The Data Science and Engineering programs offered at 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.
Graduate programs offered include the Master of Science in Data Science and Engineering (thesis and coursework-only options) and the Graduate Certificate in Data Science and Engineering. For high-performing qualifying undergraduate students that may be pursuing any degree program, an Accelerated Four-Plus-One option exists allowing completion of an undergraduate degree and the MSDSE in five years.
Program Objectives
Graduates of the master’s program 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.
Master of Science in Data Science and Engineering
The University of Maine offers both thesis and course-work only options in the Master of Science in Data Science and Engineering. All work for a master’s degree must be completed within six years. The timing starts with the first semester of registration after admission to the Master of Science in Data Science and Engineering.
The thesis option is the scientific track, typically requiring a strong engineering, computer science, human-computer interaction, or mathematics undergraduate background. Prospective master’s students with other disciplinary backgrounds are expected to make up the requisite math and engineering courses that would allow them to succeed in the graduate curriculum. The thesis option includes a substantial piece of individual research as a basis for a master’s thesis.
The coursework-only option is aimed at students who desire to focus primarily on coursework rather than research at the master’s level. The formal coursework is complemented by an optiona internship or a one-semester project course in which the student must demonstrate that he or she can apply acquired knowledge for implementing a particular solution.
Degree Requirements
Applicants to the data science and engineering program should have at least one college level statistics course in their backgrounds. Admitted students have the opportunity to become familiar with various data science, data mining, data engineering, business analytics, machine learning, and artificial intelligence topics. Computer programming, statistics germane to data science, and systems knowledge may be picked up as part of the program if applicants don’t already have these foundations. Applicants with undergraduate degrees in computer science, engineering, math, and similar fields (i.e., those with two semesters of calculus and calculus-based statistics) have the opportunity to pursue higher level machine learning and artificial intelligence fundamentals and theory courses along with applications of advanced AI methods addressing real-world problems. The multiple paths to graduation and multi-disciplinary course opportunities make the program highly flexible in meeting individual student needs.
Master (Coursework-Only Option)
A candidate must complete 30 graduate course credits on-campus or online on a program of study approved by advisors that includes:
- A specified foundation course in each of statistics, programming, and systems unless waived based on previous coursework
- DSE 510 Practicum in Data Science and Engineering (3cr)
- 12 course credits drawn from at least four of the five Theme Areas
- It is recommended that at least one course includes a substantial practical experience. Options include DSE 589 Graduate Project, DSE 590 Data SScience and Engineering Internship, or a course from an approved list.
- Further course credits from within the Foundation Courses, Theme Areas, or Domain Specializations to bring the total to 30 credits
- No more than 6 course credits, if any, at the 400 level
Foundation Courses
Statistics Foundations
Programming Foundations
Systems Foundations
Theme Area Courses
Domain Specialization Courses
Master (Thesis Option)
A candidate must complete 30 graduate course credits on a program of study approved by advisors that includes:
- Specified foundation courses in each of statistics, programming, and systems unless waived based on previous coursework
- DSE 510 Practicum in Data Science and Engineering (3cr)
- SIE 501 Introduction to Graduate Research (1cr)
- SIE 502 Research Methods (1cr)
- INT 601 Responsible Conduct of Research (1cr)
- 12 course credits drawn from at least four of the five theme areas
- 6 credits of thesis
- Further course credits from within the foundation courses, theme areas, or domain specializations to bring the total to 30 credits
- No more than 6 course credits, if any, may be at the 400 level
For either master’s degree option, a maximum of six credit hours of graduate course work taken prior to enrollment in the master’s program, whether at this university or another, may be counted toward the master’s degree assuming that the course(s) did not count toward a completed undergraduate or graduate degree and if the student’s graduate advisory committee formally approves acceptance of the courses on the student’s Program of Study.
Admission Requirements
Admission to the MS Data Science and Engineering is competitive. In the admission process, the graduate faculty considers the potential of applicants to complete a program successfully and achieve a position of leadership in the private, public or research sectors.
Students with undergraduate degrees in any field may apply. The bachelor’s degree should be from an accredited four-year U.S. accredited college or university with a 3.0 cumulative or higher GPA, or equivalent international university degree with comparable academic performance (exceptions considered on case-by-case basis)
Applications are accepted on a rolling basis and no strict deadlines apply. Thesis-based MS students applying for campus-wide research assistantships or scholarships should take and submit the GRE and complete their application packets by January 1 for fall admission. We generally seek students that score at the mean or above on the verbal, quantitative and analytical segments of the GRE exam and in the 50th percentile or above on the exam overall. Exceptions are considered on a case-by-case basis.
Required information in the MSDSE online application should include transcripts from previous institutions, test scores (if required), current resume that includes contact information for three references, an essay, and the application fee. For detailed instructions, see Further Admission Information.
Accelerated Four Plus One Program: Early Admission for UMaine Undergraduate Students
Undergraduate students from any degree program at the University of Maine may apply as early as the summer before their junior year for admission to the MS Data Science and Engineering (Coursework-Only Option) graduate degree program. Applications for conditional “early admission” should be received preferably by the middle of the first semester of the junior year and are not accepted after the senior year has commenced. The final year in completing the Master’s degrees may be taken either on-campus or online.
By taking a course overload of three credits in the second semester of the Junior year and course overloads in each of the semesters of the Senior year, a motivated student typically may acquire 9 credits (but no more than 12) for graduate school (at undergraduate tuition rates) prior to acquiring their undergraduate degree assuming that they receive a B or better in the courses. These courses, if chosen appropriately, may double count toward both the undergraduate and graduate degree. By taking a 3-credit DSE 590Data Science and Engineering Intership course graduate course with a corporation, agency or non-profit organization during the summer, a student may readily complete the coursework master’s degree in a single year after their undergraduate degree. This master’s degree will be highly complementary to an undergraduate degree in almost any field and attractive to employers.
To apply for early admission before or during the junior year, an applicant should expect to have an overall minimum undergraduate grade point average of 3.25, must have completed the University of Maine General Education Requirement in Math and must have three letters of recommendation from current or previous university instructors. Apply using the Application for Admission to the DSE Four Plus One Program. Continuation in the graduate program is based primarily on performance in the graduate courses and overall grade point average upon graduation from the undergraduate program. Accepted Four Plus One students must complete the full graduate application in their senior year. The GRE exam is typically waived for these accepted high performing students. Below a 3.0 accumulated undergraduate grade point average should be assumed cause for discontinuation in the graduate program.
Students with two or fewer semesters remaining to complete their undergraduate degree program do not qualify for the accelerated “four-plus-one program” but their applications will be considered as applications within the regular graduate admissions process. In this case, one may transfer up to two graduate courses prior to formal admission assuming those courses did not count toward another degree.
Financial Assistance
In addition to University fellowships and scholarships listed elsewhere in this Catalog, the advising professor or other DSE graduate faculty may offer graduate research assistantships to qualified students on externally funded research projects. A very limited number of teaching assistantships may be available. Consult as well Funding at the Graduate School web site.
Data Science and Engineering Graduate Faculty
Ali Abedi, Professor, Electrical and Computer Engineering
Kate Beard-Tidale, Professor, Spatial Computing
Kathleen P. Bell, Professor, Economics
Sudarshan Chawathe, Associate Professor, Computer Science
Prabuddha Chakrovathy, Assistant Professor, Electrical and Compter Engineering
Phillip Dickens, Associate Professor, Computer Science
Matthew Dube, Assistant Professor, Computer Information Systems
Richard Eason, Associate Professor, Electrical and Computer Engineering
Keith Evans, Associate Professor, Economics
Sepidah Ghanavati, Assistant Professor, Computer Science
Nicholas Giudice, Professor, Spatial Computing
Ramesh C. Gupta, Professor, Mathematics and Statistics
Pushpa Gupta, Professor, Mathematics and Statistics
Torsten Hahmann, Associate Professor, Spatial Computing
Daniel Hayes, Associate Professor, Forest Resources
David Hiebeler, Professor, Mathematics and Statistics
Raymond Hintz, Professor, Surveying Engineering Technology
Don Hummels, Professor, Electrical and Computer Engineering
Jon Ippolito, Professor, New Media
Shaleen Jain, Professor, Civil and Environmental Engineering
Tora Johnson, Environmental and Biological Sciences, University of Maine at Machias
Nory Jones, Professor, Maine Business School
Andre Khalil, Professor, Chemical and Biological Engineering
Benjamin King, Assistant Professor, Bioinformatics
Anne Kelly Knowles, Professor, History
Cyndy Loftin, Associate Professor, Wildlife, Fisheries, and Conservation Biology
Yonggong (Tim) Lu, Associate Professor, Maine Business School
Jonathan Malacarne, Assistant Professor, Economics
Craig Mason, Professor, Education and Applied Quantitative Methods
Brian McGill, Professor, Biological Science
Silvia Nittel, Associate Professor, Spatial Computing
Nigle Pitt, Professor, Mathematics and Statistics
Parinaz Rahimzadeh-Bajgiran, Assistant Professor, Forest Resources
Nimesha Ranasinghe, Assistant Professor, Spatial Computing
Andrew Reeve, Professor, Earth and Climate Sciences
Penny Rheingans, Professor, Computer Science
Judith Rosenbaum, Associate Professor, Communication and Journalism
Bruce Segee, Professor, Electrical and Computer Engineering
Salimeh Yasaei Sekeh, Assistant Professor, Computer Science
Ali Shirazi, Assistant Professor, Civil and Environmental Engineering
Andrew Thomas, Professor, School of Marine Sciences
Roy Turner, Associate Professor, Computer Science
Vince Weaver, Associate Professor, Electrical and Computer Engineering
J. Michael Weber, Professor, Maine Business School
Zheng (David) Wei, Assistant Professor, Mathematics and Statistics
Aaron Weiskittel, Professor, School of Forest Resources
Thomas Wiesen, Assistant Professor, Economics
Manuel Woersdoerfer, Assistant Professor, Maine Business School
Terry S. Yoo, Associate Professor, Computer Science
Yifeng Zhu, Professor, Electrical and Computer Engineering
Graduate School
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