ITC801: Big Data Analytics Syllabus for IT 8th Sem 2019 Pattern Mumbai University

Big Data Analytics detailed syllabus scheme for Information Technology (IT), 2019 regulation has been taken from the University of Mumbai 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 all other Mumbai University Information Technology 8th Sem Syllabus 2019 Pattern, do visit IT 8th Sem 2019 Pattern Scheme. The detailed syllabus scheme for big data analytics is as follows.

Big Data Analytics Syllabus for Information Technology BE 8th Sem 2019 Pattern Mumbai University

Course Objectives:

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Course Outcomes:

Student will be able to:

1. Explain the motivation for big data systems and identify the main sources of Big Data in the real world.
2. Demonstrate an ability to use frameworks like Hadoop, NOSQL to efficiently store retrieve and process Big Data for Analytics.
3. Implement several Data Intensive tasks using the Map Reduce Paradigm
4. Apply several newer algorithms for Clustering Classifying and finding associations in Big Data
5. Design algorithms to analyze Big data like streams, Web Graphs and Social Media data.
6. Design and implement successful Recommendation engines for enterprises.

Prerequisites:

Database Management System

Module I

Introduction to Big Data Introduction to Big Data, Big Data characteristics, types of Big Data, Traditional vs. Big Data business approach, Big Data Challenges, Examples of Big Data in Real Life, Big Data Applications 03 CO 1

Module II

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Module III

MapReduce Paradigm MapReduce: The Map Tasks, Grouping by Key, The Reduce Tasks, Combiners, Details of MapReduce Execution, Coping With Node Failures. Algorithms Using MapReduce: Matrix-Vector Multiplication by MapReduce , Relational-Algebra Operations, Computing Selections by MapReduce, Computing Projections by MapReduce, Union, Intersection, and Difference by MapReduce, Computing Natural Join by MapReduce, Grouping and Aggregation by MapReduce, Matrix Multiplication, Matrix Multiplication with One MapReduce Step . Illustrating use of MapReduce with use of real life databases and applications. 09 CO 3

Module IV

Mining Big Data Streams The Stream Data Model: A DataStream-Management System, Examples of Stream Sources, Stream Queries, Issues in Stream Processing. Sampling Data in a Stream : Sampling Techniques. Filtering Streams: The Bloom Filter 07 CO 5 Counting Distinct Elements in a Stream : The Count-Distinct Problem, The Flajolet-Martin Algorithm, Combining Estimates, Space Requirements . Counting Ones in a Window: The Cost of Exact Counts, The Datar-Gionis-Indyk-Motwani Algorithm, Query Answering in the DGIM Algorithm.

Module V

Big Data Mining Algorithms Frequent Pattern Mining : Handling Larger Datasets in Main Memory Basic Algorithm of Park, Chen, and Yu. The SON Algorithm and MapReduce. Clustering Algorithms: CURE Algorithm. Canopy Clustering, Clustering with MapReduce Classification Algorithms: Parallel Decision trees, Overview SVM classifiers, Parallel SVM, K-Nearest Neighbor classifications for Big Data, One Nearest Neighbour. 10 CO 4

Module VI

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier.
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Text Books:

1. Radha Shankarmani, M Vijayalakshmi, Big Data Analytics, Wiley Publications,
2. Anand Rajaraman and Jeff Ullman Mining of Massive Datasets, Cambridge University Press.
3. Alex Holmes Hadoop in Practice, Manning Press, Dreamtech Press.
4. Professional NoSQL Paperback, by Shashank Tiwari, Dreamtech Press
5. MongoDB: The Definitive Guide Paperback, Kristina Chodorow (Author), Michael Dirolf, O’Reilly Publications

Reference Books:

1. Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Bart Baesens , WILEY Big Data Series.
2. Big Data Analytics with R and Hadoop by Vignesh Prajapati Paperback, Packt Publishing Limited
3. Hadoop: The Definitive Guide by Tom White, O’Reilly Publications

Assessment:

Internal Assessment for 20 marks: Consisting of Two Compulsory Class Tests Approximately 40% to 50% of syllabus content must be covered in First test and remaining 40% to 50% of syllabus contents must be covered in second test. End Semester Theory Examination: Some guidelines for setting the question papers are as:

• Weightage of each module in end semester examination is expected to be/will be proportional to number of respective lecture hours mentioned in the syllabus.
• Question paper will comprise of total six questions, each carrying 20 marks.
• Q.1 will be compulsory and should cover maximum contents of the syllabus.
• Remaining question will be mixed in nature (for example if Q.2 has part
1. from module 3 then part
2. will be from any other module. (Randomly selected from all the modules)
• Total four questions need to be solved.

For detail syllabus of all other subjects of Information Technology (IT) 8th Sem 2019 regulation, visit IT 8th Sem Subjects syllabus for 2019 regulation.