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 6 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 Activities and Presentations | 30% |
| 3 | Midsem Project | 20% |
| 4 | Midsem Exam | 5% |
| 5 | Final Project | 30% |
| 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 and Presentations (30%)
Throughout the course, you will do in-class activities and deliver presentations about tools, techniques, and your own work during the course.
-
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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Your top 6 scores will be averaged to determine your grade. Each presentation will be graded on a 5-point system.
| Facets | Maximum Points | Evaluation Description |
|---|---|---|
| Content Quality | 1 | Depth of research, relevance of content, and clarity in explaining concepts. |
| Presentation Skills | 1 | Clarity, confidence, and audience engagement. |
| Creativity/Originality | 1 | Innovative approach, use of visuals, and overall uniqueness. |
| Time Management | 1 | Staying within time limits while covering all key points. Taking too little time or too much time are both bad. |
| Audience Interaction | 1 | Ability to respond to questions and engage the audience effectively. |
| Total | 5 (x5) |
3. Midsem Project (20%)
The midterm project is a group project, and focuses on exploratory data visualization.
Team projects, of course, encourage collaboration. You are encouraged to work together on all parts of the project, and must ensure that every team member is involved in all aspects of the project (design, coding, and documentation). Although the team will receive a single grade, each team member will be asked to identify their own work product to ensure equitable division of labor.
| 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 | 4 | Quality of data collection and research. |
| Data Visualisation | 5 | Effectiveness of visualisation. Choice of marks and channels. |
| Visual Design of Final Submission | 6 | Aesthetics, final execution and storytelling. |
| Total | 20 |
5. Midsem Exam (5%)
Written exam conducted by university.
4. Final Project (30%)
The final project is an individual project, due in May, and focuses on developing a real-world data visualization solution.
Potential grading rubric (Subject to change)
| Facets | Maximum Points | Evaluation Description |
|---|---|---|
| Technical Accuracy, Methodology, Data Collection and Handling | 5 | Correctness of Data Analysis, coding, or calculations used in the visualization; Data collection methodology if data was collected by student; dataset processing and preparation if using third-party data set; |
| Visualization Design | 5 | Type of visualization used in relation to data-set; adherence to design principles |
| Presentation Skills | 5 | Effectiveness of presentation, clarity in communication, and explanation of the project. |
| Documentation and Clarity | 5 | Clarity of project write-up; including references, explanations, cited sources |
| Practical Application | 5 | Real-world applicability or usefulness of the project outcomes. |
| Critical Insights / Hypothesis | 5 | Depth and relevance of hypothesis and other insights communicated through project |
| Total | 30 |
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.