Data Science and Big Data Analytics

COURSE OUTLINE:

Description

In this course, you will gain practical foundation level training that enables immediate and effective participation in big data and other analytics projects. You will cover basic and advanced analytic methods and big data analytics technology and tools, including MapReduce and Hadoop. The extensive labs throughout the course provide you with the opportunity to apply these methods and tools to real world business challenges. This course takes a technology-neutral approach. In a final lab, you will address a big data analytics challenge by applying the concepts taught in the course to the context of the Data Analytics Lifecycle. You will prepare for the Proven™ Professional Data Scientist Associate (EMCDSA) certification exam, and establish a baseline of Data Science skills.

Audience

  • Managers of teams of business intelligence, analytics, and big data professionals
  • Current business and data analysts looking to add big data analytics to their skills
  • Data and database professionals looking to exploit their analytic skills in a big data environment
  • Recent college graduates and graduate students with academic experience in a related discipline looking to move into the world of Data Science and big data
  • Individuals looking to take the EMC Proven™ Professional Data Scientist Associate (EMCDSA) certification

Prerequisites

To complete this course successfully and gain the maximum benefits from it, you should have the following knowledge and skill sets:

  • A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course
  • Experience with a scripting language, such as Java, Perl, or Python (or R)
  • Experience with SQL

Learning Objectives

  • Deploy the Data Analytics Lifecycle to address big data analytics projects
  • Reframe a business challenge as an analytics challenge
  • Apply appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
  • Select appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences
  • Use R and RStudio, MapReduce/Hadoop, in-database analytics, Windows, and MADlib functions
  • Use advanced analytics create competitive advantage
  • Data scientist role and skills vs. traditional business intelligence analyst