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 Title Practical Data Science Cookbook: Data preprocessing, analysis and visualization using R and Python
 Author(s) Prabhanjan Tattar, Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta
 Publisher: Packt Publishing, 2nd Revised edition edition (June 29, 2017)
 Hardcover/Paperback N/A
 eBook PDF
 Language: English
 ISBN10/ASIN: 1787129624
 ISBN13: 9781787129627
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Book Description
If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through handson, realworld project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of realworld data science projects and the programming examples in R and Python.
Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a stepbystep format.
 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 treebased methods and Build random forest
Over 85 recipes to help you complete realworld data science projects in R and Python
About the Authors Prabhanjan Tattar has 9 years of experience as a statistical analyst. His main thurst has been to explain statistical and machine learning techniques through elegant programming which will clear the nuances of the underlying mathematics.
 Data Science
 Python Programming
 R Programming
 Data Analysis and Data Mining, Big Data
 Statistics, Mathematical Statistics, and SAS Programming




















