Course Objectives

• Become acquainted with the use of R tool for Data Science applications.
• Acquire experience in analyzing data using R.
• Develop the skills to use the software for pre–analytic phase data handling operations.
• Learn various methods of using Hadoop and R together
• Understand how to write Interpretation and do decision making

UNIT I Introduction to Data Science

Introduction to Data Science – Basic concepts – Data – Nature – Process for Data Science – Handling Data

UNIT II R and its applications

R software – core and optional packages – Data science packages – Exploratory Analytics using R –Visualizing Data– Applications

UNIT III Pre- Processing

Pre–processing Data with R – Scrapping– sampling – munging – cleaning – data from multiple sources – extraction from data bases

UNIT IV Big Data in R

Handling Big Data in R – Hadoop and R – New frameworks – Mapreduce with R – Organizing Data Sources

UNIT V Automation

Automation of Data Analytics – considerations – organizing for Data Science –Interpreting and Decision making

Learning Resources

• James (JD) Long (2019),“R Cook book”-2nd Edition-O’Reilly Media Inc.
• Wiktorski, Tomasz (2019),”Data-intensive Systems-Principles and Fundamentals using Hadoop and Spark”-Springer.
• Andrew Olesky (2018), “Data Science with R: A Step By Step Guide with Visual Illustrations & Examples”, Kindle Edition.
• Hadley Wickham, ‎Garrett Grolemund (2017), “R for Data Science: Import, Tidy, Transform, Visualize, and Model Data”, Oreilly.
• Thomas Mailund (2017), “Beginning Data Science in R: Data Analysis, Visualization, and Modeling for the Data