CSL4050: Data Visualization (Spring 2025)

Offered in Spring 2025 Semester

Credits

3-0-3 [4.5]
400 Level

Slot A

Mon, Tue, Thu 9:00AM - 9:50AM
Venue: 106 LHB

LMS

https://moodle.iitj.ac.in/
Enrollment Code: 5xwb65

Teaching Assistants
Syllabus

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, Mini Project.
  • We will be using Python Dash, R-Shiny, D3 JS, Grephi in our lab work
Grading Policy
Class Activity Lab Activity Minors Major
15% 30% 15% 40%
  • No ‘A’ grade if score is less than 75%
  • <30% may not get passing grade

Class Activity

  • You will be provided with mini tasks inside classes
    • or before coming to class
  • These will be a quick activity that can be done in very short time

Lab Activity

  • There will be different lab activity including individual and group activity. Lowest 2 marks will be dropped from grading.
  • Each individual/group must report to TA or Me each week with the progress of the lab activity during specific office hours.
  • Please follow the deadlines.
  • No copy paste from friends. Plagiarism policy will be followed!
  • Don’t copy from Github or public repo!
    • If found will be marked zero
Learning Materials

Textbook

  • 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 Policy
As per the institute policy. You are expected to attend 75% of classes. Maximum two weeks of continuous leave. In other words I expect you to see in the classroom for about 28 to 30 classes out of 39. Those taking long leave, for more than 5 classes, please inform me or course TA.
Plagiarism tolerance

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

These policy will apply for any assignments, hands on and practical work. Copying from friends and internet both treated similarly.