Course

PROFESSIONAL ELECTIVES

Electives Electives in Data Science

Unit I

Introduction to Data Visualization – Principles – Storytelling with data – Data Visualization tools – Matplotlib – How to Display the plots – Plotting from a script – Adjusting the Plot: Line Colors and Styles – Axes Limits – Labelling Plots – Simple Scatter Plots – Visualizing Errors – Density and Contour Plots – Histograms, Binnings, and Density, Kernel density estimation – Legend – Customizing Colorbars – Choosing the colormap – Sequential colormaps – Divergent colormaps – Qualitative colormaps – Color limits and extensions – Manifold embedding of handwritten digit pixels – Multiple Subplots – Text and Annotation – Transforms and Text Position – Arrows and Annotation – Customizing Ticks – Stylesheets – ggplot – Three-Dimensional Plotting – Contour Plots – Wireframes and Surface Plots – Surface Triangulations

Unit II

Geographic Data with Basemap – Map Projections – Cylindrical projections – Perspective projections – Conic projections – Drawing a Map Background – Plotting Data on Maps – Visualization with Seaborn – Pair plots – Factor plots – histogram as a special case of a factor plot – violin plot.

Unit III

Tableau – Advanced visualizations with Tableau – Choropleth Maps – Waffle Charts – Dashboards – Creating Dashboards with Tableau and Plotly – Data visualization with R – Data Ethics and Visualization Ethics.

Course Objectives

• To provide knowledge on visualization design principles and deciding the type of visualization chart to choose for the given datasets.
• To teach on creating simple to advanced chart types using python modules and libraries.
• To help students explore, visualize and analyse various types of data sets such as geospatial and multimodal data.
• To help students work on visualization tools and enable them to understand the visual analytics such as dashboards and storytelling with a hands-on experience on tableau and R.

Course Outcomes

CO1: Understand the importance of Data Visualization and learn to create basic charts by applying visualization

design principles

CO2: Learn to create advanced visualization charts and analysis.

CO3: Explore and analyse geospatial and multimodal data.

CO4: Learn to build interactive/animated and ethically correct dashboards, construct data stories, and communicate important trends/patterns in the data sets.

CO-PO Mapping

Evaluation Pattern: 70:30

Textbook(s)

Jake VanderPlas, “Python Data Science Handbook – Essential Tools for Working with Data”, O’Reilly, 2^nd Edition, 2022.

Wes McKinney, “Python for Data Analysis”, O’Reilly, 2nd Edition, 2023.

Tamara Munzner, “Visualization Analysis and Design”, A K Peters Visualization Series, CRC Press, 2014.

Reference(s)

Scott Murray,” Interactive Data Visualization for the Web”, O’Reilly, 2013.

Alberto Cairo, “The Functional Art: An Introduction to Information Graphics and Visualization”, New Riders, 2012.

Cole Nussbaumer Knaflic, “Storytelling with Data: A Data Visualization Guide for Business Professionals”, Wiley, 2015.

Nathan Yau, “Visualize This: The Flowing Data Guide to Design, Visualization and Statistics”, John Wiley & Sons, 2011.