Each full question will have sub questions covering

• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from each module.
Textbooks:
1. Tom M Mitchell, Machine Lerning, McGraw Hill Education Pvt Ltd., Chennali.
2. Elaine Rich, Kevin K and S B Nair, Artificial Inteligence, 3
rd
Ed, McGraw Hill Education Pvt
Ltd., Chennali.
Reference Books:
1. Stuart Rusell, Peter Norving , Artificial Intelligence: A Modern Approach, Pearson Education
2nd Edition
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, h The Elements of Statistical Learning, 2nd
edition, springer series in statistics.
3. Ethem Alpaydın, Introduction to machine learning, second edition, MIT press

BIG DATA AND ANALYTICS
(Effective from the academic year 2018 -2019)
SEMESTER – VII
Subject Code 18CS72 CIE Marks 40
Number of Contact Hours/Week 4:0:0 SEE Marks 60
Total Number of Contact Hours
50
Exam Hours
3 Hrs
CREDITS –4
Course Learning Objectives: This course (18CS72) will enable students to:
• Understand Hadoop Distributed File system and examine MapReduce Programming
• Explore Hadoop tools and manage Hadoop with Ambari
• Appraise the role of Business intelligence and its applications across industries
• Assess core data mining techniques for data analytics
• Identify various Text Mining techniques
Module 1 Contact
Hours
Hadoop Distributed File System Basics, Running Example Programs and Benchmarks,
Hadoop MapReduce Framework, MapReduce Programming
10
Module 2
Essential Hadoop Tools, Hadoop YARN Applications, Managing Hadoop with Apache
Ambari, Basic Hadoop Administration Procedures
10
Module 3
Business Intelligence Concepts and Application, Data Warehousing, Data Mining, Data
Visualization
10
Module 4
Decision Trees, Regression, Artificial Neural Networks, Cluster Analysis, Association Rule
Mining
10
Module 5
Text Mining, Naïve-Bayes Analysis, Support Vector Machines, Web Mining, Social Network
Analysis
10
Course Outcomes: The student will be able to :
• Master 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.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from each module.