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

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Harbinger Systems conducted a session on ‘Application of Data Science in Government Service’ at the IPMA Forum 2016 conference and expo in Lacey, WA. Read through the conference highlights.

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

  1. 1. Applying Data Science to Government Services
  2. 2. 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 © Harbinger Systems | www.harbinger-systems.com
  3. 3. 8-Levels of Analytics (SAS) © Harbinger Systems | www.harbinger-systems.com
  4. 4. 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 © Harbinger Systems | www.harbinger-systems.com
  5. 5. 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 © Harbinger Systems | www.harbinger-systems.com
  6. 6. What’s a Machine Learning Problem? © Harbinger Systems | www.harbinger-systems.com 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
  7. 7. How can Machine Learning Add Value? © Harbinger Systems | www.harbinger-systems.com 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
  8. 8. 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 © Harbinger Systems | www.harbinger-systems.com Machine Learning Big Data
  9. 9. 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 © Harbinger Systems | www.harbinger-systems.com Machine Learning Applications
  10. 10. © Harbinger Systems | www.harbinger-systems.com 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
  11. 11. © Harbinger Systems | www.harbinger-systems.com Flowchart Start Sufficient Data? Sort into category? Predict a value? Define Problem! Labeled Data Clustering Classification Get more! Regression
  12. 12. © Harbinger Systems | www.harbinger-systems.com 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
  13. 13. © Harbinger Systems | www.harbinger-systems.com 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
  14. 14. © Harbinger Systems | www.harbinger-systems.com 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
  15. 15. © Harbinger Systems | www.harbinger-systems.com 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.
  16. 16. © Harbinger Systems | www.harbinger-systems.com 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.
  17. 17. Open Data and Gov Services © Harbinger Systems | www.harbinger-systems.com • Open data, tools and resources available • ~181K datasets
  18. 18. 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 © Harbinger Systems | www.harbinger-systems.com
  19. 19. 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 © Harbinger Systems | www.harbinger-systems.com
  20. 20. Thank You © Harbinger Systems | www.harbinger-systems.com

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