IIS University

Admissions

It is a University which is committed to excellence and overall development of the student and one where talent is nurtured and honed in the best possible manner. Come, be a part of our never-ending journey of accomplishments and accolades.

Psychometric Counselling Tele-Counselling E-mail Counselling Subject Counselling International Admissions

Admissions

It is a University which is committed to excellence and overall development of the student and one where talent is nurtured and honed in the best possible manner. Come, be a part of our never-ending journey of accomplishments and accolades.

Read More

Placements

Excellent education not only imparts knowledge, but also paves the way for a promising and successful career. The Placement Cell at The IIS University has numerous success stories to narrate in this regard...

Read More

Life@IIS Univ

Life @ IISU is nothing less than a kaleidoscope of myriad hues; complete with vibrant events and memorable occasions to cherish for a lifetime.

Read More

Academics

Taking the age-old tradition of enlightenment through education forward, The IIS University - in its continuous pursuit of excellence - firmly believes that an institution’s academic profile determines its worth and value. Therefore, on offer are a wide variety of both conventional and contemporary programmes of study.

Read More

Research

To stimulate curiosity and cater to the global needs of a dynamic education, The IIS University - since inception - has been promoting and encouraging research-based experiential learning among all its students.

Read More

About Us

The IIS University, with its perfect amalgamation of innovation, inspiration and synergy, aims to be a model institution for students across disciplines and programmes.

Read More
Big Data and Data Analytics | Business Analytics

Big Data and Data Analytics

Error message

  • Warning: ini_set(): Headers already sent. You cannot change the session module's ini settings at this time in drupal_environment_initialize() (line 691 of /home/iisunivac/public_html/includes/bootstrap.inc).
  • Warning: ini_set(): Headers already sent. You cannot change the session module's ini settings at this time in drupal_environment_initialize() (line 692 of /home/iisunivac/public_html/includes/bootstrap.inc).
  • Warning: ini_set(): Headers already sent. You cannot change the session module's ini settings at this time in drupal_environment_initialize() (line 693 of /home/iisunivac/public_html/includes/bootstrap.inc).
  • Warning: ini_set(): Headers already sent. You cannot change the session module's ini settings at this time in drupal_environment_initialize() (line 698 of /home/iisunivac/public_html/includes/bootstrap.inc).
  • Warning: ini_set(): Headers already sent. You cannot change the session module's ini settings at this time in drupal_environment_initialize() (line 702 of /home/iisunivac/public_html/includes/bootstrap.inc).
Paper Code: 
MBB 422
Credits: 
4
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

Course outcomes (Cos):

Courseoutcomes

Learningandteaching

strategies

AssessmentStrategies

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

CO 157.Formulate a problem and an abstract model to handle Big Data in business domain.

CO 158.Install Big Data tool/s like Hadoop for business analytics.

CO 159.Develop a data store to handle massive business data using Big Data tools and generate queries.

CO 160.Build a machine learning model on Big Data for business problems

CO 161.Examine the outcomes of Big Data based machine learning models and communicate the results.

CO 162.Evaluate the performance of models using metrics like confusion matrix, accuracy ,RMSE etc.

Approach inteaching:Interactive Lectures,Group Discussion,Tutorials,CaseStudy

 

Learning activitiesfor the students:Self-learningassignments,presentations

Class test,Semester endexaminations,Quiz,Assignments,

Presentation

 

 

 

18.00

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

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

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

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

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

 

*Casestudies related to entire topics are to be taught.

 

Essential Readings: 
  • Seema Acharya, SubhasiniChellappan, "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.

 

References: 

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.".

 

E resources

           

Journals

 

 

Academic Year: