18001025661 / 8527794500
info@sgtuniversity.org

Syllabus | B.Tech-Computer Science & Engineering | Big Data Analytics

 
 
Big Data Analytics Learning Schedule
L T P C
Pre-requisites: Java 3 0 0 3

COURSE OUTCOMES:

  • Identify and distinguish big data analytics applications  Describe big data analytics tools
  •  Explain big data analytics techniques
  •  Present cases involving big data analytics in solving practical problems
  •  Conduct big data analytics using system tools
  •  Suggest appropriate solutions to big data analytics problems. 

COURSE CONTENTS

UNIT -I

Overview of big data analytics Introduction to big data, Big data analytics applications

UNIT -II

Technologies and tools for big data analytics Introduction to MapReduce/Hadoop

Data analytics using MapReduce/Hadoop

Data visualization techniques, Spark

UNIT -III

Theory and methods for big data analytics Selected machine learning and data mining methods (such as support vector machine and logistic regression), Statistical analysis techniques (such as conjoint analysis and correlation analysis), Time series analysis D. Big data graph analytics .

UNIT -IV

Case-Studies 

REFERENCE BOOKS:

 

  1. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press, 2011. Ron Bekkerman, Mikhail Bilenko and John Langford, Scaling up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, 2011.
  2. Tom White, Hadoop: The Definitive Guide, O‟Reilly Media, Third Edition, 2012.
  3. Bill Franks, Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics, Wiley, 2012.
  4. Michael Minelli, Michele Chambers, and Ambiga Dhiraj, Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses, Wiley, 2013.
  5. Frank J. Ohlhorst, Big Data Analytics: Turning Big Data into Big Money, Wiley, 2012.
  6. Arvind Sathi, Big Data Analytics: Disruptive Technologies for Changing the Game, MC Press, 2012.

 

 

ADMISSIONS 2021