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Welcome to the Seaborn-Tutorial wiki!

Seaborn-Tutorial: Data Visualization is a critical though undermined skill required in pursuit of a Data Science career. This repository is an attempt to help Data Science aspirants gain necessary Data Visualization skills required to progress in their career. It includes all the types of plot offered by Seaborn, applied on random or fabricated datasets. The knowledge gained for inference shall in no way be limited to just Seaborn.

For learners who feel at ease when steps are visually explained, you may check my YouTube channel. You may opt reading for a written/article mode preview on my Medium publication. My algorithms shall try to ensure that these notebooks are well synchronized with video streaming but do not guarantee perfect Speech to Text.

Agenda: With this series of Seaborn notebooks, aspirants shall achieve or be able to upgrade their skills on:

Learn to use Pandas to have a brief overview of dataset. Learn to use various Seaborn plots. Learn to infer the representation of data distribution on any plot. Utilize underlying Matplotlib arguments to tweak Seaborn plots. Statistical interpretation of plotted data. In-depth usage & explanation of each available plotting parameter. Advanced customization as to satisfy complex real-world business problems. Custom codes for enhancing data visualization experience. Series Curriculum: Introduction to Data Visualization Fundamentals Setting up Tools & Resources (Jupyter Notebook) Overview of NumPy and Pandas Elementary Statistical Terms : Part-1, Part-2 and Part-3. Plot styling with Seaborn (With Tableau flavour) Linearly spread Data Plots Categorical Data Plots Visualization on Grids Please note that the content of each Curriculum topic might get segregated into multiple videos on YouTube OR multiple articles on Medium Publication so I would recommend opening it up as a playlist for better experience.

Note: If there is any issue with the code or explanation that you would like me to look into or advice/suggest/recommend, please feel free to reach out. If the content is useful and you would like to copy it completely or partially, feel free to do it but kindly accredit source while doing so. - Alok Kumar. If the content on publication seems well explained, I would really be glad to get notified about your applause on the story.