Each full question will have sub questions covering all the topics under a

Each full question will have sub questions covering

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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
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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.
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