Data Science and Engineering Graduate Course Groupings

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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)