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Course Detail

Course Name Data Visualization
Course Code 26CSC213
Program 5 Year Integrated M.Sc in Data Science
Semester 4
Credits 4
Campus Coimbatore

Syllabus

Unit 1

Introduction to Data Visualization – Classification of Visualization techniques – Structure and representation – Selection of a Visualization – Visualizations for high dimensional data – Graphics and computing.

Principles of Data Visualization: Multivariate data – Linked data – Visualizing trees and forests – Large Datasets – Plots and their variates – Visualizing cluster analysis – contingency tables – finite mixture models.

Methodologies: Visualization in Bayesian data analysis – Matrix visualization – Data visualization by kernel machines. Applications: Visualization for genetic network reconstruction, medical images, financial dataset and Insurance risk processes.

Tableau: Introduction to Tableau – Advanced visualizations with Tableau – Choropleth Maps – Waffle Charts – Dashboards – Creating Dashboards with Tableau and Plotly. Introduction to POWER BI -Power BI for interactive Analytics-Case studies.

Suggested lab exercises:

  1. Visualization using Python libraries and creation of basic charts.
  2. Multivariate data visualization using plots and their variates.
  3. Interactive exploration of linked data and large dataset with visualization techniques.
  4. Plotting hierarchical trees and decision forests and visualization of clusters.
  5. Correlation and distance matrices with heatmaps.
  6. Plotting Bayesian data analytical results.
  7. Visualizing genetic network and medical image data.
  8. Risk analysis plots for financial dataset and insurance risk
  9. Interactive Dashboard Building with filters, sliders, and dynamic plots.
  10. Choropleth Maps and Waffle Charts in Tableau.
  11. Importing and visualizing data using Power BI.
  12. Case study: End-to-end data visualization pipeline using any one tool.
Text Books / References

Text Books

  1. Claus Wilke, Fundamentals of Data Visualization – A Primer on Making Informative and Compelling Figures, O’Reilly Media Publisher, 2019.
  2. Jeffrey D. Camm, James J. Cochran, Michael J. Fry, and Jeffrey W. Ohlmann, Data Visualization Exploring and Explaining with Data 2021.
  3. Usama Fayyad, Georges G. Grinstein and Andreas Wierse, “Information visualization in Data Mining and Knowledge discovery”, Morgan Kaufmann publishers, 2002.

References

  1. Kieran Healy, Data Visualization a Practical Introduction, Princeton University Press, 2018.
  2. Chun-houh Chen, Wolfgang Hardle and Antony Unwin, “Handbook of Data Visualization”, Springer, 2008.
  3. Tableau for Dummies by Molly Monsey and Paul Sochan
  4. Elias Dabbas, “Interactive Dashboards and Data Apps with Plotly and Dash”, Packt Publishing, 2021.
  5. Jeremey Arnold, “Learning Microsoft Power BI: Transforming Data into Insights”, O’Reilly Media, 2022.5.
  6. Colin Ware, “Information Visualization: Perception for Design”, Morgan Kaufmann / Elsevier, 4th Ed., 2021.
  7. Scott Murray, “Interactive Data Visualization for the Web”, O’Reilly Media, 2nd Ed., 2017.

Introduction

This course explores the principles of transforming raw data into meaningful visual representations. Topics include fundamental visualization types, data structures, and advanced techniques for multivariate and high-dimensional data. The practical component covers real-world dataset analysis and interactive dashboard creation using Tableau, Plotly, and Power BI.

Objectives and Outcomes

Course Outcomes (COs)): After successful completion of the course, students will be able to

CO1 Understand and explain different types of data visualization techniques, their classification, and structural representations.
CO2 Apply principles of multivariate and linked data visualization using plots, cluster analysis, and contingency tables.
CO3 Use visualization methodologies such as Bayesian data analysis, matrix visualization, and kernel machines on given datasets.
CO4 Analyse and interpret domain-specific data from genetic networks, medical images, financial records, and insurance datasets through visualization.
CO5 Build interactive dashboards and data stories using Tableau, Plotly, and Power BI for real-world analytical use cases.

CO-PO Mapping

  PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12
CO1 3 1             1 2   2
CO2 3 3 3   2 2     2 2   3
CO3 3 3 3 2 2 2     2 2   3
CO4 3 3 3 3 3 2   2 3 3 2 3
CO5 3 2 3 2 3 3     3 3 3 3

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