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Introduction to Analytics using Hadoop- Statistics.com

0-6 Month Online
Intermediate 31-Oct-2014
Online Big Data
Online Self Paced 1181


[su_tab title = “Description”]

In this online course, “Introduction to Analytics using Hadoop,” analytics professionals will be introduced to Hadoop, and provided with an exemplar workflow for using Hadoop. They also will be introduced to writing MapReduce jobs, and leveraging Hadoop Streaming to conclude work in an analytics programming language such as Python.


[su_tab title = “Program structure”]

In this course you will learn

  1. What Hadoop is hand how to leverage it to perform analytics
  2. The software components of the Hadoop Ecosystem
  3. How to manage data on a distributed file system
  4. How to write MapReduce jobs to perform computations with Hadoop
  5. How to utilize Hadoop Streaming to output jobs

Course Program:

  • Week 1: A Distributed Computing Environment
  • Week 2: Working with Hadoop
  • Week 3: Computing with MapReduce
  • Week 4: Towards Last Mile Computation

Important Date

October 31, 2014 to November 28, 2014

Duration: – 4 Weeks

Time Requirement: about 15 hours per week, at time of your choosing.

Fees: INR 32,940 (assuming $ = INR 60)

Part Time/ Full Time:

Part Time


[su_tab title = “Eligibility”]

These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

  1. Command line experience on Linux, to manage system processes, find appropriate files and set permissions.

  2. Familiarity with Python or another programming language to leverage Hadoop streaming to perform computations.

Who Should Take This Course:

Data scientists and statisticians with programming experience who need to deal with large data sets and want to learn about Hadoop’s distributing computing capability should take Introduction to Analytics using Hadoop. This course is particularly suited to data scientists that need to access and analyze large amounts of unstructured or semi-structured data that do not fit well into traditional relational databases.


[su_tab title = “Tools”]

  • Hadoop
  • MapReduce
  • Python


[su_tab title = “Faculty”]

  • Mr. Benjamin Bengfort


[su_tab title = “Contact”]

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