Syllabus

See also: 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... · GradingGrading
See also: Syllabus · Lectures

Data Visualization Course Grading Plan

Assessment and Grading Breakdown

Your performance in this course will be evaluated across four components, each desig...

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, experience and enable.

Contact Hours:

  • Lecture (L): 1
  • Tutorial (T): 0
  • Studio (S): 6

Studio Hours: 4 Examination Duration (Hrs):

  • CWS: 10
  • STS: 40
  • MTE: 10
  • ETE: 20
  • STE: 20

Credit: 4 Semester: 6th

Course Syllabus

S. No. Contents Contact Weeks
1 Importance of data visualization; Data and image models; Visualization Design 3
2 Color; Space; Data Analysis; Multi-Dimensional data; Graphical Perception 4
3 Visualization Software; Interactive Visualization; Animation in Visualization 4
4 Mapping and cartography; Narrative; Text Visualization 3
Total   14

Detailed Structure

Data and Information

  • Data, Information, Knowledge, Wisdom
  • Exercise: 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...
  • What can we do with data?
  • Exhibit, Explain, Explore, Experience, Enable
  • Data to Information
  • Forming insights
  • Levels of Measurement
  • Conceptual v/s Measurable Data
  • Infographics
  • What separates illustrations from infographics?

    Types of Data

  • Exercise: Exercise - PIN PointExercise - PIN Point
    Dataset

    Download this dataset of synthetic PIN card numbers from Kaggle. Open the .csv file in either a text editor or Microsoft Excel or Google Sheets to see the values.

    PIN Data | Kaggle

    Are y...
  • Structured data (Tables)
  • Semi-structured data
  • Unstructured data (Longform text, images, audio, film)

    Exploring Structured data

  • Dimensions in data
  • Basic Stats
  • Spreadsheets for exploring data
  • Exercise: Exercise - Explore a DatasetExercise - Explore a Dataset
    Instructions

    Find a dataset in an area that you are interested in. For the purpose of this exercise, avoid text heavy datasets and image datasets. Make sure there are at least 4 features in the da...
  • Exercise: Exercise - Explore a DatasetExercise - Explore a Dataset
    Instructions

    Find a dataset in an area that you are interested in. For the purpose of this exercise, avoid text heavy datasets and image datasets. Make sure there are at least 4 features in the da...     with AI!
  • Identifying outliers and trends
  • Simple visualisations inside spreadsheets
  • Measures of central tendency - mean, median, mode
  • Frequency
  • Exploratory Data Analysis - simple charts to uncover trends

    Text as Data

  • Qualitative vs. quantitative aspects (word counts, context, sentiment).
  • Challenges of analyzing unstructured text.
  • Word clouds, phrase nets, topic modeling visualizations
  • Leveraging NLP (Natural Language Processing) techniques for deeper insights.

    Images as Data

  • Raster and vector images
  • What are images made of?
  • Time spent on campus - Rasagy's map of entry, exit
  • Sky maps over covid
  • Data models - Films as data
  • Visualizing 500 Days of Summer by @rasagy
  • Visualization of movies - Part II - The Batman Trilogy

Marks & Channels

  • Encoding Data into Visuals
  • Marks and ChannelsMarks and Channels
    Marks

    Marks are the the basic geometries, or graphical elements, in a plot that depict our data items or their linkages.


    Points
    Lines
    Area
    Volume
    Containment
    Connection
    Channels
    ...
  • Picking the right visualisation type
  • Exercise: 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...
  • Common mistakes WTF Visualizations · Data to Viz Caveats

    Graphical Perception

  • Data-ink ratio
  • Role of aesthetics, clarity, and function in effective visuals.
  • Gestalt principles
  • Minimizing visual clutter and maximizing information density
  • Contrast, Alignment, Repetition, Proximity
  • Accuracy of Different Chart Types - Cleveland and McGill studies on how people interpret lengths, angles, and areas.
  • Visual Encoding: Color, Shape, Size
  • Color wheels, complementary, analogous, triadic color schemes.
  • Color for cartography
  • Perceptual aspects of color (simultaneous contrast, color blindness considerations).
  • Visual Design: Typography, Spacing, Emphasis
  • Plane graphic
  • Negative space

    Types of Charts

  • From Data to Viz
  • Data Viz Project
  • Visual Vocabulary Poster - Financial Times
  • Visual variables sheet - Data Viz Handbook

Making Good Charts

  • Tufte: Data-ink ratio

  • Ben Schneiderman’s Visualization Mantra: “Overview first, zoom and filter, then details on demand.”

  • How structure influences the visualization form
  • tree diagrams, node-link diagrams
  • Bar, line, pie, scatter plots, etc.

Lying with Data // Data and Truth

  • Zomato rider payout
  • Correlation causation
  • Alberto Cairo - How Charts Lie
  • What can be measured? Literacy rate - what is literate?
  • Rich people by state - tax returns >1cr
  • Ethics in Data Visualisation
  • Photo quiz: This is an experiment about how we view history
  • Data bias - where can it come from
  • Histories - data x design, staged photographs
  • Misleading graphics
  • Common fallacies
  • Spurious correlations

Storytelling with data

Geospatial visualisations