Overcoming Data Science Faculty Shortage Via Curricular Partnerships

Concurrent Session 2

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Brief Abstract

Data science is increasingly applied to interdisciplinary degrees and majors. Teaching these skills can be difficult when the talent is scarce. One solution is curricular partnerships that include expert instructors, SME’s, and TA’s.

Presenters

Peter Bruce is Founder and Chief Academic Officer of The Institute for Statistics Education at Statistics.com. He is the developer of Resampling Stats software (originated by Julian Simon in the 1970's), and has also taught resampling statistics at the University of Maryland and in a variety of short courses. He is the author of "Data Mining for Business Analytics", with Galit Shmueli and Nitin R. Patel (Wiley, 3rd ed. 2016; JMP version 2017, R version 2018, Python version 2019), "Introductory Statistics and Analytics" (Wiley, 2015), and "Statistics for Data Scientists", with Andrew Bruce, (O'Reilly 2016). He serves on the American Statistical Association's Advisory Committee on Professional Development, and is a Series Editor for Cambridge Press.

Extended Abstract

Data Science and analytics are hot educational topics. Data science is increasingly applied to interdisciplinary verticals, in government, and in academia. We see researchers using machine learning to address childhood disease in developing countries, applying deep learning to understand religious violence, and building neural networks to enhance cybersecurity at global financial institutions… Teaching these skills can be difficult when the talent is scarce.

One solution is curricular partnerships that include expert instructors, SME’s, and TA’s, shared among multiple institutions in a proven model that provides high quality student completion and satisfaction ratings. Representatives from the Institute for Statistics Education at Statistics.com will describe how dedicated online TA’s support its independent instructors to offer programs that can be articulated or white labeled and distributed through higher educational institutions that lack the capacity to quickly stand up a data science program themselves. The Institute speaker will also review how this process supports the engagement of top experts in the field, including many published textbook authors, as instructors.

Learning objectives - after taking this workshop participants will be able to:

  1. Describe the structure and growth of the data science and analytics education market
  2. Decide which aspects of data science education (programming, analytics, IT, etc.) are most suitable for the educational market they serve
  3. Recognize the role that data-centered projects play in data science education
  4. Combine expert instructors with highly qualified TA’s to deliver top quality online instruction
  5. Take steps to build their own data science education program using outsourced instructional resources