Data Science Essentials

What is Data Science and which (Big) Data questions can you solve with it?
With the Essentials course you will obtain a structured overview of Data Science concepts, processes and technology.


1 day

€ 720,00 excl. VAT


Data Science & Big Data


  • Apr. 19 ’23
  • Apr. 26 ’23
  • Jun. 27 ’23
  • Sep. 12 ’23
  • Nov. 28 ’23

General information

There is an ever growing need to also, next to the well-known business intelligence (BI)-applications like reports, dashboards and OLAP, develop Machine Learning, Data Mining, Artificial Intelligence (simply, Data Science) applications for and together with users. In this course we will dive into the differences between Business Intelligence, Datawarehousing, Big Data and Data Science. We will show what Data Science is, why it is interesting for BI and Datawarehouse professionals and how you can put it in to practice.

Your results Data Science Essentials
After following this course you will be able to identify different concepts and steps in Data Science, talk about and give advice on tools and implementation.
You will get insight in:

  • The fundamental role of Data Science in the current data landscape
  • The differences and similarities with Business Intelligence
  • The various forms of Data Science: Data Mining, Machine Learning and Artificial Intelligence
  • Concrete real-world Data Science examples
  • A deepening in the different algorithms for Data Science
  • An overview of well-known and lesser known tools
  • Different demos of Data Science Tools (Microsoft, Tibco, RapidMiner, …)

This course is focused on everyone with a Business Intelligence and Datawarehousing background who wants to get to know the possibilities of Data Science.


During the course you will get insight into fundamental and up-to-date developments within the Data Science expertise and which question you are able to solve with Data Science. The focus is on creating insight in the cohesion between the diverse subjects.

Subjects that will be discussed:

  • Overview of Data Science
    • What is Data Science and what are the differences and similarities with BI and Datawarehousing?
    • What questions are we able to solve with Data Science?
    • The relationship between Big Data and Big Science
  • Data Mining
    • Predictive and descriptive models: how to make the choice and how to apply them
    • Supervised and unsupervised learning
    • Overview of Data Mining forms (classifications, clustering, association)
  • Machine Learning
    • Overview of Machine Learning algorithms
    • Building models and making choices
    • Neural networks, decision trees, genetic algorithms: how can they be of use and how does it work?
    • Deep learning: en route to Artificial Intelligence
  • Artificial Intelligence
    • What is Artificial Intelligence?
    • The difference between Data Mining and Machine Learning
    • AI in the daily practice: how much of it do we actually notice?
  • Data Science in practice
    • Case: Clinical Decision Support
    • Case: Intelligent Environmental Zone
  • Data Science roles
    • From BI Competence Centre to Data Science Competence Centre: from data driven to data centric
    • From BI consultant to Data Science consultant: developing a new skillset, what does this look like?
  • Data Science process
    • CRISP-DM: method for Data Science
    • Roadmap for the implementation of Data Science
    • Risks, pitfalls, measures
  • Tool demos
    • Demo RapidMiner Data Science Platform
    • Demo MS Azure Machine Learning
    • Demo TIBCO Spotfire Predictive Analytics
  • Tool Overview and Advice
    • RapidMiner, SAS, IBM, KNIME, Microsoft, TIBCO, MapR, R, Python
  • Tips and Advice for a Successful Data Science Project
    • Setting up business cases and use cases for Data Science
    • Action plan for Data Science projects
    • Success and failure factors
    • 5 tips to take home

Our courses are conducted by experienced teachers who have gained experience in consultancy practice.


  • Antoine Stelma
  • Erik Fransen
  • Jolanda van Gilst
  • Ferrij Eijgel

Registration form Data Science Essentials





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Cancellation by Connected Data Academy, represented by Connected Data Group B.V.: Connected Data Group will make every effort to allow training to take place but reserves the right to cancel training at any time. Connected Data Group is not responsible for costs incurred by the client as a result of the rescheduling or cancellation of the training. If a training course has been canceled by Connected Data Group, the client will be given the option to transfer the booking to another training course or to have the already paid course fee refunded.

Cancellation by the client: Cancellation is possible up to 5 working days before the start of the training, after which the full registration fee is due. In case of cancellation up to 20 working days before the start, no costs will be charged, in all other cases 50% of the registration fee is due. Cancellation must always be made in writing. The student has a 7-day cooling-off period after signing and can cancel without costs, unless the training has not been planned during this period.

Settlement and invoicing: Unless otherwise agreed and stated as a payment condition, invoicing of the total amount for the training takes place after booking. Payment of the full invoice amount must be made no later than 15 working days before the start of the training. Connected Data Group reserves the right to refuse participation in the training if the invoice is not paid on time. By submitting this form, you order the relevant training. There is a cooling-off period of fourteen days
For the other conditions, Connected Data Group applies the general conditions of FENIT (Filed with the Registry of the District Court in The Hague on June 3, 2003 under number 60/2003)

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We offer a (made-to-measure) in-company training if there are multiple colleagues with the same learning objectives.