e Data Science And Big Data Analytics

Course details

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 labs throughout provide many opportunities for students to apply these methods and tools to real-world business challenges as a practising 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 following modules and lessons included in this course are designed to support the course objectives:

Introduction and Course Agenda
Introduction to Big Data Analytics

• Big Data Overview
• State of the Practice in Analytics
• The Data Scientist
• Big Data Analytics in Industry Verticals

Data Analytics Lifecycle

• Discovery
• Data Preparation
• Model Planning
• Model Building
• Communicating Results
• Operationalizing

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

Advanced Analytics – Theory And Methods

• K Means Clustering
• Association Rules
• Linear Regression
• Logistic Regression
• Naïve Bayesian Classifier
• Decision Trees
• Time Series Analysis
• Text Analysis

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

The Endgame, or Putting it All Together

• Operationalizing an Analytics Project
• Creating the Final Deliverables
• Data Visualization Techniques
• Final Lab Exercise on Big Data Analytics

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 database 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