Advanced Analytics and Machine Learning with Hadoop- Statistics.com

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In this online course, you will expand on the topics from the Introduction to Analytics using Hadoop course, and introduce statisticians and data analysts to higher-order tools in the Hadoop Ecosystem.

In this course, you will learn about:

  • The software components of the Hadoop Ecosystem
  • Data loading, warehousing and manipulation with HBase, Hive, and Sqoop
  • Data aggregation and designing data workflows with Pig and Cascading
  • Machine learning and data mining with Mahout

Course Program:

  • WEEK 1: The Hadoop Ecosystem and Data Warehousing and Manipulation pt. 1
  • WEEK 2: Data Warehousing and Manipulation pt. 2
  • WEEK 3: Higher Order Hadoop Programming
  • WEEK 4: Machine Learning and Data Mining

Important Date

January 23, 2015 to February 20, 2015

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

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. Introduction to Analytics with Hadoop or equivalent familiarity with Hadoop and its core components
  2. Strong understanding of MapReduce and MapReduce API
  3. Intermediate familiarity with Java preferred
  4. “SQL and R: Introduction to Database Queries” or the equivalent familiarity with SQL and query languages
  5. Basic knowledge of operating systems (UNIX/Linux)

Who Should Take This Course:

Data scientists and statisticians who are familiar with Hadoop fundamentals, have programming experience, and who want to learn how to process and analyze large data sets with Hadoop’s distributing computing capability and ecosystem components.

  • Hadoop
  • HBase
  • Hive
  • Sqoop
  • Pig
  • Mahout
  • NoSQL
  • Ms. Jenny Kim
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This course is an extension of the basic course from Statistics.com and deep dives into Hadoop eco-system. The course is good on content – but high on price.