The art of effective visualization of multi-dimensional data - A hands-on approach

Dipanjan Sarkar (~dipanjan)


3

Votes

Description:

The talk is usually a 90 minutes session but we will be covering it in the scheduled 30 minute session focusing on the main aspects of effective data visualization with the grammar of graphics, leveraging popular open-source frameworks in Python and also as a bonus cover visualization in unstructured data including text, audio and images.

Note: All the code and resources will be shared and open-sourced for your benefit! So you don't need to take extensive notes and can focus on the presentation\talk.

Outline:

  • Introduction
    • What is Data Visualization?
    • Why Data Visualization?
  • Motivation
    • Why Effective Data Visualization
  • Effective Multi-dimensional Data Visualization
    • Whirlwind tour of the grammar of graphics
  • Visualization tools and frameworks
    • General tools & frameworks
    • Python visualization frameworks
    • R visualization frameworks
  • Visualizing Structured Data
    • Univariate analysis and visualizations
    • Multivariate analysis and visualizations
    • Visualizing from 1-D up to 6-D
  • BONUS: Visualizing Unstructured Data
    • Text
    • Images
    • Audio
  • Final words

Learning Outcome

  • Take a glance at the major data visulization frameworks
  • Get a clear understanding of univariate and multi-variate visualization
  • Learn effective strategies for visualizing data using the grammar of graphics
  • Get a clear perspective on which visualization techniques work best based on specific scenarios
  • Strategies for visualizing structured and unstructured data with actual examples

Prerequisites:

Basics of Python, data terminology (rows, columns, feature, data points, data types) helps but we will be covering briefly during the session. Hence it's not essential.

Content URLs:

This talk will be based on my article on Towards Data Science

The hands-on examples have also been open-sourced on GitHub

Speaker Info:

Dipanjan Sarkar is a Data Scientist at Intel, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems. He holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering. He is also an avid supporter of self-learning.

Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, statistical methods and deep learning. Having a passion for data science and education, he is a Data Science Mentor at Springboard, helping people up-skill on areas like Data Science and Machine Learning. He also acts as a contributor and editor for Towards Data Science, a leading online journal focusing on Artificial Intelligence and Data Science. Dipanjan has also authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing & Deep Learning.

More about me:

LinkedIn: https://www.linkedin.com/in/dipanzan/

GitHub: https://github.com/dipanjanS

Speaker Links:

LinkedIn: https://www.linkedin.com/in/dipanzan/

Blog Posts: https://towardsdatascience.com/@dipanzan.sarkar

GitHub: https://github.com/dipanjanS

Featured stories on KDnuggets: https://www.kdnuggets.com/?s=dipanjan+sarkar

Recent books:-

https://www.springer.com/us/book/9781484223871

https://www.springer.com/us/book/9781484232064

https://www.packtpub.com/big-data-and-business-intelligence/hands-transfer-learning-python

Section: Data science
Type: Talks
Target Audience: Beginner
Last Updated:

Cool. I have read your articles and they are very informative and deep. The challenge is to compress all the information into a 45 minutes talk which is engaging.

Anand B Pillai (~pythonhacker)

Thanks Anand, I think that should be possible definitely and I would plan to cover the same with examples also showcasing both structured and unstructured data viz (like text).

Dipanjan Sarkar (~dipanjan)

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