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Data Science Pathway

Data Science Pathway

Feb 08, 2022

Data Science Focused Working Group Team 

Team Leads and Steering Committee Liaisons

  • Alicia Rosburg

  • Joshua Sebree

Team Members  

  • Maureen Clayton

  • John DeGroote

  • Bryce Kanago

  • Syed Kirmani

  • Sadik Kucuksari 

  • Chris Larimer

  • Alexksandar Poleksic

  • Ali Tabei

Data Science Pathway Summary Concept

UNI will establish an advantage in data analytics and science by developing a transdisciplinary data science hub. The concept of a hub allows expertise and resources to be centrally located, reducing redundancy. The hub concept supports campus by providing coursework and supporting scholarship, rather than duplicating efforts and hires across departments. Curriculum can be modularized so that multi-course sequences of content can be adopted within multiple programs across campus. 

Goals

  1. Establish marketable programming in data science that shares resources and provides access to all academic units.

  2. Develop a framework for improved multi-, inter-, and transdisciplinary programming.

Prioritized Objectives

  1. Strengthen our position within data analytics fields by identifying stand-alone majors/minors/certificates, as well as integrated modules in multiple majors

  2. Identify strategies to leverage existing expertise and centralize future hiring efforts, including joint appointments, to minimize redundancy

  3. Remove siloes around data science, opening it up for cross-disciplinary applications in sciences, education, social science, humanities, and/or business

  4. Pioneer new modular curricular model, leading the way for stackable credentials

Equity, Diversity, and Inclusion Objectives

  • Identify obstacles to achieving equity and inclusion in the data sciences

  • Address the obstacles with adaptive solutions 

  • Recommend innovative strategies to enhance data sciences at UNI for a diverse student and faculty body

Why this pathway

Data science is a small but fast-growing field with projected double-digit job growth in the next 10 years. Also, data analytics will be expected skills in other fields of expertise. UNI is well behind the curve in developing these fields, and we need to establish our presence and develop our unique niche. With the exception of the MIS in College of Business, UNI has a limited market share in data science degrees. While there is moderate competition for 4-year degrees, there is also an opportunity to partner with community colleges and market data suggest significant growth in both student interest and employer need in the coming years. 

How this pathway will impact UNI

A multi- or transdisciplinary hub model is likely to be staffed primarily through joint appointments, the impact on existing programs is minimal. However, the outcomes of the hub should allow individual programs to embed data science experiences (and possibly micro-credentials) into their majors with limited financial and personnel investments. Data analytics and science will provide a template for stackable credentials, helping other areas of campus to utilize these opportunities.

Data Science FWG Updates 

 

Data

Fastest Growing Occupations by Meta-Field

Data Science Related Degrees Awarded in Iowa

Market data on in-demand data Science majors, and top data professions - focused on percent

Recombined market data from Stamats focused on absolute values

Background on Stamats Data