The Data Science and Big Data Analytics course educates students to a foundation level on big data and the state of the practice of analytics. The course provides an introduction to big data and a Data Analytics Lifecycle to address business challenges that leverage big data. It provides grounding in basic and advanced analytic methods and an introduction to big data analytics technology and tools, including MapReduce and Hadoop. The course has extensive labs throughout to provide practical opportunities to apply these methods and tools 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. Upon completing the course, students will have the knowledge and practical experience to immediately participate effectively in big data and other analytics projects.

Prerequisites | Required

  •   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). Many of the lab examples taught in the course use R (with an RStudio GUI).
  •   Experience with SQL (some course examples use PSQL).
  •   Computer Science and Computer Programming.

   Introduction to Big Data Analytics
   Overview of Data Analytics Lifecycle
   Using R for Initial Analysis of the Data
   Advanced Analytics and Statistical Modeling for Big Data – Theory and Methods
   Advanced Analytics, and Statistical Modeling for Big Data – Technology and Tools
  Concluding and Operationalizing an Analytics Project
  Big Data Analytics Lifecycle Lab
 Download detailed table of contents


Hands On Exercises

Course Completion Certificate

Live Project

Certification Exam

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