Asee peer logo

Designing Undergraduate Data Science Curricula: A Computer Science Perspective

Download Paper |


2018 ASEE Annual Conference & Exposition


Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Computing & Information Technology: Curriculum and Assessment

Tagged Division

Computing and Information Technology

Page Count




Permanent URL

Download Count


Request a correction

Paper Authors


Predrag T. Tosic University of Idaho

visit author page

Predrag Tosic is an early mid-career researcher with a unique mix of academic research, industrial and DOE lab R&D experiences. His research interests include AI, data science, machine learning, intelligent agents and multi-agent systems, cyber-physical/cyber-secure systems, distributed coordination and control, large-scale complex networks, internet-of-things/agents, and mathematical and computational models and algorithms for "smart" transportation, energy and other grids. He is interested in applying data analytics, machine learning, intelligent agent and AI techniques to emerging problems related to large-scale decentralized cyber-physical systems, critical infrastructures and “smart grids”, autonomous vehicles, as well as energy, health care and other domains of major economic and societal impact.

Dr. Tosic holds a doctorate in Computer Science from the University of Illinois at Urbana-Champaign. His doctoral dissertation (2006) was on Distributed AI and large-scale Multi-Agent Systems. Most recently, at Washington State University (2015 - 2017), Dr. Tosic worked on dynamics of large-scale networks, graph pattern mining, Boolean Network models of cyber-physical systems, Internet-of-Agents, as well as AI, data analytics and knowledge engineering applied to problems in health care. While at the University of Houston (2009 – 2012), he did research in machine learning, multi-agent distributed computing and control, data mining and distributed database systems, emerging behavior in complex networks, “smart energy” and computational game theory. During his graduate studies and combined five years of non-tenure-track academic research, he has authored over 70 peer-reviewed publications. He has a versatile R&D experience spanning three different high-tech industries, with both big companies (Cisco Systems and Microsoft) and high-tech startups, as well as with a leading government research lab (Los Alamos National Laboratory). He holds three USPTO patents (IP of Cisco Systems).

In addition to a doctorate in Computer Science, Predrag Tosic holds three master's degrees, two in mathematical sciences and one in CS. Tosic has a considerable teaching and student research mentoring experience. He has enjoyed working with students of a broad variety of ethnic, cultural and socio-economic backgrounds and at different types of academic institutions. He has been actively involved with IEEE – the Palouse Section and is currently President of the Section's Computer Society. He is also an active member of ACM, ASEE and AMS.

visit author page


Julie Beeston University of Idaho

visit author page

Dr. Julie Beeston has both a Master’s degree (from Carleton University) and a PhD (from the University of Victoria) in Computer Science, and she has developed and taught over a dozen courses at the university level. Beyond her teaching experience, she also has over a decade of industry experience as a software developer.

In industry, she has a history of solving ‘unsolvable’ problems. She enjoys a great deal of personal satisfaction when her analytical and problem solving skills can be applied to solve complex technical problems and when she can find creative new ways to pass the things she has learned on to the next generation.

Her first teaching experience was at Ozanam Sheltered Workshop teaching adults with mental and physical disabilities. The experience gave her the opportunity to try unique teaching methods and taught her how to tailor her teaching style to a specific person’s needs. That experience taught her that given enough time any student can master any concept. There is no limiting factor on an enthusiastic student’s ability to learn.

Her primary mission in teaching is to get the students enthusiastic about the subject. She does this by giving real-world examples of how the subject matter she is currently teaching has helped her resolve complex problems in industry.

visit author page

Download Paper |


We discuss opportunities and challenges encountered in developing new undergraduate degree programs that are inherently cross-disciplinary and require institutional and instructional support from different departments and colleges. We have recently been involved in early stages of curriculum development for an undergraduate BA/BS Data Analytics program at a prominent research university in the Pacific Northwest, involving faculty and resources from Computer Science, Mathematics & Statistics, College of Business, and other units at the University. The development of such a new degree program required developing entirely new courses and their syllabi, identifying faculty across the existing academic units suitable and available to teach those new courses, as well as identifying key existing courses across Computer Science, Mathematics, Statistics and Business that should be incorporated into the new degree program. One challenge there, was to ensure providing the solid foundations and a right balance to the future Data Analytics graduates, without making their BA/BS degree program require substantially more credit hours than the existing undergraduate programs in e.g. Statistics, Mathematics or the Data Science "track" in Computer Science.

We share some insights from this curriculum development process, primarily from a computer science perspective. Some issues that we have encountered include the following: what are the most important skills that the future BA or BS graduates in Data Analytics should acquire from computational and computer science standpoints? Is requiring those students to take the existing CS courses on e.g. Data Mining, Machine Learning or Database Systems appropriate, or should new courses (not shared with existing CS curriculum Data Science or similar "tracks") be developed? What is the right balance between training in computational (and mathematical) foundations, vs. various application domains, such as in business or life sciences? One of the key early lessons we have learned: effective, respectful and cooperative communication across the traditional disciplinary boundaries is absolutely crucial for intrinsically cross-disciplinary higher-education undertakings such as developing a new data analytics degree program to be successful.

Tosic, P. T., & Beeston, J. (2018, June), Designing Undergraduate Data Science Curricula: A Computer Science Perspective Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30283

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2018 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015