Upskill your team

Our data Academy offers a set of training tailored courses to:

  • Infuse a data-driven culture
  • Leverage your digital transformation
  • Get your team data ready
  • Help your team grow, improve its skills, and flourish


Experienced Users

Data & AI Experts


Our Data Academy offers nine main modules which are aimed at our three types of audience. Course content can be tailored to your team’s prior knowledge and expertise.

Soft Skills

  • #1 - Data Visualisation

    Have a deeper understanding of the fundamentals of data visualisation.

    Become familiar with the Spotfire tool.

    Master the practices of effective communication on data.

  • #2 - Driving Innovative Projects

    Comprehend the fundamental principles which bring together business and IT teams in implementing innovative projects .

    Understand the various challenges of implementing new business approaches, and to solve them with an efficient strategy .

  • #3 - Data Culture and Data Governance

    Understand the foundations required to infuse data literacy in businesses when undertaking a digital transformation.

    Comprehend the challenges of data governance.

    Understand how to implement data governance in an organisation.

Data Boot Camp

  • #4 - Introduction to Data Science and Machine Learning

    Comprehend the various challenges of Machine Learning.

    Understand the mathematic and algorithmic foundations of supervised and unsupervised Machine Learning (supervised classification, SVM, proximal methods, unsupervised classification, K-Means, spectral clustering).

  • #5 - Introduction to Deep Learning

    Understand deep learning algorithms.

    Acquire the foundations to develop solutions integrating technologies, such as theoretical aspects of deep learning, initiation with Tensor Flow or Pytorch.

  • #6 - Quantitative Asset Management

    Understand the different techniques for weighting portfolios and indices.

Technology Focus

  • #7 - Introduction to Python for Data Science

    Presentation and implementation of the main algorithms: logistic regression, linear regression, random forest, boosting.

    Gradually move from general Python programming to Python programming for Data Science.

  • #8 - Modeling and querying data for data sciences (SQL or NOSQL)

    Translate business needs into technical requirements.

    Define your data architecture.

    Set up and bring to life your data model, its evolutions, and performance.

  • #9 - Data Security and Business Continuity

    Understand the challenges of securing Big Data architectures.

    Evaluate the business and regulatory impacts.

    Identify the functions and tools to be implemented.

    Ensure the efficient and smooth daily workings of the business continuity of a Big Data system.

    Set up backup and business continuity plan, when implementing a Big Data system.

Practical Details


Prior to each training program, we meet with HR and Business Managers to:

  • Identify the objective of this course
  • Analyse the capacities, and needs, of our audience
  • Identify the best suited format for our audience 
  • Plan and organise all necessary tools 

Timing & Format

  • Sessions are from half-day sessions up to two full days
  • Face-to-face or remote teaching
  • Inter or intra company sessions
  • Sessions can be delivered either in French or in English


Each session includes:

  • Case studies to work in group
  • Practical cases explained by the trainer
  • Knowledge assessment at the end of the training
  • Series of presentations and exercises looked at in class
  • Evaluation Form questionnaire

Our latest blog posts

data visualisation
Christophe Le Lannou

Data Visualisation Best Practice [PDF]

A presentation by Sophie Sparkes, data analyst at Tableau Software, about the best practice in data visualisation.
On December 2015 for the ‘Society of Data Miners’

Read More »