© Heikki Topi

Data Science and Computing Education

ACM Education Council

Portland, OR

September 16-17, 2014

Heikki Topi, Bentley University

© Heikki Topi

Data Science: Contributing Disciplines

Statistics

Information

Systems /Information Science

Computer Science

Domain of Practice / Scientific Theme Area

© Heikki Topi

Or… Data Science: Contributing Disciplines

Statistics

Mathematics

Information Systems

Information ScienceEconometrics

Computer Science

Domain of Practice / Scientific

Theme Area

Data Science MethodologiesMachine Learning

Data Management

Data Visualization / Usability

Statistics

Sensors

Programming Environments

Scalable Hardware & Software SystemsSource: Moore – Sloan Data Driven Discovery (Data Science Environments) Initiative

© Heikki Topi

Moore – Sloan Data Driven Discovery Initiative

© Heikki Topi

Disciplinary Integration from the Perspective of Statistics

Source: Nolan & Temple Lang (2010)

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

Sample Degree Program: WPI MS in Data Science

Core Integrative Data ScienceMathematical 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

NYU Master’s in Data ScienceCore

Intro to Data ScienceStatistical and Mathematical Methods for Data

ScienceMachine Learning and Computational StatisticsBig Data Inference and RepresentationCapstone Project

Six electives

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

Observations on Degree ProgramsNames 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

Observations on Degree ProgramsNo 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

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

Follow-up Action?Specific ACM Education decision regarding the

importance of Data Science in the context of computingPeople and resources?

Establishing a task force to deal with specific tasksWhite paperCurriculum guidance Workshop

Industry collaborationE.g., Teradata University Network