General info 

This is a one-day training course. Select the desired start date at the top right of the screen for practical information regarding the training (location, price, registration, etc.).

Course overview

This course is intended for everyone with an interest in Big Data and Data Science. What are the differences between the two, what are the most-used tools out there, and how do you integrate these new tools and technologies in your existing workflow.

Who should attend this training

This course is intended for everyone with an interest in Big Data and Data Science.

Course Objectives

Upon completion of this course, participants will understand the following:

• Introduction to Big Data and Data Science. What is it, and what are the differences?
• Possibilities: Big Data and Data Science use cases
• Big Data and Data Science landscape: tools and technologies
• The data science maturity model: the lifecycle of Data Science projects
• Incorporating an innovation workflow into your organization
• Integration within an enterprise architecture and existing BI/DWH stack

Prerequisites

This course covers the absolute basics. A basic understanding of computer technology is required, but we won’t go deep into detail about code; we will discuss concepts and technologies only.

Course Content

• Introduction to Big Data and Data Science. What is it, and what are the differences? 
• • Terminology
  • Big Data
  • Data Science
• Possibilities: Big Data and Data Science use cases
• • Real-life examples of Big Data and Data Science projects
• Big Data and Data Science landscape: tools and technologies
  
• • Big Data Tools
  • The Hadoop Ecosystem (Spark, Kafka, …)
  • NoSQL Databases
  • Graph Databases
  • R and R Studio
  • Python – Scikit Learn and other Machine Learning Libraries
  • Deep Learning and Neural Networks - TensorFlow
• The data science maturity model: the lifecycle of Data Science projects
• Incorporating an innovation workflow into your organization
  
• • Stakeholders within the organisation
  • Ideation and roadmap
  • Implementing your first case
  • Measuring and communicating results
• Integration within an enterprise architecture and existing BI/DWH stack