Master Data Analysis with Python
By
Ted Petrou
Master Data Analysis with Python is a comprehensive text dedicated to help you completely master data analysis and visualization using python. It is divided into 10 parts:
The goal for Master Data Analysis is to be the absolute best possible text to learn how to use the data analysis libraries available in Python to explore and understand data.
The primary Python library used during the book is pandas. Visualization will be handled by the matplotlib and seaborn libraries, both of which are covered in great detail. The best practices from the very latest versions of the libraries are used.
This book assumes you already have a solid understanding of the basics of Python. If you do not, you should master these fundamentals first. Exercise Python provides the necessary prerequisite knowledge.
This book assumes no knowledge of any of the Python data science libraries. Each part progresses slowly beginning with the basics and ending with more advanced topics in the later chapters.
All the material is contained within Jupyter Notebooks. This allows you to open a notebook, read through the material, run the code, and then answer the exercises all within a single environment.
The text is also available as a single PDF file totaling over 500 pages.
- Environment Setup and Jupyter Notebooks
- Intro to Pandas
- Selecting Subsets of Data
- Essential Commands
- Grouping Data
- Time Series
- Regular Expressions
- Tidy Data
- Joining Data
- Visualization
About the Author
Master Data Analysis with Python is written by Ted Petrou, author of the highly rated text Pandas Cookbook. Ted has spent the last 3 years teaching data science in-person using Python to hundreds of students and sees first hand exactly where students struggle. He has continually upgraded his material to minimize these struggles by providing simple and direct paths forward.The goal for Master Data Analysis is to be the absolute best possible text to learn how to use the data analysis libraries available in Python to explore and understand data.
Additional Information
The book will receive continuous updates that you will have access to through at least 2020.The primary Python library used during the book is pandas. Visualization will be handled by the matplotlib and seaborn libraries, both of which are covered in great detail. The best practices from the very latest versions of the libraries are used.
This book assumes you already have a solid understanding of the basics of Python. If you do not, you should master these fundamentals first. Exercise Python provides the necessary prerequisite knowledge.
This book assumes no knowledge of any of the Python data science libraries. Each part progresses slowly beginning with the basics and ending with more advanced topics in the later chapters.
All the material is contained within Jupyter Notebooks. This allows you to open a notebook, read through the material, run the code, and then answer the exercises all within a single environment.
The text is also available as a single PDF file totaling over 500 pages.
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