• Title Practical Data Science Cookbook: Data pre-processing, 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
• ISBN-10/ASIN: 1787129624
• ISBN-13: 978-1787129627
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Book Description

If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world 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 real-world 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 step-by-step 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 tree-based methods and Build random forest

Over 85 recipes to help you complete real-world data science projects in R and Python

• 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.

• Amazon
• Amazon (Practical Data Science: A Guide to Building the Technology Stack)

• Data Science
• Python Programming
• R Programming
• Data Analysis and Data Mining, Big Data
• Statistics, Mathematical Statistics, and SAS Programming

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