Introduction-to-data-visualization

Course description

This course aims to provide a concise introduction to data visualization with an emphasis on utilizing HPC environments. The course will cover fundamental visualization theory, practical examples in python and advanced visualization tools along with example use cases.

Prerequisites

Basic knowledge in python is a prerequisite for a better comprehension of the script sections of this training material.

Course contents

Theory and visual examples

• Introduction to data visualization. -- Visualization, data storage, basic principles of effective data visualization, color theory, types of data, file formats, ethical considerations, examples of data visualization in different fields.

Visualize data using Python

• Data Analysis and Visualization in Python. -- Data cleaning and preprocessing (using numpy), Introduction to Matplotlib, seaborn for statistical data visualization, basic vis techniques(histogram, box plots, scatter plots etc.). Discussion on other libraries like pandas, bokeh, ggplot etc.
• Data Visualization Tools and Techniques. -- Introduction to popular visualization tools., design principles for effective data visualization.
• Visualization techniques for spatial data.
• Visualization technique for geospatial data.
• Visualization techniques for time-series data.
• Visualization techniques for multivariate data.
• Visualization Techniques for Trees, Graphs, and Networks.
• Visualization Techniques for Textual data.
• Interaction Techniques.

Visualization concepts under HPC environments

• Need for HPC Visualization.
• Advanced Visualization Tools and setup for HPC usage.