• What’s in it for me: The AI elephants in the room
• Explorer mind & scientific approach
• Mapping experimentations
• Knowing our limits

• Remembering how your brain works
• Models are everywhere
• Building your model toolbox

• From problems to models
• Reference frames
• All about that base rate & other fallacies
• Modelisation post-Covid

• An introduction to AI/ML and how to build a curve with LEGO blocks
• Starting with a good foundation
• About fitting, drifting & the many challenges of ML

• Science & people network
• Diversity Prediction Theorem
• Solving for complexity, biases & noises: Many heads, many perspectives, many solutions

• The WHY: causes & effects
• The Bayesian approach
• Introduction to Causal Inference

• How to communicate insights from modelization
• Sharing complexity simply
• Storytelling, analogies, and visuals

• The Journey & the many paths to insights
• What’s on the horizon of data sciences?
• The relentless pursuit of knowledge

Part 1:

• An efficient approach for data science in Alteryx
• Keeping track of your experiments

Part 2:

• Investigate your data
• The art of modelization

Part 1:

• Getting started with ML: From Regression to Forest Model

Part 2:

• Adding more strings to our ML bow: From Support Vector to Neural Net

Part 1:

• Predictive Grouping: From principal components to clusters

Part 2:

• Let’s do some simple Python: Natural Language Processing

Part 1:

• It’s about time: Time Series modeling with ARIMA, ETS & more

Part 2:

• The Power of AB testing
• A brief introduction to Network Analysis