Elke Rundensteiner

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1 Do s and Don ts (Process) For Data Science Program Development Elke Rundensteiner rundenst@cs.wpi.edu

2 Data Science Degree at WPI?

3 Develop A New Program? What s new? Why? What is in it for you? How to do it?

4 Developing New Program WPI - No Silos Tradition of Interdisciplinary Programs Interactive Media & Game Development (Art) Learning Sciences (Research) Robotics and Engineering (Eng) Bioinformatics and Comp. Biology (Science) D a t a S c i e n c e???

Development (Art) Learning Sciences (Research) Robotics and

5 Why Data Science? I ve been doing it for 20+ years Advanced data science tools Impact of data science It s all around us Practitioners : my doctor Scientists: my husband Everyone : my kids It s here to stay It s the future `

6 Context : Big Data Big Data : Technological advances in devices, software, and networking give rise to digital data that are rich in variety, volume, velocity and complexity, generated and collected at a unprecedented scale Exceptional opportunity : to extract insights important for competitive businesses and innovation, health, science and societal well-being US Competitiveness: Data Science represents basis for competition, underpinning new waves of productivity, growth and innovation [by MGI and McKinsey's Business Technology Office] Unfullfilled Need: The United States faces a shortage of close to 200,000 workers with deep analytical expertise and 1.5 million managers and analysts with the skills to make decisions based on the analytics of big data [

Competitiveness: Data Science represents basis for competition, underpinning new waves of productivity, growth and innovation [by MGI and McKinsey's Business Technology Office] Unfullfilled

7 Process at WPI Towards Data Science? University Lecture: Dr. Patricia Florissi, EMC Open Call by Dean Oates: 25+ Faculty Self-Organize into Working Groups Big Data Seminar Series Big Data Center / Business Model Big Data Infrastructure Big Data Web Presence Big Data Degree Check Point with Working Groups

into Working Groups Big Data Seminar Series Big Data Center / Business Model

8 Where are we?

9 Big Data Degree Group Organic & From the Ground Up By-in from Administration (Dean) Interest from Corporate Education Curiosity from Faculty from Many Disciplines

10 Process of Big Data Degree Group Do we want to do this? Who are we? What do we have? What is the demand? What do we want? What do others offer?

11 Big Data Degree Group What Level of Degree: BS MS PhD Find a Name for Degree: Big Data Analytics Big Data Analytics Data Science

12 Data Science Degree Group Large Group Dindled Down : 6 Core Faculty We met roughly once a week this semester Status: We have a Degree Proposal Ready

13 Data Science Business Intelligence Statistics Computing Data Scientists with Interdisciplinary Skills

14 Market Analysis Existing programs housed often in one school and department -- tainted by one discipline. [ McKinsley s Report & Educational Advisory Board Report.]

15 M.S. Degree in Data Science Leverage University Resources Involve Existing Faculty Fit into WPI Structure

16 WPI s Proposal of an M.S. Degree in Data Science Truly interdisciplinary across multiple departments and even schools, namely: Computer Science Department School of Business Mathematical Sciences with Social and Policy Studies Learning Sciences Bioinformatics and Computational Biology

departments and even schools, namely: Computer Science Department

17 Core Data Science Coursework Integrative Data Science (Must take all) Mathematical Analytics (Select one) Data Access and Management (Select one) Data Analytics and Mining (Select one) Business Intelligence & Case Studies (Select one) Practical Impact Graduate Qualifying Project, or MS Thesis

one) Data Analytics and Mining (Select one) Business Intelligence & Case

18 The Data Science M.S. Degree Graduate Qualifying Project, or MS Thesis Concentration and Electives Mathematical Analytics Data Management Data Analytics Business Intelligence Introduction to Data Science

19 Flexibility & Richness Enable a student to apply core Data Science Skills within a chosen area of concentration, such as: Business Healthcare Finances Sciences Learning Sciences

20 Professional Skills Team building Leadership Communication Writing Story Telling Interdisciplinary Interaction Incorporate this into Introductory Data Science Core Provide Further Exposure in Other Courses In-depth Graduate Qualifying Project Modules (on-line) or MOOCs

Introductory Data Science Core Provide Further Exposure in Other

21 Huge Opportunity for Our Students BIG DATA

22 Opportunity for Our Faculty BIG DATA Get Access to Data Get Involved in Starting Projects Develop Industrial Partnerships Leverage Contacts for Research Activities Enjoy Interdisciplinary Colleagues

23 Opportunity for Institution BIG DATA Attract Top Student Talent Help Solve A Major Societal Problem (Demand) Excellent Revenue Stream

24 Challenges Admission : Getting the Right Students Support : Resources to Run a Truly Compelling Program Agile :Growing the Program as Data Science Matures

25 Taking Our First Steps

Ph.D. in Bioinformatics and Computational Biology Degree Requirements

Ph.D. in Bioinformatics and Computational Biology Degree Requirements Credits Students pursuing the doctoral degree in BCB must complete a minimum of 90 credits of relevant work beyond the bachelor s degree;