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Data Science

Data Science Overview

Group Training + View more dates & times


This course provides an in-depth overview of the choices you have in processing Big
Data. It introduces Data Science, the types of data you might have, approaches to working on
and processing the data, and the capabilities, strengths, and weaknesses of those

Topics covered include:

  • NewSQL Databases
  • NoSQL Overview
  • Hadoop and MapReduce
  • Apache Pig and Hive
  • Apache Storm and Spark
  • MongoDB, HBase
Who Should Take This Course


This course is suitable for: Data engineers, analysts, architects, data scientist, software engineers, and technical managers who want a quick introduction to data science processing tools and choices.


Some knowledge of database technologies is helpful.

Why You Should Take This Course

Upon completing this course, students will be able to:

  • Understand what Data Science is
  • Know the difference between “data-at-rest” and “data-in-motion”
  • Understand what map-reduce / Hadoop is, and what it can do
  • Be aware of query technologies for easily querying with Hadoop (e.g. Hive, Pig, and
  • Understand what NoSQL databases are and what they can do
  • Become familiar with the choices in the NoSQL landscape
  • Understand the strengths and weaknesses of different NoSQL technologies
  • Be well-informed on your choices in Data Science processing, and evaluate them for your
Is there a discount available for current students?

UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.

What is the cancellation and refund policy?

Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

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