Practical Data Science Cookbook - Second Edition

Key Features
Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data
Get beyond the theory and implement real-world projects in data science using R and Python
Easy-to-follow recipes will help you understand and implement the numerical computing concepts
Book Description
As an increasing amount of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data have a competitive advantage over companies that don't, and this drives a higher demand for knowledgeable and competent data professionals.

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. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis-R and Python.

What you will learn
Get to know the installation procedure and environment required for R and Python on various platforms
Implement data science concepts such as acquisition, munging, and analysis through R and Python
Analyze and produce reports on data
Perform some text mining
Build a predictive model and an exploratory model
Build various tree-based methods and Build random forest