Practical Data Science Cookbook, Second Edition

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

• 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

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.

• 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

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract