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Course Detail

Course Name Data Visualization
Course Code 24AI738
Program M. Tech. in Artificial Intelligence
Credits 3
Campus Amritapuri ,Coimbatore

Syllabus

Value of Visualization – What is Visualization and Why do it: External representation – Interactivity – Difficulty in Validation. Data Abstraction: Dataset types – Attribute types – Semantics. Task Abstraction – Analyze, Produce, Search, Query. Four levels of validation – Validation approaches – Validation examples. Marks and Channels

Rules of thumb – Arrange tables: Categorical regions – Spatial axis orientation – Spatial layout density. Arrange spatial data: Geometry – Scalar fields – Vector fields – Tensor fields. Arrange networks and trees: Connections, Matrix views – Containment. Map color: Color theory, Color maps and other channels.

Manipulate view: Change view over time – Select elements – Changing viewpoint – Reducing attributes. Facet into multiple views: Juxtapose and Coordinate views – Partition into views – Static and Dynamic layers – Reduce items and attributes: Filter – Aggregate. Focus and context: Elide – Superimpose – Distort, Case Studies using Tableau/Qlikview – Tabular Data – Graphs – Networks – Trees – Spatial Data – Text/Logs – Time Series Complex Combinations.

Objectives and Outcomes

Preamble

Data visualization is an essential aspect of the data science portfolio and finds application across diverse disciplines which use visualization techniques to explore and present data. This course lays a road map to data-driven storytelling by focusing on the principles, methods, and techniques of scientific visualization that help to create powerful and engaging visuals, tailored to the needs of diverse stakeholders.

 

Course Objectives

  • To understand the important role of visualization in the analysis of data.
  • To apply data visualization best practices to choose the appropriate visualization tailored to the needs of the audience.
  • To learn some of the latest tools and software to produce effective visuals that capture the stories within the data

 

Course Outcomes

 

COs

Description

CO1

Understand and explain the key techniques used in data visualization

CO2

Apply effective visualizations to explore and analyze input data

CO3

Present the insights and findings in engaging formats that produce compelling stories

CO4

Evaluate data visualization systems for their effectiveness

CO5

Design and build data visualization systems following the best practices using popular software tools for visualization such as Tableau/QlikView

 

Prerequisites

  • Basic Data Science.

CO-PO Mapping

 

COs

Description

PO1

PO2

PO3

PO4

PO5

CO1

Understand the key techniques and theory used in visualization of data

3

CO2

Apply effective visualizations to explore and analyze input data

3

1

CO3

Present the insights and findings in engaging formats that produce compelling stories

3

1

CO4

Evaluate data visualization systems for their effectiveness

3

2

CO5

Design and build data visualization systems following the best practices using popular software tools for visualization such as Tableau/QlikView

3

3

3

3

2

Evaluation Pattern

Evaluation Pattern – 70:30

 

  • Midterm Exam – 20%
  • Lab Assignments – 25%
  • Project – 25%
  • End Semester Exam – 30%

Text Books / References

Text Book / References

  1. Tamara Munzner, ”Visualization Analysis and Design”, A K Peters Visualization Series, CRC Press, 2014.
  2. Claus O. Wilke, ”Fundamentals of Data Visualization: A primer for making informative and compelling figures”, O’Reilly, 2019.
  3. Kieran Healy, ”Data Visualization: A Practical Introduction”, Princeton University Press, 2019.
  4. Andy Kirk, ”Data Visualization, A Handbook for Data Driven Design”, 2nd ed, Sage Publi- cations, 2019.
  5. Nathan Yau, ”Visualize This: The Flowing Data Guide to Design, Visualization and Statistics”, John Wiley & Sons, 2011.
  6. Daniel Murray, ”Tableau Your Data!: Fast and Easy Visual Analysis with Tableau Software”, Wiley, 2016.

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