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Course Description

A female engineer looking at various data on a futuristic screen

Organizations and data professionals have tremendous opportunities to analyze Big Data whether that data exists internally within the organization or externally. This course will examine the basic technical concepts and challenges of Big Data. Participants will learn how to use data analytics tools such as Hadoop, Hive or Spark. A capstone project will be included where participants will problem-solve a real-world Big Data scenario using the tools taught in the course.

Designed For

Professionals working with data from various sectors in healthcare, finance, insurance and marketing, data science and data analytics professionals.

Learning Outcomes

Upon successful completion of the course, students will be able to:

  • Describe the basic technical concepts and challenges of Big Data.
  • Explain and apply data cleaning and feature extraction methods to Big Data.
  • Investigate machine learning and statistical learning algorithms for processing Big Data.
  • Demonstrate the use of data analytics tools such as Hadoop and Hive.
  • Review parallel processing tools for Big Data such as MapReduce and Spark.
  • Discover how to draw conclusions from analytics through real-world case studies.
  • Complete a capstone project that draws on skills gained from the course to address a real-world problem in small teams.

Additional Requirements

Admission to the program requires an Undergraduate degree or College Diploma plus the completion of a Basic Statistics course.

Applies Towards the Following Certificates

*Course details are subject to change.

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Enrol Now - Select a section to enrol in

Section Title
Big Data Analytics
Type
Online
Dates
September 16, 2019 to November 10, 2019
Contact Hours
40.0
Delivery Options
Online  
Course Fee(s)
Early Bird $995.00
Potential Discount(s)
Section Materials
  • Textbook (Confirmed) (Mandatory) Data Analytics with Hadoop: An Introduction for Data Scientists by Benjamin Bengfort, Jenny Kim © 2016 O'Rielly ISBN 9781491913703
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