Data Science and Big Data Analytics

////Data Science and Big Data Analytics

Data Science and Big Data Analytics

Course ID: EMC-DSBDA 5 Days

Data Science and Big Data Analytics


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. Labs offer opportunities for students to understand how these methods and tools may be applied 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. The course prepares the student for the Proven™ Professional Data Scientist Associate (EMCDSA) certification exam.



This course is intended for individuals seeking to develop an understanding of Data Science from the perspective of a practicing Data Scientist, including:

  • 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 data base 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 seeking to take advantage of the EMC Proven™ Professional Data Scientist Associate (EMCDSA) certification


Data Science Associate


Professional Data Scientist Associate (EMCDSA) certification exam


Instructor-led / Virtual Instructor-led

Singapore: Upon Request
Malaysia: MYR10,750
Thailand: Upon Request
India: Upon Request


  • A strong quantitative background with a solid understanding of basic statistics, as would be found in a statistics 101 level course
  • Experience with ascriptinglanguage,such as Java, Perl,orPython(or R)
  • Many of the lab examples taught in the course use R (with an RStudioGUI), which is an open source statistical tool and programming
  • Experience with SQL (some course examples use)


  • 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 project so 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 result
    – Selecting appropriate data visualizations to clearly communicate analytic insights to business sponsors and analytic audience so Using tools such as: R and R Studio, Map Reduce/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


Module 1: Introduction to Big Data Analytics

Module 2: Data Analytics Lifecycle

Module 3: Review of Basic Data Analytic Methods Using R

Module 4: Advanced Analytics – Theory And Methods

Module 5: Advanced Analytics – Technologies and Tools

Module 6: The Endgame, or Putting it All Together

What’s Next

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