2022-2023 Undergraduate Bulletin 
    Mar 28, 2023  
2022-2023 Undergraduate Bulletin

Computational Data Science Program

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Data science is the scholarly discipline that focuses on how to connect data to decisions. This involves the nuances of collecting, managing, analyzing, visualizing, and reporting data for use in decision-making. From public policy to scientific exploration or managerial action, a spectrum of skills and knowledge is needed to convert data to relevant information. All of these skills involve computer programming and computational and analytical thinking.

There is a strong connection between data science and computational science. If data are to inform decisions or answer questions, the nature of the data to be collected, and the feasibility of collecting it must be carefully thought through and analyzed. The collection of relevant data for a project then requires computational skills in web scraping, database creation and navigation, and data cleaning. The computational data science program helps students develop the skills necessary to identify, generate or track down, store and manage informative data from varied sources.

Making conclusive decisions using data requires the skills necessary to do appropriate analyses, nearly all of which would be done in a computing environment. While Hamline’s undergraduate curriculum teaches and uses several tools for this, the computational data science program augments that curriculum with broader and more computationally oriented tools and skills for data analysis.

Communicating data-driven decisions requires thorough, useful, and accurate visualizations which are also created in a computing environment with access to the data. We focus on data visualization in many ways including student poster presentations in the natural and social sciences, business analytics presentations in HSB, and student art installations in the Digital Media Arts program. The computational data science program gives students stronger skills and deeper experiences with this approach, often using data developed in collaborating programs.


Katharine Adamyk, assistant professor. BS 2014, Mathematics and Psychology, Gordon College; MS 2017, Applied Mathematics, University of Colorado; PhD 2020, Mathematics, University of Colorado. Research Interests: stable homotopy theory, algebraic topology, topological data analysis.

Craig Erickson, visiting lecturer. BS 2007, Drake University; MA 2009, Minnesota State University, Mankato; PhD 2014, Iowa State University. Major interests: matrix theory, graph theory, computational mathematics.

Ken Takata, associate professor. PhD 2004, University of Illinois-Chicago. Major interests: discrete math and computer science.

Jasper Weinburd, assistant professor. BA 2013, Mathematics, Bard College; MS 2016, Mathematics, University of Minnesota – Twin Cities; PhD 2019, Mathematics, University of Minnesota – Twin Cities. Major interests: collective behavior, self-organization, dynamical systems, differential equations, mathematical biology and ecology, modeling, data science.


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