This document discusses applied data science and machine learning. It provides an overview of machine learning concepts like learning from data and choosing the best model for a given problem. It then discusses how companies often focus more on proofs of concept and presentations rather than implementing machine learning solutions. The document outlines three hurdles to implementation: oversimplifying requirements, the "Kaggle curse" of focusing only on model accuracy, and ensuring data and skills are production-grade. It provides recommendations like having a clear business case, automating data workflows, and hiring data scientists skilled in programming for real-world impact.

1. APPLIED DATA SCIENCE
Giovanni Lanzani – Chief Science Officer
GoDataDriven
@gglanzani

2. WHO AM I
Italy
01
Leiden
University
02
KPMG
03
GoDataDriven
04

4. WHAT IS MACHINE LEARNING

5. LEARNING
FROM DATA
• You have some (lots) of data
• You need to generalize

6. BEST MODEL
• Which one would you
choose here?
• It’s about making a tradeoff
• This trade off is the most
important job of the PO
• A 100% correct answer
might not exist!!!

7. WHAT’S DATA SCIENCE

8. ULTIMATELY
• It’s about creating value from data
• Using Machine Learning,Advanced Analytics, and
visualization

9. WHEN YOU SAY DATA SCIENCE,
COMPANIES UNDERSTAND
• All the things big data
• Predictive modeling & Advanced Analytics
• More money
• Do all the cool things the others are doing

10. HOW TO GET THERE

11. TRADITIONAL
DATA WAREHOUSE
ARCHITECTURE
EDW
Data consumer
Web app
Dashboard /
Reporting
Traditional
Business app

12. AND NOW?
?
Data consumer
Web app
Dashboard /
Reporting
Traditional
Business app
API

13. WHAT COMPANIES
GOT
• A lot of POCs
• A lot of
screenshots/presentations/dashboards on a
laptop
• Nice stories to tell to their network, about
those screenshots and especially those
dashboards
• Headaches with data and infra even more
scattered

14. BUT…
• We got a data scientist working on trees, and forests
• Neural networks!
• Deep learning!!!

15. WHAT DO COMPANIES ACTUALLY NEED
• Put things into production
• They don’t teach that in any data
science course or MOOC (that I
know)

16. THE THREE
HURDLES
Credit to Jon Shave gdd.li/lavaredo

17. OVERSIMPLIFYING
Requirements
Data
Sources
Exploration
Modeling
Products
Feedback
Data scientist
ML
engineer
Data
engineer
Data
engineer

18. KAGGLE CURSE
• gdd.li/toldYouSo
• Many data scientists approach the problem at
hand with a Kaggle-like mentality: delivering the
best model in absolute terms, no matter what
the practical implications are.
• In reality it's not the best model that we
implement, but the one that combines quality
and practicality: a continuous balancing act
• Netflix competition

19. SOLVING
THEM

20. BUSINESS CASE
Business case for
• True Positives
• True Negatives
Cost of
• False Positives
• False Negatives

21. DATA
Data {insert something
here} should be pro
grade

22. SKILLS
• Participate in actually building production
quality systems OR being proficient enough in R
or python to hack together a prototype on a
very small dataset?
• Supply of the second group keeps growing while
demand is flat or shrinking
• Especially as executives get burned by “data
scientists” who don't know how to help them build
things of value

23. HIRING
• Companies that are not engineering driven, often have
trouble hiring good technical people
• The “IQ” test is not really representative of applied data
science
• At GoDataDriven we do a “at home, at your convenience”
assessment
• Real dataset, real business question, real product
• Models are software: treat them as such

24. TAKEAWAYS
• POs should know “their stuff”
• Automate all the data movements
• Hire data scientists that are good at programming (or hire machine learning
engineers)

25. QUESTIONS?
• We’re hiring
• Data & Machine Learning Engineers!
• career@godatadriven.com