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Big Data Analytics
Learn nuances unique to Big Data when solving business problems .

10 Weeks programme

Contact hours: 40 hours
  • Conceptual Learning
  • Hands-on Learning
  • Assignments
  • Case studies/ Use cases
For Enrollment
Programme Objectives
The “Big Data Analytics” programme aims to equip participants with cutting-edge skills in the most rapidly growing field in today’s Analytics landscape, viz, Big Data Analytics, and its applications in the industry. The programme awards participants the proficiency to position themselves successfully exploit the rapidly growing demand for Big Data Analytics specialists in the industry. The programme offers strong conceptual learning, complemented with business case-studies/ use-cases/ examples.
Who should go for the Programme?
The “Big Data Analytics” programme has been designed & developed primarily for traditional IT/ITES professionals who wish to add the high-demand Big Data Analytics skillset to their bouquet of skills. The programme is of significant value to professionals working as business analysts in the IT/ITES environment, who wish to learn the nuances unique to Big Data. The programme is of equal value to professionals keen to work in the emerging & rapidly growing analytics industry as specialists in Big Data Analytics.

Key Takeaways

  • Understand & appreciate the scope of Big Data Analytics in various business domains
  • Articulate the concepts & applications of Big Data technologies and their environments/ ecosystems
  • Emerge as skilled Big Data technology professionals (with strong skills in Hadoop, HDFS, Map Reduce, Hive, Pig, Sqoop, Flume, etc)
  • Envisage challenges & consideration unique to Big Data when solving Business Analytics problems
  • Position themselves to add significant value to providers as well as consumers of Big Data Analytics

Learning Mode

Curriculum Overview

Introduction to Big Data Big Data enablers Big Data ecosystem Big Data roles Big Data Analytics
Cloud Computing & Virtualization Virtual Machine & Virtualization Software Types of Cloud Major Cloud service providers Cloud Computing for Big Data
Introduction to Hadoop 1.0 Hadoop 1.0 ecosystem & architecture Hadoop Distributed File System (HDFS) HDFS concepts & commands Map Reduce (MR) concepts Hadoop 1.0 processes Name Node, Secondary Name Node, Task Tracker, Job Tracker, Data Node, FS Image, Edit Log, etc Hadoop word-count program explanation Hadoop 2.0 Yet Another Resource Negotiator (YARN) Schedulers HDFS Federation HDFS High availability Speculative Execution Map Reduce input/ output formats Output Committers Hadoop installation
Map Reduce programming paradigm Stages in a Map Reduce programme Customizations in a Map Reduce programme Map Reduce APIs & tools Executing Map Reduce programs in Hadoop Combiners and Partitioners Custom Sort and Distributed Cache Map Reduce Counters Map Reduce design patterns
Introduction to ETL/ ELT & Data Transformation Introduction to Pig Pig architecture Pig programming paradigm Pig data types and Pig commands User Defined Concepts (UDFs) in Pig Apache Data Fu & Piggy Bank Creating, registering and using UDFs in Pig Caselets using UDFs in Pig Advanced concepts in Pig
Introduction to Data Warehouse Introduction to Hive Data Lakes, Data Reservoirs, and Data Marts Schema on Read versus Schema on Write Hive architecture and Hive commands Caselets using Hive Advanced concepts in Hive
Introduction to Big Data ingestion Introduction to Flume Flume architecture Flume mappings & Flume configurations Ingesting social media data using Flume Introduction to Sqoop Sqoop architecture Caselets using Flume & Sqoop

Are there any Learning Pre-requisites?

The programme “Big Data Analytics”  is essentially of a relatively specialized nature, and hence there are certain learning pre-requisites for the programme. Participants best-suited to derive maximum benefits from the programme are IT/ITES professionals with at least two (2) years of industry experience, with knowledge of computer programming in any language, preferably in either of C/C++, Java, R or Python. In order to enhance the efficiency & effectiveness of participants’ learning, it is desirable that participants possess a BE/ B. Tech., MCA, M. Sc. or MBA degree, with Mathematics as a subject in their graduation.

Enrollment

Big Data Analytics

MEET Our Consultants & Faculty

Goutam Das

M. Tech. (Computer Engineering), IIT Kharagpur

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Dr Nidhan "Neal" Choudhuri

Ph.D. (Statistics), Michigan State University

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Dr Chiranjit Acharya

B.E., M.E., & Ph.D. (Computer Science), Jadavpur University

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Sangeet Pal

PGDM (MBA), IIM Ahmedabad

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Pravin Bhosale

B.E. (Mechanical Engineering), Shivaji University, Maharashtra

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Anirban Bhaduri

B.E. (Computer Science), IIEST Calcutta (e.k.a. Bengal Engineering & Science University)

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Shyam Karmakar

Chartered Statistician at The Royal Statistical Society, London

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Shamik Choudhury

M. Tech. (Computer Science), Indian Statistical Institute, Calcutta

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