Data Science & Big Data Analytics Training | Prepare for EMCDSA Certification Exam from EMC

Data Science & Big Data Analytics Course Description

This course provides practical foundation level training that enables immediate and effective participation in big data and other analytics projects. It establishes a baseline of skills that can be further enhanced with additional training and real-world experience. The course provides an introduction to big data and a Data Analytics Lifecycle Process 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 to real-world business challenges 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\u2122 Professional Data Scientist Associate EMCDSA) certification exam.

Data Science & Big Data Analytics Course Learning Outcomes

  • Immediately participate and contribute as a Data Science Team Member on big data and other analytics projects by
  • 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 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

Data Science & Big Data Analytics Training - Suggested Audience

This training is aimed at business intelligence professionals. Suggested attendees based on our past programs are:
  • Managers of business intelligence
  • Analytics Professionals
  • Big data professionals
  • Data and database professionals

Data Science & Big Data Analytics Training Duration

  • Open-House F2F (Public): 5 days
  • In-House F2F (Private): 5 days, for commercials please send us an email with group size to

Data Science & Big Data Analytics Training - Prerequisites

  • 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 (actually RStudio), which is an open source statistical tool and programming language
  • Experience with SQL

This Data Science & Big Data Analytics training course outline includes:

1. Introduction to Big Data Analytics
  •  Big Data Overview
  •  State of the Practice in Analytics
  •  The Data Scientist
  •  Big Data Analytics in Industry Verticals

2. Data Analytics Lifecycle
  •  Discovery
  •  Data Preparation
  •  Model Planning
  •  Model Building
  •  Communicating Results
  •  Operationalizing

3. Review of Basic Data Analytic Methods Using R
  •  Using R to Look at Data - Introduction to R
  •  Analyzing and Exploring the Data
  •  Statistics for Model Building and Evaluation

4. Advanced Analytics - Theory and Methods
  •  K Means Clustering
  •  Association Rules
  •  Linear and Logistic Regression
  •  Na├»ve Bayesian Classifier
  •  Decision Trees
  •  Time Series Analysis
  •  Text Analysis

5. Advanced Analytics - Technologies and Tools
  •  Analytics for Unstructured Data - MapReduce and Hadoop
  •  The Hadoop Ecosystem:In-database Analytics - SQL Essentials
  •  Advanced SQL and MADlib for In-database Analytics

6. The Endgame, or Putting it All Together
  •  Operationalizing an Analytics Project
  •  Creating the Final Deliverable
  •  Data Visualization Techniques
  •  Final Lab Exercise on Big Data Analytics
Keny White


Keny White is Professor of the Department of Computer Science at Boston University, where he has been since 2004. He also currently serves as Chief Scientist of Guavus, Inc. During 2003-2004 he was a Visiting Associate Professor at the Laboratoire d'Infomatique de Paris VI (LIP6). He received a B.S. from Cornell University in 1992, and an M.S. from the State University of New York at Buffalo.


After working as a software developer and contractor for over 8 years for a whole bunch of companies including ABX, Proit, SACC and AT&T in the US, He decided to work full-time as a private software trainer. He received his Ph.D. in Computer Science from the University of Rochester in 2001. "What I teach varies from beginner to advanced and from what I have seen, anybody can learn and grow from my courses".


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