The Data Science and Engineering programs offered by the University of Maine are intended to meet the growing demand for graduates with core skills in managing and analyzing complex data and analytics challenges. The graduate programs provide a pathway for students from diverse fields to transition to multiple data science and engineering career paths by providing them with core graduate level courses across the entire spectrum of the data lifecycle. In support of the interdisciplinary spirit of data science and engineering, the program is designed to accommodate students from a wide range of undergraduate degrees or other graduate degree backgrounds with options for specialization in different domains. A collection of courses with a variety of in-class and online options support students in residence as well as meet the needs of people currently in the workforce or who are otherwise place-bound and need training or retraining in the area of Data Science and Engineering.
Program Goals and Learning Objectives/Outcomes
Graduates of the master’s program will achieve the following learning objectives and outcomes:
- an appreciation of data sources, the data acquisition process, data types, data quality, and methods for cleaning.
- an understanding of issues impacting the efficient processing, representing, storing, managing, and retrieval of large amounts of data.
- an understanding of how to leverage modern computational infrastructures and software tools to perform large-scale data analysis and machine learning.
- an understanding of common analytical tools, their methods, their effective use, and the strengths and limitations of each.
- the skills to effectively explore and present data to different audiences through visual and multimodal methods.
- a familiarity with data security, curation, and preservation strategies
- the ability to form questions for analysis from an understanding of the characteristics and goals of different application domains
- an understanding of artificial intelligence and its applications
- an awareness of the ethical issues, risks, and responsibilities related to data science.
Students have the options to complete a 30-credit MS degree (thesis or coursework), a 15-credit graduate certificate, or both.
The M.S. degree in Data Science and Engineering trains students in the management, analysis, and visualization of large and complex data sets with either or both in-class and on-line options. The graduate program may be completed entirely on the campus in Orono, entirely online, or through a combination of courses taken online and on-campus at the Orono and other UMS campuses. Ultimately, as a general rule, students participating in courses online view class videos and accomplish assignments at any time throughout the week. They have the weekly opportunity to participate in a one to two-hour “live” discussion session with the professor at a mutually convenient time for distance class members prior to due dates for weekly assignments. Many of the graduate courses are already offered under this dual method of offering the course live for on-campus students with students at a distance viewing the class sessions at times that meet their schedules. Initially, some thematic core and domain specialization courses will be offered only on-campus with the expectation that over time, a majority of courses offered from UMaine will move to either hybrid dual or solely online versions. Regardless, it will be possible to earn the degree immediately online even though the selection of thematic core and domain specialization courses will be limited initially.
The program includes a set of core courses grouped in themes and a set of domain specialization courses. Students may focus solely on the Data Science and Engineering core or tailor the degree to emphasize one or more domain specializations. To complement both thematic core and domain specializations, some courses may be taken in-class or by distance from other Maine universities if pre-approved for inclusion in graduate student Programs of Study assuming that other program requirements are met.
The University of Maine offers the following graduate programs in Data Science and Engineering:
- MS Data Science and Engineering (MS DSE) with a Thesis Option (24 credits of coursework and six thesis credits)
- MS Data Science and Engineering (MS DSE) with a Coursework Option (30 credits of coursework requiring three of the credits to be a project or internship course)
- Graduate Certificate in Data Science and Engineering (15 credits of coursework)
- Four Plus One Option For this option, any qualifying undergraduate student in any degree program at the University of Maine may begin this option in their junior year enabling them to complete their bachelor’s degree and the MS DSE in five years. This option is open to other UMS campuses on a case-by-case basis.
Data science and engineering has become a critical skill field for the 21st century. Data science and engineering addresses the challenges of capturing, curating, managing, processing, analyzing, and translating massive, complex, heterogeneous, and dynamic data into manageable forms, new information, and insights. A host of new technologies (advanced computer modeling, smart sensor networks, high-precision lab instruments, wireless telecommunications, smart devices, and social media) are generating data collections at unprecedented rates. There are numerous new applications for such data in engineering, environmental, and social sciences as well as in business, industry, and government. The pervasive application of artificial intelligence (AI) techniques in continuous mining of big data across diverse domains is now viewed as essential by businesses and government in improving decision-making and acquiring insights that were not previously possible. For businesses, governments and academic institutions throughout Maine and beyond there is a growing need for a workforce well trained in exactly such skills.
Data science and engineering is intrinsically transdisciplinary. In this emerging and rapidly evolving field, precise definitions and boundaries do not yet exist. The terms “data science” and “data engineering” are used in overlapping ways, with “data science” or “data science and engineering” usually used to indicate the field in a broad sense. Representative descriptions of data science include:
- “novel mix of mathematical and statistical modeling, computational thinking and methods, data representation and management, and domain expertise” (Computing Research Association, 2016).
- “draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on the specifics of the domain to which it is applied ” (National Academies, 2018).
We have called this proposed program “data science and engineering” as both a clear indication of the disciplinary breadth and an acknowledgment of its roots in the UMaine Emerging Area in Data Science and Engineering. For brevity, we sometimes call the topic simply “data science.”
Data science and engineering relies on a novel mix of mathematical and statistical modeling, computational thinking and methods, data representation and management, effective information presentation, and consideration for responsible use of data in the context of various fields of domain expertise. Data science and engineering requires a deep understanding of how data are acquired and an understanding of the semantics of the data, which strongly influences how data are acquired, stored, accessed, analyzed, and presented. Data lineage, data quality, quality assurance, data integration, storage, privacy, security, and scalable systems and data architecture for big data are all critical topics in a robust data science program. Longer-term management and reuse of data is also becoming critical, so longer-term curation and data preservation must also be addressed.
The University of Maine has a solid foundation of strengths and resources to support Data Science and Engineering graduate programs. The programs draw upon faculty and courses from throughout the University and other UMS campuses. The list of faculty and their bios indicate the breadth of this collaboration at UMaine and beyond.