Bachelor of Science in Data Science

Faculty and Fields of Interest

Ramprasad Balasubramanian (CIS) computer vision, robotics, artificial intelligence

Paul Bergstein (CIS) software engineering, database systems

Yanlai Chen (MTH) numerical analysis, scientific computing, computational partial differential equations, dimension reduction, model order reduction, reduced order modeling, uncertainty quantification, fractional-order partial differential equations, data mining, machine learning, image processing, neural networks, high performance computing

Gary Davis (MTH) memory systems, DEs, mathematics education, data science education

Bo Dong (MTH) numerical analysis, scientific computing, discontinuous galerkin finite element methods, data science

Hua Fang (CIS) computational statistics, machine learning, pattern recognition, behavioral trajectory patterns, wireless health

Scott Field (co-director) (MTH) Bayesian inference problems, gravitational wave data science, scientific and high performance computing

Sigal Gottlieb (MTH) strong stability preserving and positivity preserving time discretizations, spatial discretization for hyperbolic problems, spectral and pseudospectral methods, WENO and ENO methods, reduced basis methods, high performance parallel computing, data science

Adam Hausknecht (MTH) software for mathematics education, computer graphics, scientific computation, noncommutative algebra, data science

Alfa Heryundono (MTH) radial basis functions, spectral and pseudospectral methods, numerical conformal mapping, tear film dynamics, mathematical problems in industry, numerical pdes, data science

Firas Khatib (CIS) bioinformatics, crowd-sourcing

Saeja Kim (MTH) computational algebra, edge detection, applied mathematics, topological data analysis, mathematics education, data science education

David Koop (co-director) (CIS) visualization, data science environments, computational provenance

Ming Shao (CIS) transfer learning/domain adaptation, deep learning, large-scale graph approximation/clustering, social media analytics

Maoyuan Sun (CIS) visual analytics, information visualization, human-centered machine learning, human-computer interaction

Iren Valova (CIS) artificial intelligence, neural networks, image processing

Cheng Wang (MTH) numerical analysis, numerical PDEs, data science

Donghui Yan (MTH) statistics, machine learning, data science

Xiaoqin Zhang (CIS) multi-agent systems, intelligent agents, e-commerce

The Data Science degree, jointly offered by Computer Science in Engineering and Mathematics in Arts & Sciences will provide undergraduates with education and training in the rapidly emerging fields of data analytics and discovery informatics, which integrates mathematics and computer science for the quantification and manipulation of information from a cognate area of application (e.g., science, engineering, business, sociology, healthcare, planning). Emphasis is placed on merging strong foundations in information theory, mathematics and computer science with current methodologies and tools to enable data-driven discovery and problem solving.

Students will be prepared for leadership positions in data analytics, information management, and knowledge engineering. Students will have opportunities to work on industry, agency or faculty sponsored research projects. Students may also participate in co-op and internship opportunities where they can gain valuable hands-on experience sought by employers locally, nationally, and globally. Upon completing the program, graduates will have skills in computer programming, statistics, data mining, machine learning, data analysis and visualization that enable solving challenging problems involving large, diverse data sets from different application domains.

Program Goals

The goals of the Bachelor’s degree program in Data Science are to:

Expand education opportunities in rapidly growing areas of information technology and information systems;
Offer state-of-the-art technology-based courses in data analysis, data mining, statistical modeling, and data visualization;
Prepare graduates with entry-level skills for managing, understanding, interpreting and communicating database and information needs of a wide variety of producers and consumers;
Stimulate and assist the development of computationally-focused options within existing departments;
Educate and train students to work in industry or academia as data scientists; and,
Broaden and deepen the basic data science education in computer science, mathematics, and statistics, with real-life data science projects in cognate disciplines, including Accounting, Biology, Chemistry, Decision & Information Sciences, Engineering, Finance, Marketing, Nursing, Physics, Political Science, and Sociology.

Student Outcomes

At the time of graduation, students will:

be able to apply contemporary techniques for managing, mining, and analyzing big data across multiple disciplines;
be able to apply computation and computational thinking to gain new knowledge and to solve real-world problems of high complexity;
be able to communicate their ideas and findings persuasively in written, oral and visual form and to work in a diverse team environment;
be prepared for graduate school or employment and have an appreciation for life-long learning;
have an appreciation for the professional, societal and ethical considerations of data collection and use.