Data Science and Computing Education

1. Data Science and Computing Education ACM Education Council Portland, OR September 16-17, 2014 Heikki Topi, Bentley University

2. Data Science: Contributing Disciplines

3. Or… Data Science: Contributing Disciplines

4. Data Science Methodologies Source: Moore – Sloan Data Driven Discovery (Data Science Environments) Initiative Machine Learning Data Management Data Visualization / Usability Statistics Sensors Programming Environments Scalable Hardware & Software Systems

5. Moore – Sloan Data Driven Discovery Initiative

6. Disciplinary Integration from the Perspective of Statistics Source: Nolan & Temple Lang (2010)

7. Sample Degree Program: CMU Computational Data Science, Analytics Track • Introduction to Computer Systems • Core (five out of six): • IS Project Course • Intelligent Information Systems • Machine Learning • Machine Learning for Big Data • Search Engines and Web Mining • Information Retrieval • Seminar in Data Science • Capstone Project • Three electives

8. Sample Degree Program: WPI MS in Data Science • Core • Integrative Data Science • Mathematical Analysis (MA) • Data Access and Management (CS or MIS) • Data Analytics and Mining (CS) • Business Intelligence and Case Studies (MIS or MKT) • Electives • Graduate Qualifying Project

9. NYU Master’s in Data Science • Core • Intro to Data Science • Statistical and Mathematical Methods for Data Science • Machine Learning and Computational Statistics • Big Data • Inference and Representation • Capstone Project • Six electives

10. Bentley University MSBA (Data Science Cluster) • Core • Data Management and Systems Modeling • Optimization and Simulation for Business Decisions • Time Series Analysis • Data Mining • Quantitative Analysis for Business • Intermediate Statistical Analysis for Business • Cluster Electives • Object-Oriented Application Development • Web-based Application Development • Data Management Architectures • Business Intelligence Methods and Technologies

11. Observations on Degree Programs • Names and curricula vary significantly • Justifiably: student expectations and capabilities are very different • Always interdisciplinary, department(s) in charge varies • Not possible without significant contributions from computing disciplines • Scientific theme areas and domains of practice starting to establish their own programs

12. Observations on Degree Programs No unified set of learning objectives or graduate capability expectations No formal model curricula exist Internal university level power struggles continue Note: Many Information Systems master’s degree programs have converted into an analytics program

13. Significant Questions for Computing Education Remain • Do we have the desire and ability to collaborate, particularly if we are not the leading partner • How do we manage a number of competing relationships and offer truly integrated degrees? • Do we need to take specific actions to establish a leadership role in this interdisciplinary space? With whom do we collaborate? • White paper to claim the space • Establishing a model curriculum at the master’s level • Accreditation • Key goal: contribute to the quality of the programs

14. Follow-up Action? • Specific ACM Education decision regarding the importance of Data Science in the context of computing • People and resources? • Establishing a task force to deal with specific tasks • White paper • Curriculum guidance • Workshop • Industry collaboration • E.g., Teradata University Network