Demystifying the Data Scientist

1. Demystifying the Data Scientist

2. Dan McClary, Ph.D. • Define Oracle Big Data products • Help Oracle customers find Big Data solutions • Previously • Startups • Howard Hughes Medical Institute @dan_mcclary • Principal Product Manager: Big Data and Hadoop • Oracle Corporation

3. Data Scientists: By the Numbers • 1: What’s a Data Scientist? • 2: Do I need a Data Scientist? • 3: How do I grow my own Data Scientists?

4. What’s a Data Scientist?

5. What’s Data Science? • The buzz around “Data Science” is growing • But isn’t it a bit like saying “chai tea?” • What is a functional definition for data science? • Buzzword or Essential New Discipline?

6. A Working Definition • Data Science seeks to • Extract meaning from data • Create “data products” • Use all available data to tell a valuable story to non-practioners • So what makes a Data Scientist?

7. Anatomy of a Data Scientist Business sensibility Machine Learning Statistical analysis Visualization Pattern Recognition BI Tools Competitive Intelligence Scientific training Production-grade programmer in Java? Python? SQL Hadoop PhD in Computer Science? Statistics? Physics? Biology?! Published researcher IT Operations Databases Big Data Design Sensibility Graph Theory Excellent Communicator/Presenter

8. Anatomy of a Data Scientist Does anyone like that even exist?

9. Anatomy of a Data Scientist: Revised • Value Proposition • Goals • Communicate • Results A person who has some degree of experience in each of • Techniques • Interpretation • Model • Requirements • Integration • Manipulation • Quality • Assurance

10. Do I Need a Data Scientist?

11. Do You Need a Data Scientist? • Do you need an army of PhDs to solve machine learning problems? • Probably not • Could you find more value in the data you do and can collect? • Undoubtedly • Do you need people to find that value • Almost certainly

12. Fitting for Data Scientists • Most organizations will benefit from a few seasoned data scientists • Help transition to a more data-driven business • Direct efforts to integrate analytics more tightly with LoBs • Good understanding of how to tackle new problems • Data scientists can be grown at home • Leverage the existing workforce • Provide growth opportunities for employees • Who? Where? How many?

13. How do I grow a Data Scientist?

14. Step #1: Find Motivated Individuals • Developers who want to • Become more statistically oriented • Better understand business challenges • Business Analysts who • Have some programming ability • Want to grow their technical capabilities • All candidates should • Possess tremendous curiosity • Be able to self-manage • Sources for Good candidates

15. Step #2: Find Low-Hanging Fruit • Find a project that has • High ROI • Limited, defined scope • Isn’t impossible • Define • The business value • The time to invest • Analytically Important, Not Impossible Value to Business Time to Answer

16. Step #3: Combine • Add your data science team • And the well-defined project • Add a seasoned data scientist for best results • Watch the team grow new skills • Evaluate the outcome • For the team members • For the business • Then Rinse and Repeat

17. Summary • What is a Data Scientist • Someone who can help drive value through data • Do you need one? • Possibly • Can you grow a data scientist • Absolutely

18. Q&A