18CS72: Big Data Analytics IS Syllabus for BE 7th Sem 2018 Scheme VTU

Big Data Analytics detailed Syllabus for Information Science Engineering (IS), 2018 scheme has been taken from the VTUs official website and presented for the VTU students. For Course Code, Subject Names, Teaching Department, Paper Setting Board, Theory Lectures, Tutorial, Practical/Drawing, Duration in Hours, CIE Marks, Total Marks, Credits and other information do visit full semester subjects post given below. The Syllabus PDF files can also be downloaded from the official website of the university.

For all other VTU IS 7th Sem Syllabus for BE 2018 Scheme, do visit VTU IS 7th Sem Syllabus for BE 2018 Scheme Subjects. The detailed Syllabus for big data analytics is as follows.

Course Learning Objectives:

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Module 1

Introduction to Big Data Analytics: Big Data, Scalability and Parallel Processing, Designing Data Architecture, Data Sources, Quality, Pre-Processing and Storing, Data Storage and Analysis, Big Data Analytics Applications and Case Studies. Text book 1: Chapter 1: 1.2 -1.7 RBT: L1, L2, L3

Module 2

Introduction to Hadoop (T1): Introduction, Hadoop and its Ecosystem, Hadoop Distributed File System, MapReduce Framework and Programming Model, Hadoop Yarn, Hadoop Ecosystem Tools. Hadoop Distributed File System Basics (T2): HDFS Design Features, Components, HDFS User Commands. Essential Hadoop Tools (T2): Using Apache Pig, Hive, Sqoop, Flume, Oozie, HBase. -Text book 1: Chapter 2 :2.1-2.6 -Text book 2: Chapter 3 -Text book 2: Chapter 7 (except walk throughs) RBT: L1, L2, L3

Module 3

Module 4

MapReduce, Hive and Pig: Introduction, MapReduce Map Tasks, Reduce Tasks and MapReduce Execution, Composing MapReduce for Calculations and Algorithms, Hive, HiveQL, Pig. -Text book 1: Chapter 4: 4.1-4.6 RBT: L1, L2, L3

Module 5

Machine Learning Algorithms for Big Data Analytics: Introduction, Estimating the relationships, Outliers, Variances, Probability Distributions, and Correlations, Regression analysis, Finding Similar Items, Similarity of Sets and Collaborative Filtering, Frequent Itemsets and Association Rule Mining. Text, Web Content, Link, and Social Network Analytics: Introduction, Text mining, Web Mining, Web Content and Web Usage Analytics, Page Rank, Structure of Web and analyzing a Web Graph, Social Network as Graphs and Social Network Analytics: -Text book 1: Chapter 6: 6.1 to 6.5 -Text book 1: Chapter 9: 9.1 to 9.5

Course Outcomes:

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.

Textbooks:

Raj Kamal and Preeti Saxena, Big Data Analytics Introduction to Hadoop, Spark, and Machine-Learning, McGraw Hill Education, 2018 ISBN: 9789353164966, 9353164966
Douglas Eadline, “Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem”, 1stEdition, Pearson Education, 2016. ISBN-13: 978-9332570351

Reference Books:

For detail Syllabus of all other subjects of BE 7th Sem Information Science Engineering, visit (IS) 7th Sem Syllabus Subjects.

For all (CBSE & Non-CBSC) BE results, visit VTU BE all semester results direct links.

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