Abed Ajraou is a Data & Insights Director, Lead Data Scientist, Data Teacher, Data Speaker and Data Author

1. Abed Ajraou – Director of Data & Insights
& Lead Data Scientist
@First Utility
Putting Data Science in
Your Business: a First
Utility Feedback

2. First Utility – Putting customers in control; saving them money
Cheaper tariffs Great service More knowledge

3. Driving the Success of DS Solutions : Skills, Roles and Responsibilities

4. Source: https://whatsthebigdata.com/2016/05/01/data-scientists-spend-most-of-their-time-cleaning-data/
What have we missed here … ?

5. Right Technology

6. Data – THE NEW POWER
Internal Data
Allow us to deliver a
better service for our
customers
Allow us to optimise the
business and give the
better price to our
customers
Allow us to give more
knowledge to our
customers

7. Industry
Data
Individual
Transaction-Level Data Internal Data
 Better Agility
 Data Lake and Data Warehousing in the
same platform
 Enable Data Discovery
 Collect more data
 Analyse the data with high performance
 Next Gen of Data Visualisation on top of
Hadoop

8. Right Mind-set

9. Start with a business problem
Not considering the business outcome, it’s actually
the first reason of project failure!

10. Start with a business problem

11. Starting with the data and not with the question … ?

12. Right Methodology

13. Explore the data
● Exploratory Analysis by Visualizing the data

14. The creativity part and lot
of trial / error process.
Feature engineering
Andrew Fogg win the competition
by categorising the colours of cars.

15. ● ML is often used in DS
● Currently, the buzz/trend ML is xgboost which gives most of the
time better result than the traditional Random Forest & Neural
Networks.
● Reason of the success? More Accurate, more efficient, easy to
use, customized and distributed.
● Need less spending time in Feature engineering but still need
some creativity.
Models to predict

16. Models to predict: gradient boosting

17. ● ML is often used in DS
● Currently, the buzz/trend ML is xgboost which gives most of the
time better result than the traditional Random Forest & Neural
Networks.
● Reason of the success? More Accurate, more efficient, easy to
use, customized and distributed.
● Need less spending time in Feature engineering but still need
some creativity.
Models to predict

18. Evaluation - validations
● Overfitting/Underfitting
is the biggest fear of a
Data Scientist.
● Cross validation is one
way to protect the
model to not overfit

19. Feedback loop
● ML algorithm is a life system …
like any life specimen, it needs cares !!!
● Learning by his mistakes, it’s the only way
to progress and to fit a real AI model.

20. Bad Methodology
Main reasons:
• No clear business case
• Try to create the best accurate model in the first place
• No agility
• No code version control

21. An iterative delivery is key
Sprint 1
Sprint 2
Main take away:
• Agility is required
• Weekly delivered is highly recommended to avoid
falling to the “tunnel effect”

22. Going forward: AML
Automated
Machine learning

23. Gartner Says
“More Than 40
Percent of Data
Science Tasks Will
Be Automated by
2020”
Source: https://www.gartner.com/newsroom/id/3570917
Automation in Machine Learning is starting

24. Gain in Efficiency
● In the old age of BI world, we gain in efficiency by using ETL tool
rather than scripting codes.
However, ML is often associate with R/Python/Scala coding.

25. Dataiku Flow => enable AML
My favorite app
The Collaborative Data Science Platform: Dataiku

26. Data Science
is nothing
without a team

27. Data Science is a range of skills !
It’s quite rare to get them in a single person
Source: Dsradar.com

28. Thank you
for your attention
Any Questions?
Keep contact:
@AAjraou