Data Science (DATA)

Professors: P. L. Shick (Chair), B. Foreman, M. Kirschenbaum, D. W. Palmer, L. Seiter; Assistant Professors: E. Manilich, W. Marget, R. Fang

Major Programs

The Department of Mathematics and Computer Science offers a bachelor of science in Data Science. The department also offers Mathematics (MT), Computer Science (CS), and Computer Science with Healthcare Technology (CS HCT) programs that are described in separate sections.

Data science is an emerging academic discipline, a response to an increasing demand for people who are able to understand and analyze data. Data science provides powerful approaches for transforming large and complex data into information, knowledge, and intelligent decisions. John Carroll University’s Data Science program utilizes an interdisciplinary approach with a focus on statistics, computer science, and mathematics applied to a specific discipline such as communications, digital humanities, entrepreneurship, exercise science, biology, physics, political science, psychology, and sociology. The major in Data Science leading to the B.S. prepares students to explore the complex relationships between data, technology, and society. Data is everywhere, so data science skills are increasingly critical in almost every discipline. Data science careers span the spectrum of possibilities from working for national companies with large data analytics departments to founding entrepreneurial start-up companies at the frontier of future technology, while filling critical roles in all fields, including medicine, business, arts and entertainment, sports, government, law, manufacturing, and research.

Program Learning Goals in Data Science.

Students will:

  1. Data Acquisition: Collect, store, preserve, manage, and share data in a distributed environment through practical, hands-on experience with programming languages and big data tools.
  2. Problem Exploration: Develop problem-solving skills through experiences that foster computational and data analytic thinking.
  3. Analysis: Develop an in-depth understanding of the key technologies in data science: data mining, machine learning, visualization techniques, predictive modeling, and statistics.
  4. Domain knowledge: Experience discipline-specific data use cases in order to solve real-world problems of high complexity.
  5. Interpretation: Learn methods for effective data communication and visualization, and demonstrate their use in data representation.
  6. Social Value: Explore social and ethical implications of the use of data and technology.