Big Data and Data Analytics

Paper Code: 
MBB 423
Credits: 
4
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

Course outcomes

Learning and

teaching strategies

Assessment

Strategies

On completion of this course, the students will be able to;

CO 1.Formulate a  problem  and  an

abstract model to handle Big Data in business domain.

CO 2.Install Big Data tool/s like Hadoop for business analytics.

CO 3.Develop a data store to handle massive business data using Big Data tools and generate queries.

CO 4.Build a machine learning model on Big Data for business problems

CO 5.Examine the outcomes of Big Data based machine learning models and communicate the results.

CO 6.Evaluate the performance of models using metrics like confusion matrix, accuracy,

RMSE etc.

Approach in teaching: Interactive Lectures,Group Discussion, Tutorials, Case Study

 

Learning activitiesfor the students: Self-learning assignments, presentations

Class test, Semester endexaminations, Quiz, Assignments, Presentation

 

18.00
Unit I: 

Understanding Big Data

Digital data and its classification, characteristics of data, evolution and definition of big data. Challenges with big data, why big data, Traditional Business intelligence versus Big Data

Big Data Analytics

What is Big data analytics, why sudden hype around big data analytics, classification of analytics, top challenges facing big data, terminologies used in big data environment, Top analytics tools

 

18.00
Unit II: 

Big Data Technology Landscape

Apache Hadoop, Why Hadoop, Comparison with other systems: RDBMS, Grid computing, Hadoop overview, HDFS and its ecosystems, Hadoop architecture and 2.x core components. Managing Resources and applications with Hadoop YARN (Yet Another Resource Negotiator), Understanding MapReduce Programming, Running sample MapReduce program, Executing MapReduce Applications -Word count, Tera Sort, Radix Sort.

Introduction to Hadoop Ecosystem, Pig, Hive, Sqoop, HBase

18.00
Unit III: 

Pig: Introduction to PIG, Execution Modes of Pig, Comparison of Pig with Databases, Pig on Hadoop Hive: Hive Shell, Architecture, data types, Comparison with Traditional Databases, HiveQL, Tables, User Defined Functions.

 

18.00
Unit IV: 

NoSQL: Use of NoSQL, Types of NoSQL, Advantages of NoSQL. Use of No SQL in Industry, NoSQL Vendors, SQL versus NoSQL, NewSQL

Hbase: Hbase basics, Concepts, Clients, Example, Hbase Versus RDBMS.

 

18.00
Unit V: 

Machine Learning using python ,Python installation (Window and Ubuntu), Execution modes of Python, Executing Python programs on hadoop, Python Libraries and Tools - Pandas for data analysis, Matplotlib for data visualization, Numpy for matrix processing, SciPy for image manipulation. Applications of Machine Learning, Implementation of machine learning in Hadoop environment

*Case studies related to entire topics are to be taught.

Essential Readings: 
Essential readings
  • Seema Acharya, Subhasini Chellappan, "Big Data Analytics" Wiley 2015.
  • Michael Minelli, Michelle Chambers, and AmbigaDhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013.
  • P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence", Addison-Wesley Professional, 2012.
  • Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
  • Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
  • E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
Suggested readings
  • Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
  • Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.".

 

References: 
E resources

 

Journals

 

Academic Year: 
2023-2024
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