10 hours course outcomes after studying this course

This preview shows page 123 - 126 out of 138 pages.

10 Hours Course Outcomes: After studying this course, students will be able to Interpret the impact and challenges posed by IoT networks leading to new architectural models. Compare and contrast the deployment of smart objects and the technologies to connect them to network.
Appraise the role of IoT protocols for efficient network communication. Elaborate the need for Data Analytics and Security in IoT. Illustrate different sensor technologies for sensing real world entities and identify the applications of IoT in Industry. 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. David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Robert Barton, Jerome Henry, "IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things”, 1 st Edition, Pearson Education (Cisco Press Indian Reprint). ( ISBN: 978- 9386873743) 2. Srinivasa K G, “Internet of Things”, CENGAGE Leaning India, 2017 Reference Books: 1. Vijay Madisetti and ArshdeepBahga, “Internet of Things (A Hands-on-Approach)”, 1 st Edition, VPT, 2014. ( ISBN: 978-8173719547) 2. Raj Kamal, “Internet of Things: Architecture and Design Principles”, 1 st Edition, McGraw Hill Education, 2017. ( ISBN: 978-9352605224)
BIG DATA ANALYTICS [As per Choice Based Credit System (CBCS) scheme] (Effective from the academic year 2017 - 2018) SEMESTER – VIII Subject Code 17CS82 IA Marks 40 Number of Lecture Hours/Week 4 Exam Marks 60 Total Number of Lecture Hours 50 Exam Hours 03 CREDITS – 04 Module – 1 Teaching Hours Hadoop Distributed File System Basics, Running Example Programs and Benchmarks, Hadoop MapReduce Framework, MapReduce Programming 10 Hours Module – 2 Essential Hadoop Tools, Hadoop YARN Applications, Managing Hadoop with Apache Ambari, Basic Hadoop Administration Procedures 10 Hours Module – 3 Business Intelligence Concepts and Application, Data Warehousing, Data Mining, Data Visualization 10 Hours Module – 4 Decision Trees, Regression, Artificial Neural Networks, Cluster Analysis, Association Rule Mining 10 Hours Module – 5 Text Mining, Naïve-Bayes Analysis, Support Vector Machines, Web Mining, Social Network Analysis 10 Hours 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.

  • Left Quote Icon

    Student Picture

  • Left Quote Icon

    Student Picture

  • Left Quote Icon

    Student Picture