Data Science and Engineering
Relevant and in Demand
The private, public, and research sectors rely heavily on data science and engineering for analyzing and translating massive, complex, heterogeneous, and dynamic data into manageable forms, creating new information, and providing insights in order to better understand phenomena and guide decision-making. The advanced knowledge gained through graduate-level data science and engineering programs is needed across a wide range of commercial, non-profit, government, and academic settings.
Graduate programs are offered both on campus and in entirety by distance methods. Discover more about the graduate programs in Data Science and Engineering.
graduate coordinator
Silvia Nittel
Key Admissions Deadlines
- Rolling Admissions
- No GRE/GMAT Requirements
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Program Offerings
Vision
The Data Science and Engineering programs at the University of Maine address the growing demand for professionals skilled in managing and analyzing complex data. These graduate programs provide students from diverse academic backgrounds with core coursework spanning the entire data lifecycle. With in-class and online options, the program accommodates both residential students and working professionals seeking training or retraining in data science and engineering.
Program Goals and Learning Objectives/Outcomes
Graduates of the MS program will:
- Understand data acquisition, types, quality, and cleaning methods.
- Gain expertise in processing, storing, managing, and retrieving large datasets.
- Leverage modern computational tools for large-scale data analysis and machine learning.
- Use analytical tools effectively, recognizing their strengths and limitations.
- Present data through visual and multimodal methods for diverse audiences.
- Understand data security, curation, and preservation strategies.
- Formulate analytical questions informed by application domain goals.
- Apply artificial intelligence techniques.
- Navigate ethical considerations and responsibilities in data science.
Students may complete a 30-credit MS degree (thesis or coursework), a 15-credit graduate certificate, or both.
Overview
The MS in Data Science and Engineering trains students in data management, analysis, and visualization with flexible delivery options: entirely online, on-campus in Orono, or a hybrid format. Courses accommodate varied schedules, with live discussions and pre-recorded lectures available. Students can choose thematic core courses, domain specializations, or a combination of both.
Programs offered include:
- MS with Thesis: 24 credits of coursework + 6 thesis credits.
- MS Coursework Option: 30 credits, including a project or internship option.
- Graduate Certificate: 15 credits.
- Four Plus One Option: Complete a bachelor’s and MS degree in five years, available for UMaine undergraduates and select UMS campuses.
Program Rationale
Data science and engineering address the challenges of managing and analyzing massive, complex datasets, with applications across engineering, sciences, business, and government. Technologies like AI, smart sensors, and social media have created unprecedented data needs, requiring professionals skilled in analysis, visualization, and responsible data use.
The program’s transdisciplinary approach integrates mathematical and statistical modeling, computational methods, data representation, and domain expertise. Core topics include data acquisition, storage, quality assurance, security, scalable systems, and long-term data curation.
UMaine’s program draws on faculty expertise and resources from across its campuses, ensuring a robust and collaborative foundation for data science and engineering education.
MS Data Science and Engineering with Thesis Option
The candidate must complete 30 credits consisting of:
(a) Required Courses:
DSE 510 Practicum in Data Science and Engineering (3cr),
SIE 501 Introduction to Graduate Research (1cr),
SIE 502 Research Methods (1cr), and
INT 601 Responsible Conduct of Research (1cr)
(b) One, two, or all three of the Foundation Courses unless the material has been covered in previous coursework and the course(s) are explicitly waived by the current instructor and the graduate coordinator,
(c) 12 course credits from at least four of the five Theme Areas
(d) 6 credits of thesis
(e) further course credits from within the Foundation Courses, Theme Areas, or Domain Specializations to arrive at the total of 30 credits.
MS Data Science and Engineering with Coursework-Only Option
The candidate must complete 30 credits consisting of:
(a) Required Course: DSE 510 Practicum in Data Science and Engineering (3cr)
(b) One, two, or all three of the Foundation Courses unless the material has been covered in previous coursework and the course(s) are explicitly waived by the current instructor and the graduate coordinator,
(c) 12 course credits from at least four of the five Theme Areas
(d) It is recommended that at least one course includes a substantial practical experience. Options include DSE 589 Graduate Project, DSE 590 Information Systems Internship, or a course from an approved list.
(e) further course credits from within the Foundation Courses, Theme Areas, or Domain Specializations to arrive at the total of 30 credits
Data Science and Engineering Graduate Certificate Curriculum
The Graduate Certificate in Data Science and Engineering (GCDSE) consists of 15 credits. Students in consultation with their adviser should not select courses that are duplicative of courses that may have been taken in the student’s undergraduate degree program. If a previously taken course is duplicative of an elective course in one of the five core theme areas, simply select another course in that area or another theme core area so that the total is still 9 course credits in three of the five Core Theme Areas.
Although copied below, the official graduate certificate requirements may be found in the Graduate Catalog (select current year catalog and then Graduate Programs, Certificates and Specializations > Data Science and Engineering (Certificate)). The GCDSE is offered wholly on-campus as well as entirely online.
The candidate must complete 15 credits consisting of:
(a) Required Course: DSE 510 Practicum in Data Science and Engineering (3cr),
(b) 9 course credits from at least three of the five Theme Areas
(c) 3 further course credits from within the Foundation Courses, Theme Areas, or Domain Specializations
Data Science and Engineering Four Plus One Program
Conditional Acceptance while an Undergraduate Student
By planning ahead during your undergraduate program, you can finish in a single year the Master of Science in Data Science and Engineering. The final year in completing the Master’s degrees may be taken either on campus or online if the final courses desired are offered online.
Application to the Four Plus One Program: Undergraduate students from any degree program at the University of Maine may apply as early as the summer before their junior year for admission. 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.
Note: Students from other UMS campuses may propose participation by having their faculty contact us to cooperatively propose a Four Plus Memorandum of Understanding (MOU) to their campus Provost.
Application for Admission to the MS DSE Four Plus One Program (Word Document)
An applicant should expect to have an overall minimum undergraduate grade point average of 3.25, must have completed at least a semester course in calculus, and must have three letters of recommendation from current or previous university instructors. Provisionally admitted Four Plus One students with an undergraduate grade point average of 3.25 or better may take up to 9 credits of graduate-level courses in Data Science and Engineering toward the MS Data Science and Engineering with Coursework-Only Option. These graduate courses (9 credits) may also count towards the Bachelor’s degree (joint credits) but they must also be part of the student’s Master’s Program of Study in Data Science and Engineering.
Application to the Graduate School: Upon graduation with a bachelor’s degree, and with satisfactory performance in courses taken as an undergraduate, the student may be formally matriculated into the master’s program. Below a 3.0 accumulated undergraduate grade point average should be assumed cause for discontinuation in the program.
By taking a course overload of three credits in the second semester of the Junior year and a course overload 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 towards both the undergraduate and graduate degree. By taking a 3-credit Internship graduate course (e.g. SIE 590) 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. Graduate-level courses to double count must be at the 500-599 level. For Four Plus One students pursuing the MS DSE during and after the undergraduate degree, see the long list of qualifying graduate courses.
In the senior year, provisionally admitted students must submit a formal application to the Graduate School. Four Plus One students must enroll in the master’s program within one semester/term after receiving their bachelor’s degree in order to use the joint credits.
To save course credit tuition dollars for the student, the Application for Admission to the Four Plus One Program is submitted to the appropriate DSE Graduate Coordinator rather than to the graduate school. If a student is accepted and the performance criteria below are met, acceptance into the Four Plus One Program indicates a commitment by the DSE graduate faculty (1) to accept the student’s graduate courses completed while an undergraduate student within their graduate program of study and (2) to support the student’s application for formal admission to the graduate school after completion of the undergraduate degree.
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 MS DSE Four Plus One Program (Word Document)
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 submit the full application to graduate school in their senior year. The GRE exam is typically waived for these accepted high performing Four Plus One students.
Students with two or fewer semesters remaining to complete their undergraduate degree program do NOT qualify for the “four-plus-one program” but their applications will be considered as applications within the regular MSIS admissions process. In this case, one may transfer up to two graduate courses prior to formal admission.
Formal Application to Graduate School: The formal application for admission to the graduate program through the graduate school can occur anytime during or just after the senior year of the undergraduate program. Apply
Exceptions to Graduate School Rules: The Graduate School Rules pertaining to Four Plus One Programs may be found in Section 3.3. Exceptions to these rules may be applied for by completing the form for Request for an Exception to Regulation. Exceptions granted in the past have included: (a) allowing a student to count more graduate courses taken at the undergraduate tuition rate than the twelve normally allowed for a Four Plus One student prior to formal entry into graduate school, (b) allowing a student with a 3.25 GPA to double count 9 graduate credits in the undergrad program since their GPA in the major was well above 3.5, and (c) allowing a student who entered as a Four Plus One Student to convert to a thesis program (i.e. Four Plus Two Program) in order to accept a funded graduate assistantship.
Application for Admission to the MS DSE Four Plus One Program (Word Document)
Data Science and Engineering Graduate Course Groupings
For a desktop-friendly version of course grouping tables, click here.
Course Tables Overview
Foundation Courses
Required Courses
Theme Courses
Theme 1: Data Collection Technologies
Theme 2: Data Representation and Management
Theme 3: Data Analytics
Theme 4: Data Visualization and Human Centered Computing
Theme 5: Data Security, Preservation, and Reuse
Domain Specialization Courses
Domain A: Spatial Informatics
Domain B: Bioinformatics / Biomedicine
Domain C: Business Information
Domain D: Social and Behavioral Data Science
Domain E: Engineering Analytics
Artificial Intelligence (AI) Coursework Focus Areas
Course Tables Overview
Most courses listed in the tables below are offered on a yearly basis although some may be offered every other year. The semesters listed in the tables below indicate the likely semester(s) in which each course is next likely to be offered as indicated by the participating academic units. Similarly, the instructor listed is the likely instructor. The information in the tables is provided for planning programs of study. However, whether a course is offered in any particular year and by which instructor needs to be confirmed for that year and semester by consulting the official course schedules in MaineStreet through the UMaine Portal (or the publicly accessible UMaineOnline Course Search). Latest graduate course descriptions may be accessed in the menu to the left or through the UMaine graduate catalog.
Only three external courses from other University campuses may typically be included in a graduate student’s program of study. Maine Business School (MBS) graduate courses are viewed as internal courses regardless from which campus they originate.
Note: Courses designated with an asterisk (*) are typically available both online and in-person. Unless otherwise specified, courses are 3 credits.
Foundation Courses
Admitted candidates missing appropriate background prerequisite courses will take one foundation course in each of the three foundation areas of statistics, programming, and systems as appropriate and as advised by their graduate committee and/or advisor. Foundation courses may count towards the degree if approved on the student’s Graduate Program of Study. Currently approved courses within the three foundation areas include:
Statistics Foundations
- DSE 501: Statistical Foundations of Data Science and Engineering *
- Usual Semester: Fall, Prereq: college-level statistics
- STS 437: Statistical Methods in Research
- Usual Semester: Fall, Prereq: some statistics
- ECE 515: Random Variables and Stochastic Processes *
- Usual Semester: Fall, Prereq: graduate standing; ECE 316 or equivalent
- DSC 550:Data Mining (UMA)*
- Usual Semester: Fall, Prereq: college-level statistics
Programming Foundations
- DSE 502: Programming Foundations for Data Science and Engineering *
- Usual Semester: Fall, Prereq: program admission (online)
- SIE 507: Information System Programming *
- Usual Semester: Fall, Prereq: program admission (online)
- SIE 508: Object-Oriented Programming *
- Usual Semester: Spring, Prerequisite: SIE507 or programming experience (online)
- COS 522: Computing for Data Science (USM) *
- Usual Semester: Fall, Prereq: some Python programming (online)
- CIS 449: Introduction to Programming and Data Analysis (Programming with R) (UMA) *
- Usual Semester: Summer (online)
Systems Foundations
- DSE 503: Systems Foundations for Data Science and Engineering *
- Usual Semester: Summer, Prereq: DSE 501 or equivalent, or instructor permission
All students are expected to take all three foundation courses unless the courses would be repetitive of past coursework to the extent that waivers should be granted. For Foundation Course waiver requirements, see Advising Notes 3 and 2.
Required Courses
Whether in a graduate degree or graduate certificate program, all students must complete the following introductory course. This interdisciplinary team-taught course is structured around an overview of data science and engineering topics and tools as applied to large case study data sets.
- DSE 510: Practicum in Data Science and Engineering *
- Usual Semester: Spring, Prereq: program admission and SIE 507, or instructor permission
The following courses are all required for all students pursuing the MS Data Science and Engineering with Thesis Option.
- SIE 501: Introduction to Graduate Research (1 Credit) *
- Usual Semester: Spring, Prereq: program admission
- SIE 502: Research Methods (1 Credit) *
- Usual Semester: Fall, Prereq: SIE 501
- INT 601: Responsible Conduct of Research (1 Credit) *
- Usual Semester: Fall/Spring, Prereq: graduate standing
Theme Courses
Courses currently contained within the themes include the following:
Theme 1: Data Collection Technologies
- BUA 682: Data Pre-Processing for Business Analytics *
- Usual Semester: Spring, Prereq: Introduction to Statistics and some programming
- ECE 533: Advanced Robotics
- Usual Semester: Spring, Prereq: ECE 417 or instructor permission
- ECE 571: Advanced Microprocessor-based Design
- Usual Semester: Fall, Prereq: ECE 471 or instructor permission
- ECE 585: Fundamentals of Wireless Communications
- Usual Semester: Spring, Prereq: ECE 484
- SFR 609: Remote Sensing Problems
- Usual Semester: Fall/Spring, Prereq: instructor permission
- SIE 559: Geosensor Networks *
- Usual Semester: Fall, Prereq: Programming
- SMS 540: Satellite Oceanography
- Usual Semester: Fall, Prereq: SMS 501 and SMS 541 or instructor permission
- SVT 437: Practical GPS *
- Usual Semester: Fall, Prereq: SVT 341
- SVT 531: Advanced Digital Photogrammetry *
- Usual Semester: Spring, Prereq: SVT 331
- SVT 532: Survey Strategies in Use of Lidar *
- Usual Semester: Spring, Prereq: SVT 331
Theme 2: Data Representation and Management
- BUA 681: Data Management and Analytics *
- Usual Semester: Fall, Prereq: Introduction to Statistics and some programming
- COS 580: Topics in Database Management Systems
- Usual Semester: Fall, Prereq: instructor permission
- COS 541: Cloud Computing *
- Usual Semester: Spring, Prereq: COS 331 or equivalent
- ECE 574: Cluster Computing
- Usual Semester: Spring, Prereq: Instructor permission
- ECE 583: Coding and Information Theory *
- Usual Semester: Spring, Prereq: ECE 515 or instructor permission
- SIE 550: Design of Information Systems *
- Usual Semester: Fall, Prereq: program admission or instructor permission
- SIE 557: Database Systems Applications *
- Usual Semester: Spring, Prereq: SIE 507 or programming
- SIE 585 (SIE 580): Formal Ontologies: Principle and Practice *
- Usual Semester: Fall, Prereq: SIE 505 or instructor permission
Theme 3: Data Analytics
- BIO 593: Advanced Biometry
- Usual Semester: Fall, Prereq: course in statistics
- BMB 520: Introduction to Image Analysis
- Usual Semester: Fall, Prereq: program admission
- BUA 684: Business Data Mining and Knowledge Discovery *
- Usual Semester: Spring, Prereq: Introduction to Statistics and some programming
- CMJ 601: Seminar in Research Methods
- Usual Semester: Fall, Prereq: instructor permission
- COS 470/COS 570: Introduction to Artificial Intelligence *
- Usual Semester: Spring, Prereq: instructor permission
- COS 475/COS 575 (COS 598): Machine Learning *
- Usual Semester: Spring, Prereq: MAT 126, MAT 127, STS 232 (or STS 332, 434, 435)
- COS 5xx (COS 598): Interpretability and Explainability in Machine Learning (tentative – proposed for inclusion)
- Usual Semester: ?, Prereq: COS 475/575
- COS 473/COS 573: Computer Vision *
- Usual Semester: Fall, Prereq: COS 226 or instructor permission
- COS 573: Deep Learning (USM) *
- Usual Semester: Spring, Prerequisites: instructor permission
- COS 575: Machine Learning (USM) *
- Usual Semester: Fall, Prerequisites: instructor permission
- ECE 577: Fuzzy Logic
- Usual Semester: Spring, Prereq: program admission or instructor permission
- ECE 584: Estimation Theory *
- Usual Semester: Summer, Prereq: ECE 515 or instructor permission
- ECE 590: Neural Networks
- Usual Semester: Fall, Prereq: instructor permission
- ECE 598 (ECE 591): Deep Learning
- Usual Semester: Fall, Prereq: program admission or instructor permission
- ECO 530: Econometrics
- Usual Semester: Fall, Prereq: MAT 126 and MAT 215/MAT 232 or instructor permission
- ECO 531: Advanced Econometrics and Applications
- Usual Semester: Spring, Prereq: B or better in ECO 530, or instructor permission
- ECO 532: Advanced Time Series Econometrics
- Usual Semester: Spring, Prereq: ECO 530 or instructor permission
- EHD 572: Advanced Qualitative Research
- Usual Semester: Spring, Prereq: EHD 571 or equivalent
- EHD 573: Statistical Methods in Education I *
- Usual Semester: Fall/Spring, Prereq: none listed
- EHD 574: Statistical Methods in Education II *
- Usual Semester: Spring, Prereq: EHD 573 or equivalent
- PSE 509: Experimental Design (4 credits)
- Usual Semester: Spring, Prereq: none listed
- PSY 540: Advanced Psychological Statistical Methods and Analysis I
- Usual Semester: Fall, Prereq: PSY 241 or equivalent
- PSY 541: Advanced Psychological Statistical Methods and Analysis II
- Usual Semester: Spring, Prereq: PSY 241 or equivalent
- SFR 528: Qualitative Data Analysis in Natural Resources
- Usual Semester: Fall, Prereq: EDH 571 or instructor permission
- SMS 595: Data Analysis Methods in Marine Science
- Usual Semester: Spring, Prereq: MAT 126 or equivalent
- STS 531: Mathematical Statistics
- Usual Semester: Fall, Prereq: C or better in MAT 425 or STS 434, or instructor permission
- STS 533: Stochastic Systems
- Usual Semester: Spring, Prereq: C or better in STS 434
Theme 4: Data Visualization and Human Centered Computing
- BUA 683: Information Visualization *
- Usual Semester: Spring, Prereq: Introduction to Statistics and some programming
- COS 565: Data Visualization *
- Usual Semester: Spring, Prereq: COS 226, SIE 508, or instructor permission
- SIE 515: Human Computer Interaction *
- Usual Semester: Fall, Prereq: program admission or instructor permission
- SIE 516: Interactive Technologies for Solving Real-World Problems *
- Usual Semester: Fall, Prereq: program admission or instructor permission
- SIE 517 (SIE 598): Spatial Interaction Design *
- Usual Semester: Spring, Prereq: program admission or instructor permission
Theme 5: Data Security, Preservation, and Reuse
- COS 435/COS 535 (COS 598): Information Privacy Engineering *
- Usual Semester: Fall, Prereq: college-level knowledge of IT or software development
- DIG 500: Introduction to Digital Curation *
- Usual Semester: Fall, Prereq: none listed
- DIG 510: Metadata Systems *
- Usual Semester: Summer, Prereq: DIG 500 recommended
- DIG 550: Digital Preservation *
- Usual Semester: Spring, Prereq: DIG 500, DIG 510, and DIG 540 recommended
- SIE 525: Information Systems Law *
- Usual Semester: Spring, Prereq: program admission or instructor permission
- CYB 501: Cybersecurity Fundamentals *
- Usual Semester: Fall/Spring, Prereq: graduate standing
- CYB 520: Cybersecurity Policy and Risk Management *
- Usual Semester: Spring, Prereq: graduate standing
- CYB 551: Cybersecurity Investigations *
- Usual Semester: Spring, Prereq: graduate standing
Domain Specialization Courses
A single course may not count under more than one domain specialization or theme category. Courses currently contained within the domain specializations include the following:
Domain A: Spatial Informatics
- SIE 505: Formal Foundations for Information Science *
- Usual Semester: Spring, Prereq: SIE 550 or instructor permission
- SIE 509: Principles of Geographic Information Systems *
- Usual Semester: Fall, Prereq: program admission or instructor permission
- SIE 510: GIS Applications *
- Usual Semester: Spring, Prereq: SIE 509 or instructor permission
- SIE 512: Spatial Analysis *
- Usual Semester: Fall, Prereq: Introduction to Statistics or instructor permission
- SIE 555: Spatial Database Systems *
- Usual Semester: Fall, Prereq: SIE 550 and programming
- SIE 558: Real-time Sensor Data Streams *
- Usual Semester: Fall, Prereq: programming or instructor permission
- INT 527: Integration of GIS and Remote Sensing Analysis in Natural Resource Applications *
- Usual Semester: Spring, Prereq: permission and graduate standing
- CIS 461/DSC 461: Spatial-Temporal Information Science *
- Usual Semester: Spring, Prereq: CIS 360 or permission
- GEO 605: Remote Sensing *
- Usual Semester: Spring, Prereq: graduate standing
- ANT 521: Geographic Information Systems I *
- Usual Semester: Fall/Spring, Prereq: instructor permission
- ANT 522: Geographic Information Systems II *
- Usual Semester: Fall/Spring, Prereq: ANT 521 or instructor permission
- GIS 420: Remote Sensing and Image Analysis *
- Usual Semester: Fall, Prereq: ANT 522 or instructor permission
- GIS 426: Community Applications of GIS *
- Usual Semester: Fall, Prereq: ANT 522 or instructor permission
- GIS 428: Web-Based Maps, Applications and Services *
- Usual Semester: Spring, Prereq: ANT 521 and ANT 522 or instructor permission
Domain B: Bioinformatics / Biomedicine
- BMB 502: Introduction to Bioinformatics *
- Usual Semester: Spring, Prereq: BMB 280 or instructor permission
- BMB 520: Introduction to Image Analysis
- Usual Semester: Fall, Prereq: program admission
- BMS 625: Foundations of Biomedical Science and Engineering (1 credit)
- Usual Semester: Fall, Prereq: none listed
- ECE 583: Coding and Information Theory *
- Usual Semester: Spring, Prereq: ECE 515 or instructor permission
- SIE 505: Formal Foundations for Information Science *
- Usual Semester: Spring, Prereq: SIE 550 or instructor permission
Domain C: Business Information
- BUA 680: Foundations of Business Intelligene and Analytics *
- Usual Semester: Fall/Spring, Prereq: Introduction to Statistics
- BUA 684: Business Data Mining and Knowledge Discovery *
- Usual Semester: Spring, Prereq: Introduction to Statistics and some programming
- BUA 685: Problem Solving and Decision Analysis *
- Usual Semester: Fall, Prereq: Introduction to Statistics, economic principles, and some programming
- BUA 686: Predictive Analytics and Business Forecasting *
- Usual Semester: Summer, Prereq: Introduction to Statistics and some programming
- CIS 450/BUA 450/DSC 450: Data Mining *
- Usual Semester: Fall, Prereq: CIS 255/352/360/449 or instructor permission
Domain D: Social and Behavioral Data Science
- HTY 665: Digital and Spatial History *
- Usual Semester: Spring, Prereq: graduate standing
- CMJ 593: Special Topics in Communication: Social Media and Digital Cultures
- Usual Semester: Fall, Prereq: instructor permission
Domain E: Engineering Analytics
- CIE 598: Civil Engineering Systems and Optimization
- Usual Semester: Fall, Prereq: MAT 126, MAT 127, or instructor permission
- ECE 515: Random Variables and Stochastic Processes *
- Usual Semester: Fall, Prereq: graduate standing and ECE 316 or equivalent
- ECE 573: Microprogramming
- Usual Semester: Fall, Prereq: ECE 471 or ECE 475
- ECE 523: Mathematical Methods in Electrical Engineering
- Usual Semester: Fall, Prereq: senior or graduate standing in ECE
- ECE 533: Advanced Robotics
- Usual Semester: Spring, Prereq: ECE 417 or instructor permission
- ECE 571: Advanced Microprocessor-based Design
- Usual Semester: Fall, Prereq: ECE 471 or instructor permission
- ECE 574: Cluster Computing
- Usual Semester: Spring, Prereq: Instructor permission
- ECE 577: Fuzzy Logic
- Usual Semester: Spring, Prereq: ECE 477 or instructor permission
- ECE 583: Coding and Information Theory *
- Usual Semester: Spring, Prereq: ECE 515 or instructor permission
- ECE 584: Estimation Theory *
- Usual Semester: Summer, Prereq: ECE 515 or instructor permission
- ECE 585: Fundamentals of Wireless Communication *
- Usual Semester: Spring, Prereq: ECE 484
- ECE 590: Neural Networks
- Usual Semester: Fall, Prereq: instructor permission
- ECE 598 (ECE 591): Deep Learning
- Usual Semester: Fall, Prereq: program admission or instructor permission
Note: Courses designated with an asterisk (*) are typically available both online and in-person. Unless otherwise specified, courses are 3 credits.
Artificial Intelligence (AI) Coursework Focus Areas
The DSE program does not have officially designated concentrations, specializations or graduate certificates in Artificial Intelligence currently. However, several in depth courses in AI may be pursued as part of and count towards the MS Data Science and Engineering degree. The primary factor controlling whether a student may take any or several of these courses is dependent upon the student’s previous coursework background. Students with undergraduate degrees in computer science, engineering, and math are more likely to have fewer or no prerequisites to make up in order to take the following courses.
AI Applications in Business
Prerequisites: A recommended overall prerequisite for all of the BUA courses is BUA 601 Data Analysis for Business as well as the DSE Foundation Courses or equivalents.
Although not explicitly focused on AI concepts or applications, any of the previously listed business analytics courses in the DSE program (i.e., BUA designators) may provide the analytic foundations for better understanding current and future uses of AI in the business environment. In particular, BUA 680 covers general philosophical principles underlying all business analytics activities and BUA 685 covers empirical causal modeling with Bayesian belief networks for business applications, which is one of the most promising areas in applied AI in the rapidly emerging big data era.
Artificial Intelligence (AI) Courses Offered by the School of Computing and Information Science
Prerequisites: It is recommended that a student pursuing these courses should have, at a minimum, prerequisite coursework in object-oriented design, programming, and data structures (e.g., COS 225 and 226 or their equivalents), two semesters of calculus, statistics (at least at the level of STS 232 but preferably at the level of STS 332, 434, or 435), and experience in software development.
- COS 470/570 Topics in Artificial Intelligence
- COS 475/575 Machine Learning
- COS 5xx/4xx Interpretability and Explainability in Machine Learning (tentative addition to DSE list – currently COS 598)
- COS 535/435 Engineering Privacy in Software Systems (currently COS 598)
- COS 5xx/4xx Introduction to Private AI: Privacy in Machine Learning (tentative addition to DSE list – currently COS 598)
- COS 573/473 Computer Vision
- SIE 554 Spatial Reasoning (tentative addition to DSE list)
- SIE 585 Ontology Engineering (tentative addition to DSE list)
Artificial Intelligence (AI) Courses Offered by the Department of Electrical and Computer Engineering
Prerequisites: It is recommended that a student pursuing these courses should have, at a minimum, prerequisite coursework that includes three semesters of calculus, a calculus-based statistics course, and engineering level programming skills.
- ECE 491/591 Deep Learning
- ECE 490/590 Artificial Neural Networks
- ECE 533 Advanced Robotics (Prerequisite: ECE 417 Introduction to Robotics or equivalent)
- ECE 577 Fuzzy Logic (Prerequisite: ECE 477 Hardware Applications Using C or equivalent)
Admissions
Admission to the MS Data Science and Engineering and the Graduate Certificate in 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. However, those with two semesters of calculus (e.g., MAT 126, 127), a semester of statistics (e.g., STS 232 or ECE 316 or CHB 350), and proficiency in programming will have more options for classes they may pursue. Students without these background prerequisites will be required to take foundation courses that count toward the degree. All students must have at least a college level statistics course as a prerequisite to taking the foundations course in statistics. If missing that course, an approved college statistics course may be taken prior to or made part of conditional or provisional admission.
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 complete their application packets by January 1 for fall admission.
MS in Data Science and Engineering
Admission Criteria
- Bachelor’s degree 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)
Application Requirements
- Online application
- Transcripts from previous institutions
- Current resume to include three references
- Essay
- $65 application fee
For detailed application instructions, see Further Admission Information.
Graduate Certificate in Data Science and Engineering
Admission Criteria
- Bachelor’s degree 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)
Application Requirements
- Online application
- Transcripts from previous institutions
- Current resume
- Essay
- $35 application
For detailed application instructions, see Further Admission Information.
Accelerated 4+1 Program in Data Science and Engineering
UMaine undergraduate students interested in the Four Plus One option should first apply directly to the graduate coordinator in DSE. The Graduate School application must be completed after being accepted into the 4+1 program.
Further Admission Information
Application Content Instructions – One line on the application form states: Please list any member(s) of the University of Maine faculty whom you have identified as a potential faculty mentor. In response to this request you may indicate a preferred major adviser from the list of over fifty DSE graduate faculty members or request that the adviser be drawn from a specific campus department or school. If no preference is stated, we attempt to make a best-match for you. Depending on circumstances, not all preferences may be able to be met.
Application Resume Instructions – A single page is sufficient but submit no more than two. Include academic, employment, and any other germane accomplishments. On your resume please provide contact information for three professional or educator recommendations. Recommenders should be able to comment on your academic strengths, work ethic, responsibilities, accomplishments, career advancement, or ability to be successful. We may or may not solicit recommendations by phone, email or letter as appropriate.
Application Essay Prompt – Discuss motivations, life experiences, and ability to succeed. The essay is evaluated not only for content, but also for intrinsic writing quality and strengths.
Application Fee Waivers – Application fees are waived for:
- Graduates of University of Maine System Schools
- Veterans of the US Armed Forces
- Fulbright Scholars
- Participants in select programs (eg. IRT/McNair)
Transfer Courses – For the MS in DSE, a maximum of six credit hours of graduate coursework taken prior to enrollment in the master’s program may be counted toward the master’s degree. Students will confer with their graduate advisory committee upon admission to determine the transferability of previous coursework. The transfer documentation is best provided on the student’s Program of Study (Master’s POS Form) which is filed with the appropriate signatures. In the event you ever need to change a course on the POS, use the Change in Program of Study form found at the same link
Course Waivers – If some required courses are duplicative of courses that may have been taken in the student’s undergraduate degree program, those courses need not be repeated, and the student will select in consultation with the student’s faculty advisor and the DSE Graduate Coordinator additional approved courses to arrive at the total of 30 credit hours. A course waiver form must be submitted. If waived for a course in a Core Theme area, up to three credits are satisfied in that area and an additional approved course outside of the Core Theme areas may therefore be chosen.
Courses Pursued on Other UMS Campus – Typically up to two courses taken on other UMS campuses as listed under the DSE Graduate Course Groupings may be accepted on a student Programs of Study. For each external course you take you must file a Domestic Study Away Form. Submit the form at the same time you enroll in the course.
On-Leave Status – If you decide to enroll in no courses in either program for one or more regular academic year semesters, your are required to submit a Request for On-Leave Status. If you fail to submit such a request, you are assumed to have left the program by the Graduate School.
Tuition – For more information about tuition and fees please visit the Bursar’s Office webpage. Alternatively, applicants interested in the online program option may consult the Tuition and Fees through UMaineOnline.
International Students – International applicants should review the Office of International Programs page for more information on Visas, financial statements, and other requirements, or should contact an international advisor. Among further documentation that may be required includes:
- TOEFL or IELTS scores (TOEFL minimum of 80 or IELTS 6.5.) – Waived for native English speakers and students graduating from an English-speaking or ESL programs.
- WES or ECE certified transcript translation.
- Certificate of Finances if attending on campus
- Copy of passport or visa if attending on campus
STEM Status – International students applying to the MS DSE on campus should note that the MS DSE program is certified as a STEM program (see U.S. Immigration and Customs Enforcement Student and Exchange Visitor Program and CIP Code 14.3801) potentially allowing a longer postgraduate training stay in the U.S.
Graduate Certificate in Addition to MS DSE – Students currently enrolled in a master’s degree program through the Graduate School who desire to pursue an approved graduate certificate program simultaneously in a subarea or specialization must apply for admission to the certificate program before one-half of the required master’s credits are completed. While the graduate certificate might be in DSE, it might be in another program as well (e.g. Graduate Certificates in Information Systems. GIS, Computing for Educators, Digital Curation, etc.). See the full list of graduate certificates offered through UMaine Online.
Financial Aid
For general information on the range of grants, loans and scholarships available from Federal and other sources for graduate students, contact the Office of Student Financial Aid. The department provides no assistantships for non-research based degrees, however, university wide assistantships and scholarships may be available. All full-time students are eligible to apply. Note: Those seeking a graduate certificate are ineligible for most grants, loans and scholarships.
Some employers offer tuition reimbursement programs, please confer with your HR representative to confirm such a program exists and your eligibility.
For prospective DSE students:
- You can apply for the program here.
- The DSE program has rolling enrollments. International students are usually accepted up to two month before the start of the first semester. U.S. based students are accepted up to 2 weeks before the start of the semester. We recommend to start in the Fall semester since the course sequence builds with the Fall semester and the foundation courses are currently offered once per school year.
- A common question of prospective students is about available funding and graduate assistantships.
- Financial support is highly competitive and prioritized for students in thesis programs. Limited partial scholarships are available for highly qualified students (First Class honors or Second Class honors- upper division) in nonthesis master’s degree programs.
- It is recommended to find a thesis advisor and a potential graduate assistantship for thesis-MS before applying for the DSE program.
- The Graduate School has this page on their website which, at the bottom, has a list of positions that were sent by various departments.
- Additionally, the university has a CareerLink resource which allows you to browse various job listings, including assistantships.
- We do not offer application fee waivers.
For first semester DSE students:
- Please use your Maine.edu email address since all official contact will be done via this email address.
- New, first semester DSE student need to contact their academic advisor and discuss the selection of their first semester courses. Once the courses are selected, the student will activated and enrolled in the courses via the Graduate School.
- Please fill out and submit this form.
For DSE students:
Once you are passed the initial enrollment the following applies to you for the following semesters.
- After the initial enrollment, you can self-service enroll in courses using Mainestreet.
- Please check with the Grad School for more information here.
- You can enroll in and drop courses.
- Note, that each semester, courses usually can be added until the end of the first semester week, and dropped for a full refund until about 3 weeks after the beginning of the semester.
- Course enrollment for the following semester starts
- around end the end October for the Spring semester and
- April for the Summer and Fall semester.
- Note, that courses fill up quickly and register early.
- International students need to enroll in at least one in-person course for visa purposes.
- Before contacting your academic advisor to determine the courses for the following semester, please fill out or update your program of study spreadsheet (below) and send it along to your advisor when requesting a meeting.
- To track your progress in your program please use one of the following program of study sheets:
- Thesis-MS in DSE: MS DSE (thesis) Curriculum Sheet
- Course-work MS in DSE: MS DSE Curriculum Sheet
- Graduate Certificate in DSE: GC DSE Curriculum Sheet
- If you plan to do a DSE589 Graduate Project course or a DSE590 Internship course in the following, please contact the DSE Director Dr. Nittel ahead of time. Approval for both courses is necessary.
- DSE 590 – Data Science and Engineering Internship
Utilization of knowledge gained from the information systems graduate program within a business, non-profit or government organization and acquisition of practical training.
Prerequisites & Notes: Successful completion of nine credits of required courses in a school graduate program. Student needs to acquire permission to enroll in Internship course by filling out this form and submitting it to the Graduate Coordinator in the semester prior to the internship. (DSE590 Internship Form) - Taking courses at other UM campuses:
- You will register as a non-degree student at the other campuses to take a course. Please check the contact information below.
- If you have financial support (such as a graduate assistantship), please fill out and submit a Domestic Study away form. (form)
- If you do not have financial support via UM, please contact Emily Kuhlmann (emily.kuhlmann@maine.edu) at the end of the semester to transfer the credit to UMaine. She will ask you to fill out a program of study, including the transferred credit.
- Program of Study for MS: POS for MC
- Program of Study for Graduate Certificate: POS for certificates
- For courses at USM, please contact:
- Bruce MacLeod <macleod@maine.edu>, RegisterUSM <registerusm@maine.edu>
- For courses at UMA, please contact:
- CIS449: Matt Dube (matthew.dube@maine.edu), UMA enrollment services (umaenrl@maine.edu)
- Cybersecurity courses: Henry Felch (henry.felch@maine.edu), UMA enrollment services (umaenrl@maine.edu)
- DSC525: Rocko Graziano (rocko.graziano@maine.edu), UMA enrollment services (umaenrl@maine.edu)
For graduating DSE students:
Checklist for graduating students:
- In the semester before your graduation, please contact the DSE Director with your current program of study spreadsheet to check your program progress.
- You also need to fill out an official program of study that is signed by you and the DSE Director. She will submit it to the Graduate School. The forms can be found here:
- Students must be registered for at least 1 CR in the semester they plan to graduate.
- In the semester you plant to graduate, you need to apply for Graduation on Mainestreet. Please check for more information on the graduation process the Graduate School website
- Note the deadlines for application for graduation.
- If you accidentally missed the application deadline, please contact Student Records at um.gradapply@maine.edu for assistance.
Advising Notes
Math Readiness – What is the one last thing that you might still accomplish that might better prepare you for entering computing courses at the University of Maine?
True Story: After reading a book about Kahn Academy assigned in the COS 490 class, one of our top senior students in computer science came to the realization that his math education indeed had many holes in it due to missing concepts somewhere along the line or just forgetting concepts he had previously learned. Even though he had already completed all the required math courses in the curriculum with A’s (including the calculus course sequence), he went back to the third grade level in Kahn Academy and marched though all of the online student lessons up to and through the Algebra materials. Spending an hour or so each evening, it took him several weeks. He aced his graduate record exams for entry into grad school and is convinced that this computer-aided self-learning and review in math made all the difference. Thus, if you want to better prepare yourself for some of your computing courses, you might want to consider following his suggested process before and even after you start your program in the School of Computing and Information Science. If pressed for time, however, perhaps a review of the Algebra I and II materials may be sufficient. See https://www.khanacademy.org/math
Elective Courses Not Contained in the Official UMaine Catalog – All of the elective courses listed above continue to be accepted. Many of these courses at other campuses and in other programs are NOT listed in the official online catalog for pragmatic reasons. However, these elective courses are typically still accepted by the faculty for inclusion on your Program of Study. Other relevant courses may also be petitioned for that are not on the above current list.
Transfer Courses – Any course taken at another university that is included for credit on your Program of Study is viewed as a transfer course. This requires approval in the process of admission or through the approval process for the Program of Study.
Waived Courses – Waived courses are required courses that need not be taken because the student has already covered the subject matter of the course in previous courses. See the SCIS Course Waiver Form. If a course is waived, another course is taken in its place with approval of the Graduate Coordinator in consultation with the faculty. In some instances, the replacement course is prespecified. For instance if SIE 507 is waived, the replacement course is SIE 508 unless that subject matter as well has already been covered in previous coursework by the student.
Taking Courses from Other Campuses – If a course listed above is taken from another campus, it must first be approved on your Program of Study (See Master’s POS or Grad Certificate POS). After approval, you must complete the Domestic Study Away Form (DSAF) for each course taken on another campus. Each DSAF should be submitted near the time in which you enroll in any course from away. Sign it, submit it first to the Graduate Coordinator (harlan.onsrud@maine.edu) and then send the form with those two signatures to the UMaine Graduate School (debbi.clements@maine.edu). The graduate school will forward the form to other campus offices that may need it to validate your active student status, particularly if you are receiving any financial aid.
Programs of Study (POS) – If needed for study away courses or for documenting the acceptance of transfer courses, please complete the POS form immediately upon admission. Otherwise, completing the POS after one or two semesters in the program is fine. (See https://umaine.edu/graduate/ > Students (in the upper menu)> Forms and Documents> Master’s and CAS Program of Study and/or Certificate Program of Study as appropriate.) In completing the form, you may determine the semester that SIE courses are typically offered by consulting (a) the tables found in the Data Science Curriculum that contains as well most of the MSIS courses (search by the course number) or (b) the Graduate Student Guide on pages 11 through 14. For the MSIS Program of Study, include ten and only ten courses on the POS. For the IS Graduate Certificate include only the 5 required courses. All information and your signature must be supplied. If you don’t yet know the exact semester for each course or the exact course(s) you will ultimately pursue, take a best guess for now. You may always alter the form later by submitting a Change in Program of Study form available from the same link.
On-Leave Status – If you decide to enroll in no courses for one or more regular academic year semesters, your are required to submit a Request for On-Leave Status. If you fail to submit such a request, you are assumed to have left the program by the Graduate School.
Application for Graduation – As graduation approaches, you should file a Completion of Degree Requirements form with the Graduate School. This often first requires submission of a Change in Program of Study form to ensure that any changes in your POS have been approved by the faculty. (See https://umaine.edu/graduate/students/forms-and-documents/ > Change in Program of Study and/or Completion of Degree Requirements as appropriate.)