Bachelor of Science in Data Science

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.

Faculty and Fields of Interest

Amir Akhavan Masoumi, BS 2004 Oroumieh Azad University, MS 2009 Phd 2015 University Of Science Malaysia, (CIS) Information Security, Complex Systems Science, Data Visualization, Cognitive Load Analysis

Ramprasad Balasubramanian, MS 1991 University of Toledo, MS 1993 University of Kentucky, PhD 2000 University of South Florida, Computer Vision, Robotics, Artificial Intelligence

Yuchou Chang, BS 2003 Northwestern Polytechnical University, MS 2006 Shanghai Jiao Tong University, PhD 2012 University of Wisconsin-Milwaukee, Postdoctoral Fellow 2015 Barrow Neurological Institute, Computational Imaging, Artificial Intelligence, Brain-Computer Interface

Yanlai Chen, BS 2002 University of Science and Technology of China, MS 2007, PhD 2007 University of Minnesota Twin Cities, 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

Zheng Chen, PhD 2014 Brown University, Numerical Analysis, Scientific Computing, High-order Numerical Methods, Fractional Partial Differential Equation, Post-processing Techniques, Uncertainty Quantification

Gary Davis, BSc 1968, PhD 1971 Monash University, Memory Systems, Des, Mathematics Education, Data Science Education

Bo Dong, BS 2002 University of Science and Technology of China, PhD 2007 University of Minnesota, Numerical Analysis, Scientific Computing, Discontinuous Galerkin Finite Element Methods, Data Science

Hua Fang, PhD 2006 Ohio University, Computational Statistics, Machine Learning, Pattern Recognition, Behavioral Trajectory Patterns, Wireless Health

Scott Field, PhD 2011 Brown University, Bayesian Inference Problems, Gravitational Wave Data Science, Scientific and High Performance Computing

Sigal Gottlieb, ScB 1993, ScM 1995, PhD 1998 Brown University, 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, Software for Mathematics Education, Computer Graphics, Scientific Computation, Noncommutative Algebra, Data Science

Alfa Heryudono, BS 2000 University of Indonesia, MS 2002 Southern Illinois University, PhD 2008 University of Delaware, Radial Basis Functions, Spectral and Pseudospectral Methods, Numerical Conformal Mapping, Tear Film Dynamics, Mathematical Problems in Industry, Numerical Pdes, Data Science

Firas Khatib, BA 2001 University of California Berkeley, PhD 2008 University of California Santa Cruz, Bioinformatics, Crowd-sourcing

Saeja Kim, BS 1975 Seoul National University, MS 1985 Brown University, PhD 1988 University of Illinois at Urbana-Champaign, Computational Algebra, Edge Detection, Applied Mathematics, Topological Data Analysis, Mathematics Education, Data Science Education

Ashokkumar Patel (Co-director), MS 1989 Gujarat University, MS 2013 Georgia Southwestern State University, PhD 2002 North Gujarat University, Cybersecurity, Machine Learning, Big Data Analytics, Biomatrices-based Trustworthy Secure Cyberspace

Ming Shao, PhD 2016 Northeastern University, Transfer Learning/Domain Adaptation, Deep Learning, Large-scale Graph Approximation/Clustering, Social Media Analytics

Iren Valova, BS 1991, MS 1993 Technical University, Sofia, Bulgaria, PhD 1997 Tokyo Institute of Technology, Artificial Intelligence, Neural Networks, Image Processing

Vijay Varma, PhD 2019 Caltech, Gravitational Waves, Data-Driven Modeling, Numerical Relativity

Cheng Wang, BS Temple University, PhD University of Science and Technology China Numerical Analysis, Numerical Partial Differential Equations, Data Science

Haiping Xu, BS 1989, MS 1992 Zhejiang University, MS 1998 Wright State University, PhD 2003 University of Illinois Chicago, Software Engineering, Distributed Computing, Deep Learning, Cloud Computing, Cybersecurity

Donghui Yan (Co-director), BS and MS 1994 Shanghai Jiao Tong University, PhD 2008 University of California Berkeley, Statistics, Machine Learning, Data Science

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.