Course Duration : 5 Days

Data Science and Big Data Analytics for Business Transformation

This course builds on skills developed in the Data Science and Big Data Analytics course. The main focus areas cover Hadoop (including Pig, Hive, and HBase), Natural Language Processing, Social Network Analysis, Simulation, Random Forests, Multinomial Logistic Regression, and Data Visualization. Taking an “Open” or technology-neutral approach, this course utilizes several open-source tools to address big data challenges.

  1. Upon successful completion of this course, participants should be able to:
    •   Articulate the business value of Big Data and the opportunities it presents to drive growth and innovation
    •   Discuss key Data Science analytic methods and identify opportunities for applying these methods
    •   Lead analytics projects using a structured lifecycle approach
    •   Develop Data Science teams to leverage the required skill sets andappropriate organizational models
    •   Drive innovation via analytics projects by understanding how to driveorganizational change

The intended audience for this course includes:

  •   Leaders of functional areas wanting to enhance analytics-driven decision making
  •   Business leaders looking to build a new analytics or Data Science capability
  •   Leaders of Business Intelligence or Operations teams looking to raise the level of analytics

The content of this course is designed to support the course objectives. The following focus areas are included in this course:

Module 1: Introduction

  •   Overview of Data Science and Big Data analytics
  •   Business drivers for advanced analytics
  •   Stages of analytical maturity in an organizationModule 2: Deriving Business Value from Big Data
  •   Business value of a Data Science project
  •   Overview of key advanced analytic techniques and theirapplications
  •   Big Data tools and technologiesModule 3: Leading Analytic Projects
  •   Overview of data analytics lifecycle
  •   Frame a business problem as an analytics problem
  •   Four main deliverables in an analytics projectModule 4: Developing Data Science Teams
  •   Develop an analytic team, roles and skill sets
  •   Four approaches to develop Data Science capabilities
  •   Three organizational models for Data Science teamsModule 5: Driving Innovation via Analytic Projects
  •   Cultivate characteristics of visionary thinking to apply to Data Science teams
  •   Incorporate change management as part of implementing a data-driven approach to decision making
  •   Leverage small wins to change how the organization approaches problems