To all voracious readers, here is an ideal list of books to have in bookshelf of every data scientist and analyst. These books cover a wide range of topics and perspective, which would help to become a well-rounded data scientist and analyst. Here are a few:

• R Cookbook by Paul Teetor

• Python for Data Analysis by Wes McKinney 

• Web Analytics: An Hour a Day by Avinash Kaushik 

• Web Analytics 2.0 by Avinash Kaushik 

• How to Measure Social Media: A Step-By-Step Guide to Developing and Assessing Social Media ROI by Nichole Kelly

• The Visual Display of Quantitative Information by Edward R. Tufte

• Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics by Nathan Yau

• The Wall Street Journal Guide to Information Graphics: The Dos & Don’ts of Presenting Data, Facts, & Figures by Dona M. Wong

• Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few

Read more at https://www.analyticsvidhya.com/blog/2015/06/infographic-read-books-analytics-data-science/

R Programming is used for analyzing data as well as for the prediction of data. Using data models are made to further do analytical analysis. Models once made are then further predicted  using the function predict(). The article further explains the method of prediction using an example. Read more at: 

http://www.dummies.com/programming/r/how-to-predict-new-data-values-with-r/