Introduction to Data Science and Big Data

ONLINE TRAINING
December 15 2020
May 20 2021
November 18 2021
CLASSROOM TRAINING
July 26 2021
October 5 2021
Training Cost
ONLINE TRAINING
545 EUR (VAT ex.) per person
CLASSROOM TRAINING
595 EUR (VAT ex.) per person
Practical informationClass from 9 AM to 5 PM
LanguageEnglish (unless all attendees speak Dutch)
Location

Most of our classroom training courses take place in Belgium (Edegem) or The Netherlands (Breda). Please click the button with the desired date to check the exact location of the training.

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Upon your request we can organize this training for you.
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Duration

1 day

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

ENROLL NOW
This training in-company?
Upon your request we can organize this training for you.
CONTACT US