Overview Course

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 course takes an “Open”, or technology-neutral approach, and includes a final lab which addresses a big data analytics challenge by applying the concepts taught in the course in the context of the Data Analytics Lifecycle.

Learning Outcome

Upon completion of this course, participants should be able to:
  • 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 intelligenceanalyst

Who should Attend?

  • Managers of teams of business intelligence, analytics, and big data professionals
  • Business and Data Analysts
  • Data and database professionals
  • College graduates and graduate students with academic experience in a related discipline

Eligibility Criteria

  • A strong quantitative background with a solid understanding of basic statistics
  • 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), which is an open source statistical tool and programming language
  • Experience with SQL (some course examples use PSQL)
This course is endorsed under Critical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer to Programme Support under CITREP+ page


Information as accurate as of 10 June 2019