sample dataset so there are restrictions similar to a real life visualisation problem. Use the pro... - Exercise - Schematic MapsExercise - Schematic Maps
Credits
This exercise is based on Rasagy's Schematic Map exercise. You can watch his Outlier 2022 talk Scheming Against Accurate Maps on Youtube and check out Map School on GitHub.
Step 1 - Where...
3. Midsem Project (20%)
The midterm project is a group project (groups up to 3). See Project - Midsem ProjectProject - Midsem Project
A Week in Data
Track personal habits (sleep, screen time, food intake, mood) and use what you learnt about data visualisation to create a data story that represents your lifestyle. Try and track a....
Team projects encourage collaboration. You should work together on all parts of the project, and should ensure that every team member is involved in all aspects of the project. The team will receive a single grade.
| Facets | Maximum Points | Evaluation Description |
|---|---|---|
| Punctuality | 2 | Submitting proposal (1) and final deliverable (1) by due date. |
| Project Concept | 3 | Impactfulness, uniqueness of area of exploration. Evaluated based on project proposal. |
| Data Collection | 5 | Quality of data collection and research. |
| Data Visualisation | 5 | Effectiveness of visualisation. Choice of marks and channels. |
| Visual Design of Final Submission | 5 | Aesthetics, final execution and storytelling. |
| Total | 20 |
5. Midsem Exam (5%)
Written exam conducted by university.
4. Final Project (20%)
The midterm project is a group project (groups up to 3) due end of April. See Project - Endsem ProjectProject - Endsem Project
Expected time available to work on this project is about 2 weeks, so the scope for the project is similiar to Midsem projects.
Option 1 - Expand on your midsem project
If you felt there was more ....
| Criteria | Marks | Description |
|---|---|---|
| Timeliness | 1 | Proposal and final project submitted on time. |
| Idea & Insights | 3 | Originality of concept and depth of thinking or hypothesis. |
| Data & Research | 4 | Quality of data collection, sources, and how well it's handled. |
| Visualisation | 4 | Clear and effective use of charts, graphs, or visuals to show the data. |
| Design & Execution | 4 | Aesthetic quality, clarity, and technical accuracy of the final outcome. |
| Presentation | 4 | Communication during presentation and quality of documentation. |
| Total | 20 |
5. Endsem Exam (5%)
Written exam conducted by university.
Academic Integrity
All submissions must be original and properly cite any third-party materials used, including datasets and code fragments. For group projects, the contributions of each team member must be clearly documented. Violations of academic integrity will be handled according to institutional policies.