Data Science

Updated: Aug 4, 2019

Practical Data Science Cookbook – Second Edition by Prabhanjan Tattar, Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort & Abhijit Dasgupta

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What you will learn:

Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest

Python Data Science Cookbook – Gopi Subramanian

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Python Data Science CookbookDownload

What you will learn:

This book is intended for all levels of data science professionals, both students and practitioners from novice to experts. Different recipes in the chapters cater to the needs of different audiences. Novice readers can spend some time in getting themselves acquainted with data science in the first five chapters. Experts can refer to the later chapters to refer/understand how advanced techniques are implemented using Python. The book covers just enough mathematics and provides the necessary references for computer programmers who wish to understand data science. People from a non-Python background can effectively use this book. The first chapter of the book introduces Python as a programming language for data science. It will be helpful if you have some prior basic programming experience. The book is mostly self-contained and introduces data science to a new reader and can help him become an expert in this trade.

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