Learning to make use of Jupyter to document your Data Science process - real time - and in whatever programming language you want! Using this methodology will allow you to provide insights that help your organization make better decisions to solve their business problems.

1. 2015 Healthcare Data Science
Practical Data Science: The WPC Healthcare Strategy for
Delivering Meaningful Data Science Projects
Damian Mingle
@OPENDATASCI

3. Representative Clients
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4. What’s the Problem?
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A Common Scenario: Johnny Data Scientist
• Does not like working with others
• Too much black magic – not enough explanation
• His process is always different
• Uses multiple languages
• Hates producing presentations
• Constant unclear project status
• Doesn’t capture business needs
• Models aren’t production quality

5. Why Jupyter?
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Interactive Computing Environment
• Notebook Web Application: Writing and running code
interactively
• Kernels: Over 40 programming languages
• Notebook Documents: Self-contained documents which
include: Live code, Interactive widgets, Plots, Narrative text,
Equations, Images, and Video

6. Why a Data Science Methodology?
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Data Science Projects Involve Risk
• Strategically: Provides confidence to the business that Data
Science projects can be delivered profitably
• Tactically: Management can understand status assessments
• Operationally: Empowers the Data Science team to do the
right thing, the right way, the first time.

7. Business Understanding
Uncover important factors at the Start
• Determine business objectives
• Assess situation
• Determine data science goals
• Produce project plan
Understand the
Data Science
project objectives
and requirements
from a business
perspective. Then
convert this
knowledge into a
Data Science
problem definition
and preliminary
plan designed to
achieve the
objective.
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8. Exercise 1
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https://github.com/drmingle/Boston-Data-Festival-2015/tree/master/Exercises

9. Data Understanding
Become familiar with the data
• Collect initial data
• Describe data
• Explore data
• Verify data quality
Identify data
quality problems,
discover first
insights into the
data, and/or
detect interesting
subsets to form
hypotheses
regarding hidden
information.
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10. Exercise 2
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https://github.com/drmingle/Boston-Data-Festival-2015/tree/master/Exercises

11. Data Preparation
Construct the Final Dataset
• Select data
• Clean data
• Construct data
• Integrate data
• Format data
Data Science task
in this phase have
to do with
selection of table,
record, and
attributes. In
addition,
transformation
and cleaning of
data.
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12. Exercise 3
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https://github.com/drmingle/Boston-Data-Festival-2015/tree/master/Exercises

13. Modeling
Various Modeling Techniques Are Selected
• Select modeling technique
• Generate test design
• Build model
• Assess model
In this phase,
calibrating
parameters is
important. Some
techniques may
require the Data
Scientist to go
back to the data
preparation phase.
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14. Exercise 4
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https://github.com/drmingle/Boston-Data-Festival-2015/tree/master/Exercises

15. Evaluation
Review Your Steps with Certainty
• Evaluate results
• Review process
• Determine next steps
At the end of this
phase, a decision
on the use of the
data science
results should be
reached.
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16. Exercise 5
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https://github.com/drmingle/Boston-Data-Festival-2015/tree/master/Exercises

17. Deployment
Make Use of The Model
• Plan deployment
• Plan monitoring and maintenance
• Produce final report
• Review project
This phase can be
as simple as
generating a
report or as
complex as
implementing a
repeatable data
science process
across the
enterprise.
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18. Exercise 6
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https://github.com/drmingle/Boston-Data-Festival-2015/tree/master/Exercises

19. Data Scientist 2.0
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Lead Analytically Your Organization
• Use Jupyter to document your process – real time – using
whatever language you want!
• Establish a Data Science Methodology that is comprehensive
• Provide insights that help the organization make better
decisions to solve their business problems

20. Have Questions?
E-mail: dmingle@wpchealthcare.com
Twitter: @damianmingle
LinkedIn: DamianRMingle