Most Rigorous, practical and hands-on Big Data Analytics Course

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Classroom Learning

Classroom Learning

The program consists of about 350 hours of learning –
  • 200 hours of classroom sessions that include hands-on exploration of the tools and techniques,
  • 50 hours of online learning (recorded videos, live webinars and course readings) and
  • 100 hours of work outside the classroom with lab assignments and projects.
Online - Big Data Lab

Online - Big Data Lab

All the technology tools for data ingestion, processing, analysis and visualization will be stably installed, maintained and hosted for you to access at any time to work on your assignments and the Capstone project. The tools include
  • Big Data Tools (Hadoop, Spark, Hive, Pig, etc.)
  • Visualization Tools such as Tableau and Gephi
  • Programming Environments such as Python and R.
Industry Perspective Lectures

Industry Perspective Lectures

We believe in industry-relevant exposure, and ensure that a significant portion of your learning happens through practitioners and industry leaders. Industry lectures complement the classroom sessions through practical case studies and examples. Industry practitioners also act as mentors and coaches on the practical elements of the course (Capstone project and assignments).
Capstone Projects

Capstone Projects

As a participant, you will be pursuing a real-world problem using a range of techniques and tools that you’ve learned in class. Once again, the Big Data Lab will be available to you for this at all times. The Capstone project is a critical element, and will serve as your tangible ‘body of work’ – an impressive way in which you can prove your mastery in this field to potential employers.
The post graduate program in Big Data Analytics will help you get a visceral understanding of the entire data value chain – the flow of information from inception to analysis to insight – that is essential to drawing insights from big data. While there is a proliferation of technology tools like Hadoop, HDFS, Spark etc., all of them have developed as a result of a unique challenge faced by teams analyzing data, and trying to draw useful insights from it.
Our big data analytics course is built on four pillars:
  • Statistical foundations necessary for data science
  • Big Data Technologies for a hands-on exploration of handling large, complex, disparate data
  • Machine Learning and Advanced Analytics techniques to draw inferences from complex datasets
  • Visualization skills necessary to display the data in a useful and compelling way
Throughout this data analytics course, we will use real-world examples and practical datasets and help you to develop your skills through a full-fledged Big Data lab on the cloud.
Curriculum
Module Contents
Statistical Foundations
  • Descriptive & inferential statistics
  • Experiment design
  • Hypothesis testing and estimation
  • Predictive analytics – regression (Ordinary least squares, multiple linear, logistic)
  • Sampling
  • Probability distributions
  • Correlation and interactions

Tools: R, Excel

Big Data Technologies
  • Hadoop and Spark ecosystem
  • Data discovery and acquisition – Real time, web, DB, archives, machine logs
  • Data storage and manipulation in HDFS
  • NoSQL databases (MongoDB)
  • Big Data in the cloud with AWS
  • Data processing with Spark, Hive, Pig

Tools: Hadoop, Hbase, Spark, Pig, Hive, MongoDB, AWS

Machine Learning on Big Data
  • Feature Engineering
  • Dimensionality reduction
  • Tree-based methods: Decision trees, random forest
  • Classification
  • Clustering
  • Recommendation systems
  • Graphical models and page rank algorithm

Tools: R, Python, Mllib, GraphX

Visualization & Insight
  • Exploratory data analysis
  • Graphical representation using libraries
  • Visualizing graphical and network models
  • Campaign analysis and dashboards
  • Insight presentation – written & visual
  • Case studies on real world data sets

Tools: Tableau, Gephi, R libraries

* Please note the above mentioned curriculum is indicative and the program director reserves the right to add/remove/modify the topics as per the demand and industry requirement
Tools covered

Big Data: Hadoop, HDFS, Spark, Spark Streaming, Hive, Pig, HBase, MongoDB

Libraries: MLlib, GraphX

Visualization: Tableau, Gephi

Programming: Python, R

Experiential learning
  • Industry Capstone Project
  • Big Data Lab
  • Assignments and case studies


Learning Big Data Tools

Great Learning’s Big Data Analytics certification course is an exclusive blend of fundamental concepts, specially designed to provide students with analytical skills in open source software such as Hadoop, Spark, Hive etc. It provides Big Data expertise to meet the ever-growing demand for Big Data analysts and data scientists. Through our extremely comprehensive and multidisciplinary curriculum in Big Data, including tools such as Python, R, Hadoop etc,students will be able to study and explore the concepts of real world business analytics and information technology. Our curriculum is a perfect balance of illustrative and predictive components that helps students to build applicable, practical and critical skills in using Big Data (Hadoop, MongoDB, Pig etc), Visualization (Tableau, Gephi) and Programming (Pyton,R)tools.

Our curriculum includes various Modules, For example, Machine learning on Big Data and gives utmost importance to extensive classroom learning. The course also includes various rigorous industry sessions and capstone project to acquaint students, first-hand, with advanced big data technologies such as Hadoop, Hive, MongoDB etc. and foster in-depth understanding of machine learning; to help them visualize and comprehend results and lastly to turn the results into relevant insights. Our advanced and fully equipped 24 by 7 Big Data lab on the cloud makes software assimilating extremely easy for students while they are learning various big data tools.

Program Advisors

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Dr. Satya V. Nitta
Worldwide Leader & Program Director - Cognitive Sciences and Education Technology, IBM PhD (Rensselaer Polytechnic Institute)
Dr. Satya V. Nitta is passionate about solving major global problems by inventing and developing technologies to address them. He has deep experience in inventing, building world class teams and developing groundbreaking technologies in both the hardware and software areas of computing. Dr. Nitta has envisioned and launched the cognitive computing for education initiative from IBM Research as part of the larger global IBM Watson Education business. His global team invents and develops technologies at the intersection of cognitive neuroscience and cognitive computing and employs multiple techniques in fields ranging from machine learning, natural language processing, virtual and augmented reality, to experimental and computational neuroscience.

Faculty

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Dr. Kavi Mahesh
Director, Post Graduate Program in Big Data Analytics Program,GREAT LAKES INSTITUTE OF MANAGEMENT | PH.D. , M.S. – COMPUTER SCIENCE (GEORGIA INSTITUTE OF TECHNOLOGY, ATLANTA, USA)
Dr. Kavi Mahesh is the Dean of Computing and Decision Sciences at Great LakesInternational University. His areas of interest are knowledgemanagement, analytics, semantics, epistemology, ontology, classification studies, textprocessing and unstructured data management. He has three US patents and has publishedtwo books, 16 book chapters and 80 papers which have received over 1300 citations. He was previously with OracleCorporation, USA and New Mexico State University and has consulted in the area ofKnowledge Management with Infosys, Hewlett Packard, United Nations and EasyLib.com. Heholds an M. Tech. in Computer Science from the Indian Institute of Technology, Bombay(1989) and an MS (1991) and a PhD (1995) in Computer Science from Georgia Institute ofTechnology, USA.
Dr. P. K. Viswanathan
Professor (Statistics & Business Analytics), Great Lakes Institute of Management | PhD, MS (Manitoba, Canada), MBA (FMS, Delhi)
Dr. PK Viswanathan is a Professor in the area of Business Analytics and Statistics at Great Lakes Institute of Management. He is one of the country’s foremost academicians in the area of Analytics and has been listed within top 10 Analytics Academicians in the country. With over three decades of academic experience across leading business schools in India, Dr. Viswanathan is also the Program Director for Great Lakes’ Business Analytics program.
Dr. Bappaditya Mukhopadyay
Professor (Statistics & Business Analytics), Great Lakes Institute of Management | PhD (Indian Statistical Institute)
Dr. Bappaditya Mukhopadyay is a Professor in the area of Statistics and Business Analytics at Great Lakes Institute of Management. He is also a visiting professor at IIM Calcutta, University of Ulm, Germany and S.P.Jain Center for Management Singapore & Dubai. One of India’s leading Risk Analytics experts, Dr. Bappaditya is a Special Invitee on Board for Risk Management Committee, Punjab National Bank, Member Index Committee at NCDEX, Advisory board member at Asia Pacific Association of Derivatives and Special Invitee on board for risk management committee at IFCI. Dr. Bappaditya is the Program Director for Great Lakes’ Business Analytics program and is listed as one of the top 10 Analytics Academicians in India.
Dr. Srabashi Basu
Professor (Analytics & Quantitative Methods), Great Lakes Institute of Management | PhD (Penn State University, USA), MSc Statistics (University of Calcutta)
Dr. Basu is a Professor in the area of Analytics and Quantitative Methods at Great Lakes Institute of Management. She has worked and taught extensively in a range of analytics areas including health analytics, complex data analytics, predictive modelling and statistics. Dr. Basu has also been a faculty member at Indian Statistical Institute and University of Texas Health Science Centre, and has numerous publications to her name. Dr Basu also does corporate trainings in the area of Analytics and her expertise areas include Big Data Analytics, Statistics, R and Data Mining.
Sridhar Telidevara
Associate Professor (Statistics & Business Analytics), Great Lakes Institute of Management | PhD (State University, New York), MA (State University, New York)
Dr Sridhar is an Associate Professor in the area of Statistics and Business Analytics at Great Lakes Institute of Management. After starting his career with corporates, Dr. Sridhar pursued his academic interests and has been engaged with various institutes with a total of 10+ years of academic and research experience in USA, India and Dubai. Dr. Sridhar’s interests include Statistical Modeling, Econometrics and Quantitative techniques.
Amit Kapoor
Visualization Expert Instructor | MBA (IIM Ahmedabad), B Tech (IIT Delhi)
Amit began his career in management consulting and has close to a decade of experience with leading consulting firms such as AT Kearney in India and Booz & Company in Europe. He is one of the country’s leading instructors in the area of Visualization and has conducted multiple corporate trainings to leading companies in this domain.

Industry Connect

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The Post-Graduate Program in Big Data Analytics, offered by Great Lakes and Illinois Institute of Technology (IIT) Chicago, is conceived and delivered in collaboration with an impressive array of Corporate Partners, who contribute to making this program industry-oriented through practical instruction, real-world case studies and expert mentorship.
Dr. Nitin Agarwal
AVP & Head Data Science At Impetus Infotech
In his role, Dr. Agrawal heads a global team of 18 Data Scientists in delivering Big Data solutions to a variety of clients. Previously, Dr. Agrawal was Associate Professor at IIM Indore and has worked with SAS Institute and SAS. He earned his PhD from North Carolina State University.
Ullas Nambiar
Head Of Technology Innovation at Zensar Technologies
Previously, Ullas was AVP of Data Science at Myntra, Head of Analytics at EMC and a Scientist at IBM Research. Ullas has over 15 years of experience in Technology Innovation and earned a PhD in Computer Science from Arizona State University.
Pradeepta Mishra
Chief Data Scientist Machine Learning & AI Practice, Ma Foi Analytics
A Data Scientist with over 10 years experience in predictive modeling, machine learning and text mining in healthcare, retail and e-commerce. Pradeepta is also a technical author and has published books on machine learning in R and has a Masters Degree in Analytical & Applied Economics from Utkal University.

Program Schedule

Detailed Schedule for June/July 2016 batch will be updated shortly

Gurgaon Campus
Batch Commencement Date – June/July 2016
Chennai Campus
Batch Commencement Date – June/July 2016
Bangalore Center
Batch Commencement Date – June/July 2016
Pune Center
Batch Commencement Date – 8th April 2016

thanks for staying tuned! Bootstrap