CSL4050: Data Visualization (Spring 2024)

Table of Contents


  • Credits L-T-P [C]: 3-0-3 [4.5]
  • Expectation from 4000 level course:
    • 1 Contact Hr + 2 Non-Contact Hr
    • Learn by Assignments/Experiments
  • Where: LHB 105
  • Slot: A (Monday, Tuesday, and Thursday 9:00 AM - 9:50 AM)
    • Lab Slot: Will be Announced
  • LMS: Moodle
    • Credential: Internet ID/Password
    • Easy Enrollment Code: ka9858


  • Introduction: Data for Graphics, Design principles, Value for visualization, Categorical, time series, and statistical data graphics, Introduction to Visualization Tools
  • Graphics Pipeline: Introduction, Primitives: vertices, edges, triangles, Model transforms: translations, rotations, scaling, View transform, Perspective transform, window transform
  • Aesthetics and Perception: Graphical Perception Theory, Experimentation, and the Application, Graphical Integrity, Layering and Separation, Color and Information, Using Space Effectively
  • Visualization Design: Visual Display of Quantitative Information, Data-Ink Maximization, Graphical Design, Exploratory Data Analysis, Heat Map
  • Multidimensional Data: Query, Analysis and Visualization of Multi-dimensional Relational Databases, Interactive Exploration, tSNE
  • Interaction: Interactive Dynamics for Visual Analysis, Visual Queries, Finding Patterns in Time Series Data, Trend visualization, Animation, Dashboard, Visual Storytelling
  • Collaboration: Graph Visualization and Navigation, Online Social Networks, Social Data Analysis, Collaborative Visual Analytics, Text, Map, Geospatial data

Lab Content

  • Visualization Design, Exploratory data analysis, Interactive Visualization Tools like Gephi, D3, etc. Mini Project.
  • We will be using Python Dash, R-Shiny, D3 JS, Grephi in our lab work

Learning Materials


  • E. TUFTE (2001), The Visual Display of Quantitative Information, Graphics Press, 2nd Edition.
  • J. KOPONEN, J. HILDÉN (2019), Data Visualization Handbook, CRC Press.

Reference Books

  • M. LIMA (2014), The Book of Trees: Visualizing Branches of Knowledge, Princeton Architectural Press.
  • R. TAMASSIA (2013), Handbook of Graph Drawing and Visualization, CRC Press.
  • S. MURRAY (2017), Interactive Data Visualization for the Web, O’Reilly Press, 2nd Edition.

Attendance Requirement

As per the notification from academics 100% attendance is mandatory. If you have genuine reason please take leave approval as per academics rule.

If attendance falls below 75%, one should get at least C grade to pass the course. Otherwise F grade will be assigned.

Grading Policy

Quizzes (slido) Mini Project Lab Assignments Minors Major
10% 10% 30% 15% + 15% 20%

Quizzes (Slido)

  • Each class will have 2 to 3 quizzes on slido. On mobile or laptop.
  • Questions will be on understanding level
  • 75% of the quizzes will be part of the grading
  • Leader board will be shown each class

Mini Project

  • Students need to build visualization project as part of the lab exercise
  • Projects can be done in group, number of group members will be decided after the add-drop date.

Lab Assignment

  • There is 9 different assignments out of which 7 is graded
  • Please follow the deadlines
  • No copy paste from friends. Plagiarism policy will be followed if found
  • All of the lab assignments are already uploaded in moodle
  • Don’t copy from Github or public repo!
    • If found will be marked zero
    • However, you can select a Github project on visualization and make substantial changes as part of your project. In this case share the changes you have made in the project.

Plagiarism tolerance is 7% from single source and 15% cumulative, anything more will reduce your marks as follows:

  • Any logical/conceptual/formulation plagiarism: zero marks
  • Other form of plagiarism (above 50%): zero marks
  • Otherwise: Percentage of plagiarism will be deducted from the obtained mark