Deconstructing the Data Scientist Or Why Ten Heads Are Better than One

1. Deconstructing the Data ScientistOr Why Ten Heads Are Better than One Dr. Donald R. Jones J.C. Wetherbe Professor of MIS April 23, 2014

2. Data Scientist: The Sexiest Job of the 21st Century by Thomas H. Davenport and D.J. Patil

3. …, a PhD in physics from Stanford, … began exploring people’s connections, … He began forming theories, testing hunches, and finding patterns … He could imagine that new features capitalizing on the heuristics he was developing might provide value to users. … But LinkedIn’s engineering team, caught up in the challenges of scaling up the site, seemed uninterested. … were openly dismissive …

4. Who Are These People? If capitalizing on big data depends on hiring scarce data scientists, then the challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive. None of those tasks is as straightforward as it is with other, established organizational roles. Start with the fact that there are no university programs offering degrees in data science. There is also little consensus on where the role fits in an organization, how data scientists can add the most value, and how their performance should be measured.

5. Today’s Goal: To Deconstruct the Data Scientist

7. The Human Side of Big Data “If your goal is to make something good happen with big data, perhaps the most important component is the human one.” “… almost every other major factor of big data production is free or cheap.” Source: BD@W -Davenport

8. The Data Scientist

10. Educational Challenge: The Ten-Year Rule How long does it take to become an expert at something? IT takes ten years 10,000 hours of training You need to know 50,000 chunks of information - Herbert Simon

11. HORIZONTAL vs VERTICAL DATA ANALYSTS • Vertical – Deep knowledge in a narrow field • Horizontal – blend the skills, combine vision with technical knowledge

12. Solution – The Data Scientist Team

13. Few Answers, Lots of Questions • How many of the data scientist’s specialties can be crammed into one person? • What mix of specialized skills works best? • What are characteristics of high performance “data scientist” teams? • What are the implications for curriculum design? Undergraduate, Masters, Doctoral?