Big Data & Analytics

Learning Outcomes: 
Students will be able to:
• Demonstrate knowledge of statistical data analysis techniques used in decision making
• Apply principles of Data Science to the analysis of large-scale problems
• Use data mining software to solve real-world problems
• Employ cutting edge tools and technologies to analyse Big Data
Syllabus: 
Unit NoTopics
1

Introduction To Big Data:

 Big Data and its Importance, Four V’s of Big Data, Drivers for Big Data, Introduction to Big Data Analytics, Big Data Analytics applications. 

2

Big Data Technologies:

Hadoop’s Parallel World, Data discovery, Open source technology for Big Data Analytics, cloud and Big Data, Predictive Analytics, Mobile Business Intelligence and Big Data, Crowd Sourcing Analytics, Inter- and Trans-Firewall Analytics, Information Management.

3

Processing Big Data:

 Integrating disparate data stores, Mapping data to the programming framework, Connecting and extracting data from storage, Transforming data for processing, subdividing data in preparation for Hadoop Map Reduce.

4

HadoopMapreduce:

 Employing Hadoop Map Reduce, Creating the components of Hadoop Map Reduce jobs, Distributing data processing across server farms, Executing Hadoop Map Reduce jobs, monitoring the progress of job flows, The Building Blocks of Hadoop Map Reduce Distinguishing Hadoop daemons, Investigating the Hadoop Distributed File System Selecting appropriate execution modes: local, pseudo-distributed, fully distributed.  

5

Big Data Tools And Techniques:

 Installing and Running Pig, Comparison with Databases, Pig Latin, User- Define Functions, Data Processing Operators, Installing and Running Hive, Hive QL, Querying Data, SQOOP, OOZIE,User-Defined Functions, Oracle Big Data..

Reference Books: 
Name: 
Cookbook for big data
Publication: 
Packet publication
Syllabus PDF: 
AttachmentSize
PDF icon BDA.pdf210.87 KB
branch: 
CBA
Course: 
2016
Stream: 
M.Tech