Data Science and Big Data Analytics

Overview

This course provides practical, foundation level training that enables immediate and effective participation in Big Data and other Analytics projects. It includes an introduction to Big Data and the Data Analytics Lifecycle to address business challenges that leverage Big Data. The course provides grounding in basic and advanced analytic methods and an introduction to Big Data Analytics technology and tools, including MapReduce and Hadoop. The extensive lab sessions provide many opportunities for students to apply these methods and tools to real-world business challenges as a practicing Data Scientist. The course takes an “open” or “technology-neutral” approach, and includes a final lab in which students address a Big Data Analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle.

Pre-requisite Knowledge/Skills

• A strong quantitative background with a solid understanding of basic statistics.
• Experience with a scripting language, such as Java, Perl or Python and R.
• Experience with SQL

The parameters given above are specific pre-requisite (or refresher) training and reading required to be completed prior to enrolling for or attending this course. Having this requisite background will help ensure a positive experience in the class, and enable students to build on their expertise to learn many of the more advanced tools and analytical methods taught during the course.

Course Objective

Upon successful completion of the course, participants should be able to:

• Immediately participate and contribute as a Data Science Team Member on big data and other analytics projects by
  • Deploying the Data Analytics Lifecycle to address big data analytics projects
  • Reframing a business challenge as an analytics challenge
  • Applying appropriate analytic techniques and tools to analyze big data, create statistical models, and identify insights that can lead to actionable results
  • Selecting appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audiences Using tools such as: R and RStudio, MapReduce/Hadoop, in-database analytics, Window and MADlib functions
• Explain how advanced analytics can be leveraged to create competitive advantage and how the data scientist role and skills differ from those of a traditional business intelligence analyst.

Course Outline

The contents of this course are designed to support the course objectives. The following focus areas are included in this course:

• Module 1: Introduction and Course Agenda
• Module 2: Introduction to Big Data Analytics
• Module 3: Data Analytics Lifecycle
• Module 4: Review of Basic Data Analytic Methods Using R
• Module 5: Advanced Analytics – Theory And Methods
• Module 6: Advanced Analytics - Technologies and Tools
• Module 7: The Endgame, or Putting it All Together

Course Deliverables