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INTRODUCTION TO BIG DATA ANALYTICS

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K3K4K5K5K6-------K3K3K6
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Course Mapping332220000000332

{{{credits}}}

LTPC
2023

Course Objectives

  • To understand the competitive advantages of big data analytics
  • To understand the distributed storage for big data
  • To learn distributed method for processing of big data
  • To understand how to represent unstructured data using NoSQL and processing
  • To learn how statistical methods used for analyzing big data

{{{unit}}}

Unit IIntroduction to Big Data7

Introduction – Understanding Big Data – Big Data: Benifitting – Managing – Organizing and Analyzing Big Data: Learning and Analytics; Technology Challenges for Big Data.

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Unit IIHDFS9

Introduction – Distributed File System – Google File System – HDFS Design Goals – Using HDFS.

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Unit IIIData Processing using MapReduce10

Introduction – MapReduce Overview – Working of MapReduce – Programming – Writing and Testing MapReduce Programs

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Unit IVNoSQL9

Introduction to NoSQL – Characteristics of NoSQL – NoSQL Storage Types – Advantages and Drawbacks - NoSQL Database Framework: Hive and HBase

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Unit VData Analysis10

Statistical Methods: Regression modelling – Multivariate analysis; Classification: SVM – Decision Trees; Linear Classifiers

Course Outcomes

Upon the completion of the course the students should be able to:

  • Understand how to leverage the insights from big data analytics (K2)
  • Understand and apply distributed computing for better storage of data (K3)
  • Develop applications using Hadoop related tools(K4)
  • Use database frameworks like Hive and Hbase for data analysis(K3)
  • Solve applications using statistical and data analytic methods (K3)

Text Books

  1. Parag Kulkarni, Sarang Joshi, “Big Data Anlytics”, PHI Learning Pvt. Ltd, 2016.
  2. Anil Maheshwari, “Big Data Essentials”, McGraw Hill, 2019

References

  1. Arshdeep Bahga, Vijay Madisetti, “Big Data Analytics: A Hands-On Approach”, Published by A Hands-on Approach Textbooks, 2016.
  2. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics”, John Wiley & sons, 2012.
  3. Gaurav Vaish, “Getting Started with NoSQL”, Packt Publishing Ltd, 2013.
  4. E. Capriolo, D. Wampler, and J. Rutherglen, “Programming Hive”, O’Reilly, 2012.
  5. Lars George, “HBase: The Definitive Guide”, O’Reilly, 2011.

Suggestive Experiments

  • Hadoop
  1. Applications using Map-Reduce programming (Examples: word count

/ frequency programs / matrix multiplication)

  • R
  1. Linear and logistic Regression (Loan prediction using Credit approval dataset, Sales prediction using Bigmart dataset)
  2. SVM / Decision tree classification techniques (Flower type classification based on available attributes using Iris dataset, Passengers survival classification using titanic dataset)
  3. Clustering (Document categorization by multiclass techniques)
  4. Visualize data using any plotting framework
  • Database
  1. Application that stores data in Hbase (Sentiment analysis using twitter dataset)

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