This course provides hands-on training in modern data visualization and analysis platforms commonly used in scientific computing, engineering research, and data analytics workflows. The course focuses on two widely adopted visualization environments: ParaView and Tableau Public. Participants will develop practical skills for exploring, analyzing, and communicating complex datasets through interactive and high-performance visualization techniques.

The first portion of the course introduces ParaView, an open-source, parallel visualization application widely used in high-performance computing (HPC), computational science, and engineering research communities. Participants will learn how to visualize and analyze large-scale scientific datasets using ParaView’s graphical and scripting capabilities. Topics include data representations, color maps and transfer functions, data filtering, visualization pipelines, multiview configurations, and camera linking techniques.

Using example scientific datasets, including synthetic seismic and vector field data, participants will explore advanced visualization methods such as streamline generation, plot-over-line analysis, histograms, and derived quantity calculations using the calculator tool. Additional topics include time-dependent datasets, animation controls, time interpolation, camera animations, static and dynamic vector field visualization, and automation using Python scripting within ParaView.

The course also demonstrates how to perform remote and parallel visualization workflows using HPE DSI high-performance computing clusters, enabling participants to visualize and analyze large datasets in distributed computing environments.

The second portion of the course focuses on Tableau Public and modern interactive data analytics workflows. Participants will learn how to create interactive charts, maps, dashboards, and web-based visualizations for exploratory data analysis and presentation. The course introduces core Tableau concepts, including data connections, worksheet design, dashboard construction, and interactive filtering techniques.

Participants will also work with more advanced Tableau features, including calculated fields, parameters, data management tools, and interactive dashboard controls designed to enhance user engagement and analytical insight. By the conclusion of the workshop, participants will publish their visualizations to the Tableau Public platform for web-based sharing and presentation.

Upon successful completion of the course, participants will be able to:

• Visualize and analyze scientific and engineering datasets using ParaView
• Construct visualization pipelines and apply data filters effectively
• Create animations and time-dependent visualizations for scientific datasets
• Use Python scripting to automate visualization workflows in ParaView
• Perform remote and parallel visualization using HPC resources
• Design interactive dashboards and data visualizations using Tableau Public
• Create charts, maps, and web-based analytical dashboards
• Apply calculations, parameters, and interactive controls to improve data exploration
• Publish and share interactive visualizations online