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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 |
  3. 3. 8-Levels of Analytics (SAS) © Harbinger Systems |
  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 |
  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 |
  6. 6. What’s a Machine Learning Problem? © Harbinger Systems | 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 | 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 | 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 | Machine Learning Applications
  10. 10. © Harbinger Systems | 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 | Flowchart Start Sufficient Data? Sort into category? Predict a value? Define Problem! Labeled Data Clustering Classification Get more! Regression
  12. 12. © Harbinger Systems | 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 | 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 | 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 | 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 | 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 | • 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 |
  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 |
  20. 20. Thank You © Harbinger Systems |