2CSE60E13: Big Data Analytics

Learning Outcomes: 
Upon Completion of the course, the students will be able to
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
Syllabus: 
Unit NoTopics
Overview of big data analytics

Introduction to big data, Big data analytics applications

Technologies and tools for big data analytics

Introduction to MapReduce/Hadoop, Data analytics using MapReduce/Hadoop, Data visualization techniques,Spark

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

Case studies
Reference Books: 
Name: 
Mining of Massive Datasets
Author: 
Anand Rajaraman and Jeffrey David Ullman
Publication: 
Cambridge University Press, 2011
Name: 
Scaling up Machine Learning: Parallel and Distributed Approaches
Author: 
Mikhail Bilenko and John Langford
Publication: 
Cambridge University Press, 2011
Name: 
THadoop: The Definitive Guide
Author: 
Tom White
Publication: 
O‟Reilly Media
Edition: 
Third Edition
Name: 
Big Data Analytics: Turning Big Data into Big Money
Author: 
Frank J. Ohlhorst
Publication: 
Wiley, 2012.
Name: 
Big Data Analytics: Disruptive Technologies for Changing the Game
Author: 
Arvind Sathi
Publication: 
MC Press, 2012
Syllabus PDF: 
AttachmentSize
PDF icon ELECTIVE IV (BDA) .pdf187.33 KB
branch: 
BDA
Course: 
2014
2016
Stream: 
B.Tech