1. Microsoft Machine Learning & Data Science Summit
Machine Learning & Data Science Conference
5/29/ :21 AM
Microsoft Machine Learning & Data Science Summit
September 26 – 27 | Atlanta, GA
BR004
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

2. Putting science into the business of data science
Rafal Lukawiecki
Data Scientist
Project Botticelli Ltd
@rafaldotnet

3. Objectives Practical, applied data science
What does business expect from data science(tists)
The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Project Botticelli Ltd and Microsoft make no warranties, express, implied or statutory, as to the information in this presentation.
Copyright © 2016 Project Botticelli Ltd unless noted otherwise. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Project Botticelli Ltd or Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE.

4. Study with projectbotticelli.com Online training PPTs Offer
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Introduction to BI & Big Data
Power BI DAX
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Offer
$50 off annual memberships (exp 31 Oct 2016): 50IGNITE2016

5. Learn with me!
Hands-on 5-day Practical Data Science (SQL+R+Cortana) class:
London 24 Oct technitrain.com/coursedetail.php?c=68
Chicago 7 Nov sqlskills.com/rafal
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Oslo 21 Nov glasspaper.no/rafal
Stockholm 28 Nov cornerstone.se/rafal
More info: projectbotticelli.com/courses

6. Why do my customers need data science?

7. 5/29/2018
Why?
Discover reason behind success, failure
Understand customers, products, patterns
Accurately plan future
Experiment before making decisions
Experiment with autonomous decision making (AI)
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

8. Advanced analytics, or data science, or artificial intelligence?

9. Machine Learning & Data Science Conference
5/29/ :21 AM
Prepare
Explore, visualise
Data science
Acquire
Advanced analytics
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

10. Data science more than data engineering
Scientific method of reasoning applied to data and analytics
Hypothesis, experiments, facts, logical reasoning
+ data engineering

11. Data science Data wrangling (munging), retrieval + storage
Data mining & machine learning
Statistics
Big data
Data science

12. Machine learning Academic discipline Part of IT and computing
Algorithms that detect meaningful patterns in data

13. Machine learning = data mining?
In analytics today: yes
Data mining uses machine learning on flat data

14. AI in practice Machine learning models Business change
Autonomous, “intelligent” decisions
Business change
AI in practice
Artificial Intelligence
System that perceives its environment and takes actions to maximize its chance of success.
Russell & Norvig, 2003

15. Real-world team for advanced analytics
Data expert
Data scientist
Domain expert
Real-world team for advanced analytics

16. Example: hospital readmissions
Does enough data show examples of readmission?
Are the predictable patterns of readmitting?
Can we reduce readmissions? What is readmission?
Example: hospital readmissions

17. Goal to analytics to goal
Start with a (business) goal
Express it as a testable hypothesis (H0 + HA, etc)
Progressively refine the hypothesis through experiments and further models

18. How? Define & initialise a model Train model (process cases)
5/29/2018
How?
Define & initialise a model
Train model (process cases)
Validate model
Use it: Explore or Deploy
Update and revalidate
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

19. Stating the goal State it as a series of hypotheses
Iterate from high-level to specifics
From “Data can reduce readmissions” to “system X that tracks specific set of patient data including […] reduces readmissions by N%”
Experiments may be needed to get data
This requires a budget

20. Demo
Hypothesis: hospital length of stay for elderly patients with dementia significantly differs from those not suffering from it

21. Demo
Hypothesis: car servicing satisfaction ratings influence customer churn and future sales

22. Real world experiments
Design
Target and control group(s)
Run
Introduce the change (promo, pricing, product…)
Evaluate
Statistically compare control and experiment results

23. But, does it all work? Data Driven Decision-making Run on analytics
5/29/2018
But, does it all work?
Data Driven Decision-making
Run on analytics
The more data-driven a firm is, the more productive it is
5-6% increase (1σ) even when controlling for confounding factors
Brynjolfsson, Hitt, Kim, MIT & Penn’s Wharton School, 2011
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

24. Putting science into the business of data science
Human intuition drives innovation
Advanced analytics validates facts
Data science enables Data Driven Decisions
By 2020, 10% of organisations will have a specialised unit for productizing and commercializing data. [Gartner, 2016]

25. Summary Scientific method of reasoning underpins data science
5/29/2018
Summary
Scientific method of reasoning underpins data science
Hypothesis iteration as a method for progressively solving a business problem
Chicago: 7 Nov sqlskills.com/rafal
projectbotticelli.com
BI video tutorials, PPTs, and articles $50 off annual: 50IGNITE2016
projectbotticelli.com/courses
Discover: rafal.net
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

26. 5/29/ :21 AM
Copyright © 2016 Project Botticelli Ltd unless noted otherwise.
Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Project Botticelli Ltd or Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE.
© 2010 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.