Computational Data Science Program

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.

Faculty

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.

Alexander Wiedemann, assistant professor. BA 2013, Mathematics, Tusculum University, Greeneville, Tennessee; PhD 2019, Mathematics, University of South Carolina, Columbia. Interests: complex systems and network dynamics, discrete mathematics, computational complexity, genomics, opinion dynamics, data science, quantitative methods in public safety and policy.

Affiliated Interdisciplinary Program

Data Visualization Minor

Programs

Computational Data Science Major (BS)