An alarm goes off in a nuclear power plant
Then another. And another.
The first was a small mechanical failure
Cascading failure
Cascading failure of humans
Everything Else vs Data Science
Part 1: Identifying Customer Pain & Designing a Potential Solution
People don’t pay for algorithms. They pay for solutions.
Interviewing Users
“Tell me about a time when…”
“Imagine the ideal situation…”
Product Vision: Imagine a good place
Product Vision: Ambitious future
What is your product vision?
Predicting a value? Suggesting an action? Recommending something?
Supervised Machine Learning
Supervised Machine Learning: Classification
“Using ____________ data I can predict (classify) __________”
It appears you are experiencing a nuclear meltdown
It appears you are experiencing a nuclear meltdown
Part 2: Finding Data, Prototyping, and Testing
Finding data
Finding data
Can we find the data elsewhere?
Collaborative Filtering
Mini version of the vision
Part 3: Measuring Success, Iterating on Design, and Shipping
Explainability v Complexity
Explainability v Complexity
Explainability v Complexity
Juggling requirements
Juggling requirements
Final Thoughts
Data Science + Magic = Profit
Data Science + (Well Understood Customer Pain + Thoughtful Design) = Profit
Data Science is part of the solution
Data Science is not the entire solution
References