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You will be able to find information about Big data analytics along with its Course Objectives and Course outcomes and also a list of textbook and reference books in this blog.You will get to learn a lot of new stuff and resolve a lot of questions you may have regarding Big data analytics after reading this blog. Big data analytics has 5 units altogether and you will be able to find notes for every unit on the CynoHub app. Big data analytics can be learnt easily as long as you have a well planned study schedule and practice all the previous question papers, which are also available on the CynoHub app.

All of the Topic and subtopics related to Big data analytics are mentioned below in detail. If you are having a hard time understanding Big data analytics or any other Engineering Subject of any semester or year then please watch the video lectures on the official CynoHub app as it has detailed explanations of each and every topic making your engineering experience easy and fun.

Big data analytics Unit One

Introduction

Introduction: Introduction to big data: Introduction to Big Data Platform, Challenges of Conventional Systems, Intelligent data analysis, Nature of Data, Analytic Processes and Tools, Analysis vs Reporting.

Big data analytics Unit Two

Stream Processing

Stream Processing: Mining data streams: Introduction to Streams Concepts, Stream Data Model and Architecture, Stream Computing, Sampling Data in a Stream, Filtering Streams, Counting Distinct Elements in a Stream, Estimating Moments, Counting Oneness in a Window, Decaying Window, Real time Analytics Platform (RTAP) Applications, Case Studies – Real Time Sentiment Analysis – Stock Market Predictions.

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Big data analytics Unit Three

Introduction to Hadoop

Introduction to Hadoop: Hadoop: History of Hadoop, the Hadoop Distributed File System,

Components of Hadoop Analysing the Data with Hadoop, Scaling Out, Hadoop Streaming, Design of HDFS, Java interfaces to HDFS Basics, Developing a Map Reduce Application, How Map Reduce Works, Anatomy of a Map Reduce Job run, Failures, Job Scheduling, Shuffle and Sort, Task execution, Map Reduce Types and Formats, Map Reduce Features Hadoop environment.

Big data analytics Unit Four

Frameworks and Applications

Frameworks and Applications: Frameworks: Applications on Big Data Using Pig and Hive, Data processing operators in Pig, Hive services, HiveQL, Querying Data in Hive, fundamentals of HBase and ZooKeeper.

Big data analytics Unit Five

Predictive Analytics and Visualizations

Predictive Analytics and Visualizations: Predictive Analytics, Simple linear regression, Multiple linear regression, Interpretation of regression coefficients, Visualizations, Visual data analysis techniques, interaction techniques, Systems and application

Big data analytics Course Objectives

 To optimize business decisions and create competitive advantage with Big Data analytics

 To learn to analyze the big data using intelligent techniques

 To introduce programming tools PIG & HIVE in Hadoop echo system

Big data analytics Course Outcomes

At the end of the course, the students will be able to

 Illustrate big data challenges in different domains including social media, transportation, finance and medicine

 Use various techniques for mining data stream

 Design and develop Hadoop

 Identify the characteristics of datasets and compare the trivial data and big data for various applications

 Explore the various search methods and visualization techniques

Big data analytics Text Books

1) Tom White, “Hadoop: The Definitive Guide”, Third Edition, O’reilly Media, Fourth Edition, 2015.

2) Chris Eaton, Dirk DeRoos, Tom Deutsch, George Lapis, Paul Zikopoulos, “Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data”, McGrawHill Publishing, 2012.

3) Anand Rajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”, CUP, 2012

Big data analytics Reference Books

1) Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics”, John Wiley& sons, 2012.

2) Paul Zikopoulos, DirkdeRoos, Krishnan Parasuraman, Thomas Deutsch, James Giles, David Corrigan, “Harness the Power of Big Data:The IBM Big Data Platform”, Tata McGraw Hill Publications, 2012.

3) Arshdeep Bahga and Vijay Madisetti, “Big Data Science & Analytics: A Hands On Approach “, VPT, 2016.

4) Bart Baesens, “Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (WILEY Big Data Series)”, John Wiley & Sons, 2014.

Scoring Marks in Big data analytics

Scoring a really good grade in Big data analytics is a difficult task indeed and CynoHub is here to help!. Please watch the video below and find out how to get 1st rank in your B.tech examinations . This video will also inform students on how to score high grades in Big data analytics. There are a lot of reasons for getting a bad score in your Big data analytics exam and this video will help you rectify your mistakes and help you improve your grades.

Information about JNTU-K B.Tech R19 Big data analytics was provided in detail in this article. To know more about the syllabus of other Engineering Subjects of JNTUH check out the official CynoHub application. Click below to download the CynoHub application.

Get Complete Lecture Notes for Big data analytics on Cynohub APP

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