Course

Discipline Specific Electives: Business Analytics

Unit 1

Introduction to data visualization – value of visualization – what visualization is and why it is done: External representation – interactivity – difficulty in validation. Data abstraction: dataset types – attribute types – semantics. Task abstraction – analyze, produce, search, query. Four levels of validation – validation approached – validation examples. Marks and channels

Unit 2

Design principles: Categorical, time series, and statistical data graphics. Multivariate displays. Data for data graphics.

Unit 3

Rules of thumb – Arrange tables: Geospatial displays – Visualization of Spatial Data, Networks, and Trees. 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.

Unit 4

Manipulate view: change view over time – Select elements – changing viewpoint – reducing attributes. Fact 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. Dashboards, interactive displays.

Objective:

This course helps students understand different techniques in Data Visualization. Course Outcome

CO1: Differentiate between the types of charts and plots used for data visualization. CO2: Perform exploratory data analysis using Pandas and Matplotlib.

CO3: Perform exploratory data analysis using Seaborn. CO4: Create a basic dashboard using Plotly and Dash. CO5: Generate various types of charts in Tableau.

References Textbook:

• • Tamara Munzer “Visualization Analysis and Design, A K Peters Visualization Series, CRC Press,
  • Igor Milovanovic, “Python Data Visualization Cookbook”, Packt Publications

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