Application of Data Science in Government Services – IPMA Forum 2016 Speaker Session

Applying Data Science
to
Government Services

Data Science
• Extracting knowledge or insights from data
– in various forms, structured or unstructured
• Utilizes data preparation, statistics, predictive
modeling and Machine Learning
• Applied to various domains
– Discovering new cures, Improving science research
– Optimizing supply chains and delivery routes
– Reducing traffic congestions, Optimizing energy grids
– Forecasting weather, Improving sports performance
– Improving security and reducing spam
– Targeted marketing, personalization, churn prediction
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8-Levels of Analytics (SAS)

Information Strategy (Gartner)
• Enterprise Information Management
– Information is everywhere & growing
– Volume, Variety & Velocity
– Drive innovation in rapid information processing
• Information Strategy
– Harness the power of information assets
– Drive growth, improve efficiency
• Data Analytics – Strategic decision making
– Insights from your large and complex datasets
– Predict future behaviors, trends and outcomes

Machine Learning (ML)
A type of Artificial Intelligence that provides computers with
ability to learn without being explicitly programmed.
– Computer can infer rules inherent in data
– Computer adapts when exposed to new data
• (Tom Mitchell ) - A computer program is set to learn from an
experience E with respect to some task T and some performance
measure P if its performance on T as measured by P improves
with experience E
• Automating Automata

What’s a Machine Learning Problem?
Emphasis of machine learning is on
automatic methods
Devise learning algorithms that do the
learning automatically without human
intervention
Program by example: we don't care what
the machine does, as long as it does it
right
Result-oriented rather than process-
oriented

How can Machine Learning Add Value?
ML is a data driven approach
• Business knowledge isn’t necessary
ML is domain independent
• Same algorithms can be used across domains and in different use cases
ML creates flexible decision systems
• Creates robust systems that can adjust for changing systems without
human intervention

ML and Big Data
ML thrives with big data!
– Accuracy of algorithms increases with size of data
– Statistical approaches can treat big datasets much better than
traditional paradigms
– Decision making using ML can adapt to transactional data much better
Machine Learning Big Data

Fraud Detection: Did the user really do this login/make this purchase?
Product Recommendation: Will the user like this product?
Stock Trading: Will the stock go up or down?
Medical Diagnosis: Given some symptoms, what is the patient
suffering from
Machine Learning Applications

How to Categorize the Problem?
Generally, machine learning problems looks to:
Identify a Value
Assign data points to a category
Discover similarities between two data points

Flowchart
Start
Sufficient
Data?
Sort into
category?
Predict a
value?
Define Problem!
Labeled
Data
Clustering
Classification
Get more!
Regression

What to look for in algorithms:
Flexible across many use cases
Able to handle several input types
Accurate
Resistant to over-fitting/noise/error
Machine Learning Algorithms

Random Forest
Used for classification and regression
Works on small subsets of data and combines the result into the best estimate
XGBoost
Works on classification and regression
Starts off with a weak learner that improves over successive iterations
K-Means
Works on classification and clustering
Tries to find boundaries between data points for each individual variable
Machine Learning Algorithms

Tools and Technologies
Emphasis on tools which
Can integrate with existing data architecture
Have a smooth learning curve
Simplify the process of analysis and prediction
Have an active community

Popular Machine Learning Tools
Python
Free, open-source, widely popular
Consolidates many important libraries in python, C
Has an active community
Disclaimer: Brand names, logos and trademarks used herein remain the property of their respective owners.

Popular Machine Learning Tools
R
Statistical computing language that simplifies complex
statistical operations
Large number of libraries available for extending
functionality (DB connectors, algorithm, visualization)
Disclaimer: Brand names, logos and trademarks used herein remain the property of their respective owners.

Open Data and Gov Services
• Open data, tools and resources available
• ~181K datasets

Sample Applications
• City-Data provides detailed profiles of all U.S. cities -
demographics, crime rates, home values, cost of
living, etc.
• Farmers can use Climate Corporation’s services to
plan, manage, and protect crops
• SPOT Crime : Free public facing crime mapping and
alert website

Conclusion
• Harness the power of your data to deliver
higher value services and remain competitive
–“Data is the currency of the future” –
Michael Cockrill, CIO State of WA
• Machine learning provides a powerful
framework for extracting insights

Thank You

Application of Data Science in Government Services – IPMA Forum 2016 Speaker Session

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Application of Data Science in Government Services – IPMA Forum 2016 Speaker Session

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