Predictive Analytics - it's not just for scientists
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Transcript of Predictive Analytics - it's not just for scientists
Lee Hawthorn, Acma, CGMA, Ba(Hons)
Many roles over the years Software engineer
Finance Manager
Business Analyst
Motivated to learn and share, to solve problems
Current : Data Analyst @ Payzone UK
uk.linkedin.com/in/leehawthorn/
@lee_hawthorn
Blog at Leehbi.com
Stories in the data
WHY DO
CUSTOMERS
CHURN
WHERE IS THE
OPTIMAL
LOCATION FOR
ASSETS
HOW CAN WE
IMPROVE THE
MARKETING
CAMPAIGNS
HOW CAN WE
REDUCE FRAUD
Data Mining History
1990’s
Academia
2000-2010
Largest Companies
2010+
Increasing
demand
2013+
Cloud/Mature
Apps/Services
2015
Black box?
Data revolution
AZURE MACHINE
LEARNING
RAPID MINER
POWER
QUERY/PIVOT
COMMUNITY
DEVELOPMENT
DATA MOVING TO
THE CLOUD
ROBUST
ALGORITHMS1
110 Types of regression, which one to use?
How do we begin?
Cross Industry Standard Process for Data Mining
CRISP-DM
Demo 1
We want to predict the number of
transactions in potential stores that we are
considering to recruit.
Quantitative number leads us to regression.
Keep it simple – Linear Regression
Demo 2
We want to predict the category of customers that will purchase our new e-book reader
For marketing purposes we should classify Early Adoptors to Laggards.
This is a classification problem – we’ll use decision trees.
Best Practices
Executive buy-in
Get the subject matter experts involved early
If you need IT help give them plenty of notice
Document the whole process
Make it repeatable
Challenge and test the model yourself
Use third-party support if you need it
Enjoy yourself
Next Steps
Do you have a skill gap?
Database skills - http://geekgirls.com/category/office/databases/
Statistics skills – Learn R : https://www.coursera.org/specialization/jhudatascience/1?utm_medium=dashboard
Coding skills - https://www.codeschool.com
Join a user group –
http://bavc.sqlpass.org/
http://www.londonr.org/