Big Data Analytics detailed syllabus scheme for Electronics & Telecommunication Engineering (EC), 2019 regulation has been taken from the MU official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.
For 7th Sem Scheme of Electronics & Telecommunication Engineering (EC), 2019 Pattern, do visit EC 7th Sem Scheme, 2019 Pattern. For the Department Level Optional Course-3 scheme of 7th Sem 2019 regulation, refer to EC 7th Sem Department Level Optional Course-3 Scheme 2019 Pattern. The detail syllabus for big data analytics is as follows.
Big Data Analytics Syllabus for Electronics & Telecommunication Engineering BE 7th Sem 2019 Pattern Mumbai University
Prerequisites:
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Course Objectives:
- To Provide an Overview of an exciting growing field of Big Data Analytics.
- To introduce the tools required to manage and analyze big data like Hadoop, NoSql, Map Reduce.
- To teach the fundamental techniques in achieving big data analytics with scalability and streaming capability.
Course Outcomes:
After successful completion of the course student will be able to
- Understand the key issues in big data management.
- Acquire fundamental enabling techniques using tools in big data analytics.
- Achieve adequate perspectives of big data analytics in various applications like sensor, recommender systems, social media applications etc.
Module 1
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Module 2
Hadoop 06
- Introduction to Hadoop. Core Hadoop Components, Hadoop Ecosystem, Physical Architecture, Hadoop limitations.
Module 3
NoSQL 08
- Introduction to NoSQ, NoSQL business drivers, NoSQL case studies.
- NoSQL data architecture patterns: Key-value stores, Graph stores, Column family (Bigtable) stores, Document stores, Variations of NoSQL architectural patterns.
- Using NoSQL to manage big data: What is a big data NoSQL solution? Understanding the types of big data problems; Analyzing big data with a shared-nothing architecture; Choosing distribution models: master-slave versus peer-to-peer; Four ways that NoSQL systems handle big data problems
Module 4
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Module 5
Techniques in Big Data Analytics 12
- Finding Similar Item: Nearest Neighbor Search, Similarity of Documents
- Mining Data Streams: Data Stream Management Systems, Data Stream Model, Examples of Data Stream Applications: Sensor Networks, Network Traffic Analysis
- Link Analysis: PageRank Definition, Structure of the web, dead ends, Using Page rank in a search engine, Efficient computation of Page Rank: Page Rank Implementation Using MapReduce
- Frequent Itemset Mining : Market-Basket Model, Apriori Algorithm, Algorithm of Park-Chen-Yu
Module 6
Big Data Analytics Applications 08
- Recommendation Systems: Introduction, A Model for Recommendation Systems, Collaborative-Filtering System: Nearest-Neighbor Technique, Example.
- Mining Social-Network Graphs: Social Networks as Graphs, Types of Social-Network. Clustering of Social Graphs: Applying Standard Clustering Techniques, Counting triangles using MapReduce.
Text Books:
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Reference Books:
- Bill Franks , Taming The Big Data Tidal Wave: Finding Opportunities In Huge Data Streams With Advanced Analytics, Wiley
- Chuck Lam, Hadoop in Action, Dreamtech Press
Internal Assessment:
Assessment consists of two class tests of 20 marks each. The first class test is to be conducted when approximately 40% syllabus is completed and second class test when additional 40% syllabus is completed. The average marks of both the test will be considered for final Internal Assessment. Duration of each test shall be of one hour.
End Semester Examination:
- Question paper will comprise of 6 questions, each carrying 20 marks.
- The students need to solve total 4 questions.
- Question No.1 will be compulsory and based on entire syllabus.
- Remaining question (Q.2 to Q.6) will be selected from all the modules.
For detail Syllabus of all subjects of Electronics & Telecommunication Engineering (EC) 7th Sem 2019 regulation, visit EC 7th Sem Subjects of 2019 Pattern.