Data Analytics (CS-503)
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B.Tech RGPV notes AICTE flexible curricula Bachelor of technology
Syllabus
UNIT 1:
DESCRIPTIVE STATISTICS :Probability Distributions, Inferential Statistics ,Inferential
Statistics through hypothesis tests Regression & ANOVA ,Regression ANOVA(Analysis of
Variance)
UNIT 2:
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.
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.
UNIT 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.
UNIT 4:
HADOOP MAPREDUCE: 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.
UNIT 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, User-Defined Functions, Oracle Big Data.
NOTES
- Unit 1
- Unit 2
- Unit 3
- Unit 4
- Unit 5
Books Recommended
1. Michael Minelli, Michehe Chambers, “Big Data, Big Analytics: Emerging Business
Intelligence and Analytic Trends for Today’s Business”, 1st Edition, Ambiga Dhiraj, Wiely
CIO Series, 2013.
2. Arvind Sathi, “Big Data Analytics: Disruptive Technologies for Changing the Game”, 1st
Edition, IBM Corporation, 2012.1. Rajaraman, A., Ullman, J. D., Mining of Massive
Datasets, Cambridge University Press, United Kingdom, 2012
3. Berman, J.J., Principles of Big Data: Preparing, Sharing and Analyzing Complex
Information, Morgan Kaufmann, 2014
4. Barlow, M., Real-Time Big Data Analytics: Emerging Architecture, O Reilly, 2013
5. Schonberger, V.M. , Kenneth Cukier, K., Big Data, John Murray Publishers, 2013
6. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data
Streams with Advanced Analytics”, 1st Edition, Wiley and SAS Business Series, 2012.
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