Scientific Computing and Data Science: A Comparison

Scientific Computing broadly describes the application of computer simulations, usually based on a combination of mathematical models and numerical methods, to solve problems in science and engineering. By contrast, Data Science refers to the statistical analysis and interpretation of data resulting from experimental measurements or simulations. Continued increases in available computational power have made feasible progressively larger-scale scientific simulations that produce ever-increasing quantities of data. In some cases, the processing and interpretation of this data has become a bottleneck limiting the usefulness of the simulations. Concurrently, the increasing ability to record and store data streams in many settings, e.g., weather monitoring stations, retail environments, online website traffic, etc., has pushed to the forefront the need for efficient data science tools.  These developments have created new opportunities at the nexus of the traditionally separate arenas of scientific computing and data science.

Two new Masters level programs, the Scientific Computing Master’s Program (SCMP) and the Data Science Master’s Program (DATS) have been developed within the School of Engineering and Applied Science to address this evolving landscape and to provide training for students interested in a broad range of careers. These two program share a common core consisting of courses in mathematical foundations, computer programming, machine learning, and data analytics. However, they also are distinct in important ways: the SCMP program emphasizes application of modern computer simulation methods in fields related to natural science and engineering, while DATS focuses on statistical methods in a broad range of application domains. The degree requirements for both programs are summarized below.

Typically (but not exclusively), SCMP candidates have a background in a natural science or engineering discipline, interest or prior experience with scientific computing, and seek to learn how to deploy and use data science and machine learning techniques

In both cases, some prior programming experience is useful but not absolutely required. Students who have a weak programming background but are interested in SCMP or DATS, and who wish to have a strong technical background upon graduation, are encouraged apply to the Master’s of Computer and Information Technology (MCIT) program and apply for a dual-degree with SCMP/DATS after the first semester. The MCIT program is designed for students with no prior experience in computer science. Students in this program learn programming, discrete math, data structures and algorithms, computer architecture, and software engineering, along with a number of other electives in computer science and engineering.  Graduates of this program are well positioned for a variety of software engineering and project management jobs in the tech industry.

Prospective students are encouraged to decide which of these two programs, SCMP or DATS, is the best match for their interests and apply to at most one.

DATS and SCMP Program of Study: An overview