17CS82 Big Data Analytics syllabus for CS

Hadoop Distributed File System Basics, Running Example Programs and Benchmarks, Hadoop MapReduce Framework, MapReduce Programming

Essential Hadoop Tools, Hadoop YARN Applications, Managing Hadoop with Apache Ambari, Basic Hadoop Administration Procedures

Business Intelligence Concepts and Application, Data Warehousing, Data Mining, Data Visualization

Decision Trees, Regression, Artificial Neural Networks, Cluster Analysis, Association Rule Mining

Text Mining, Naïve-Bayes Analysis, Support Vector Machines, Web Mining, Social Network Analysis

Course outcomes:

The students should be able to:

• Explain the concepts of HDFS and MapReduce framework
• Investigate Hadoop related tools for Big Data Analytics and perform basic Hadoop Administration
• Recognize the role of Business Intelligence, Data warehousing and Visualization in decision making
• Infer the importance of core data mining techniques for data analytics
• Compare and contrast different Text Mining Techniques

Question paper pattern:

• The question paper will have ten questions.
• There will be 2 questions from each module.
• Each question will have questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from each module.

Text Books:

1. Douglas Eadline,"Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem", 1stEdition, Pearson Education, 2016. ISBN-13: 978-9332570351

2. Anil Maheshwari, “Data Analytics”, 1st Edition, McGraw Hill Education, 2017. ISBN-13: 978-9352604180

Reference Books:

1) Tom White, “Hadoop: The Definitive Guide”, 4th Edition, O’Reilly Media, 2015.ISBN-13: 978-9352130672

2) Boris Lublinsky, Kevin T.Smith, Alexey Yakubovich,"Professional Hadoop Solutions", 1stEdition, Wrox Press, 2014ISBN-13: 978-8126551071

3) Eric Sammer,"Hadoop Operations: A Guide for Developers and Administrators",1stEdition, O'Reilly Media, 2012.ISBN-13: 978-9350239261