Data Science and Engineering Graduate Course Groupings (Desktop)
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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) Concentrations
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.
Foundation Courses
Admitted candidates missing appropriate background prerequisite courses will take one foundation in each of the three foundation areas of statistics, programming, and systems as appropriate and as advised by their graduate committee and/or advisor. The 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:
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Sem / Instr
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| Statistics Foundations | |||||
| COS598 (DSE501) | Statistical Foundations of Data Science and Engineering | 3 | College level statistics | Yes | Fall – Sekeh |
| STS437 | Statistical Methods in Research | 3 | Some statistics | No | Fall – contact dept |
| ECE515 | Random Variables & Stochastic Processes | 3 | Grad standing, ECE 316 or equivalent | Yes | Fall – Abedi |
| Programming Foundations | |||||
| SIE507 | Information Systems Programming | 3 | Program admission | Yes | Fall – Ranasinghe |
| Systems Foundations | |||||
| ECE598 (DSE503) | Systems Foundations for Data Science and Engineering | 3 | SIE 507, equivalent, or permission | Yes | Spring – Zhu |
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.
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| DSE510 (SIE598) |
Practicum in Data Science and Engineering |
3 | Program admission and SIE507 or permission | Yes | Spring – Beard |
The following courses are all required for all students pursuing the MS Data Science and Engineering with Thesis Option.
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| SIE501 | Introduction to Graduate Research | 1 | Program admission | Yes | Spring – Egenhofer |
| SIE502 | Research Methods | 1 | SIE501 | Yes | Fall – Egenhofer |
| INT601 | Responsible Conduct of Research | 1 | Grad standing | Yes | Fall, Spring – Woersdoerfer |
Theme Courses
Courses currently contained within the themes include the following:
Theme 1: Data Collection Technologies
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Prerequisites
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Online
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Sem/Instr
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| BUA682 | Data Pre-processing for Business Analytics | 3 | Intro stats and some programming | Yes | Spring – Lu MBS |
| ECE533 | Advanced Robotics | 3 | ECE 417 or instr permission | No | Spring – Eason |
| ECE571 | Advanced Microprocessor-based Design | 3 | ECE 471 or instr permission | No | Fall – Weaver |
| ECE585 | Fundamentals of Wireless Communications | 3 | ECE484 | Yes | Spring – Abedi |
| SFR609 | Remote Sensing Problems | 3 | Instructor permission | No | Fall, Spring – Hayes/ Morin/ Rahimzadeh |
| SIE559 | Geosensor Networks | 3 | Programming | Yes | Fall – Nittel |
| SMS540 | Satellite Oceanography | 3 | SMS 501 and SMS 541 or permission | No | Fall – Thomas |
| SVT437 | Practical GPS | 3 | SVT 341 | Yes | Fall – Hintz |
| SVT531 | Advanced Digital Photogrammetry | 3 | SVT 331 | Yes | Spring – Hintz |
| SVT532 | Survey Strategies in Use of Lidar | 3 | SVT 331 | Yes | Spring – Hintz |
Theme 2: Data Representation and Management
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Course Title
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Prerequisites
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Online
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Sem/Instr
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| BUA681 | Data Management and Analytics | 3 | Intro stats and some programming | Yes | Fall – Suleiman MBS |
| COS580 | Topics in Database Management Systems | 3 | Instructor permission | No | Fall – Chaw |
| COS598 (COS541 /441) | Cloud Computing | 3 | COS 331 or equivalent | No | Spring – Dickens |
| ECE574 | Cluster Computing | 3 | Instr permission | No | Spring – Weaver |
| ECE583 | Coding and Information Theory | 3 | ECE515 or permission | Yes | Spring – Abedi |
| SIE550 | Design of Information Systems | 3 | Program admission or instr permission | Yes | Fall – Egenhofer |
| SIE557 | Database Systems Applications | 3 | SIE507 or programming | Yes | Spring – Nittel |
| SIE585 (SIE580) | Formal Ontologies: Principles and Practice | 3 | SIE505 or instr permission | Yes | Fall – Hahmann |
Theme 3: Data Analytics
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Sem/Instr
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| BIO593 | Advanced Biometry | 3 | Course in statistics | No | Fall – McGill |
| BMB520 | Introduction to Image Analysis | 3 | Program admission | No | Fall – Kelley |
| BUA684 | Business Data Mining and Knowledge Discovery | 3 | Intro stats and some programming | Yes | Spring – Lu |
| CMJ601 | Seminar in Research Methods | 3 | Instructor permission | No | Rosenbaum – Fall |
| COS 470/ COS570 | Introduction to Artificial Intelligence | 3 | Instructor permission | No | Spring – Turner |
| COS475/ COS575 (COS598) | Machine Learning | 3 | MAT 126, MAT 127, STS 232 (or STS332, 434,435) | Yes | Spring – Sekeh |
| COS473/ COS573 (COS598) | Computer Vision | 3 | COS 226 or instr permission | No | Fall – Yoo |
| ECE577 | Fuzzy Logic | 3 | Program admission or instr permission | No | Spring – Segee |
| ECE584 | Estimation Theory | 3 | ECE 515 or instr permission | Yes | Summer – Abedi |
| ECE590 | Neural Networks | 3 | Instructor permission | No | Fall – Musavi |
| ECE598 (ECE591) | Deep Learning | 3 | Program admission or instr permission | No | Fall – Zhu |
| ECO530 | Econometrics | 3 | MAT126 & MAT215/MAT232 or permission | No | Fall – Malacarne |
| ECO531 | Advanced Econometrics and Applications | 3 | B or better in ECO530 or permission | No | Spring – Evans |
| ECO532 | Advanced Time Series Econometrics | 3 | ECO530 or permission | No | Spring – Wiesen |
| EHD572 | Advanced Qualitative Research | 3 | EHD571 or equivalent | No | Spring – Fairman |
| EHD573 | Statistical Methods in Education I | 3 | None listed | Yes | Fall, Spring -Mason |
| EHD574 | Statistical Methods in Education II | 3 | EDH573 or equivalent | Yes | Spring – Mason |
| PSE509 | Experimental Design | 4 | None listed | No | Spring – Porter |
| PSY540 | Advanced Psychological Statistical Methods and Analysis I | 3 | PSY241 or equivalent | No | Fall – Eli |
| PSY541 | Advanced Psychological Statistical Methods and Analysis II | 3 | PSY241 or equivalent | No | Spring – Eli |
| SFR528 | Qualitative Data Analysis in Natural Resources | 3 | EDH571 or permission | No | Fall – De Urioste-Stone |
| SMS595 | Data Analysis Methods in Marine Science | 3 | MAT126 or equivalent | No | Spring – Xue |
| STS531 | Mathematical Statistics | 3 | C or better in MAT425, STS434, or permission | No | Fall – contact dept |
| STS533 | Stochastic Systems | 3 | C or better in STS434 | No | Spring – contact dept |
Theme 4: Data Visualization and Human Centered Computing
| Number
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Course Title
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Credits
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Prerequisites
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Online
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Sem/Instr
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| BUA683 | Information Visualization | 3 | Intro stats and some programming | Yes | Spring – Suleiman MBS |
| COS565 | Data Visualization | 3 | COS226, SIE508, or permission | No | Spring – Rheingans |
| SIE515 | Human Computer Interaction | 3 | Program admission or instr permission | Yes | Fall – Giudice |
| SIE516 | Interactive Technologies for Solving Real-World Problems | 3 | Program admission or instr permission | No | Spring – Giudice |
| SIE517 (SIE598) | Spatial Interaction Design | 3 | Program admission or instr permission | Yes | Spring – Ranasinge |
Theme 5: Data Security, Preservation, and Reuse
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Sem/Instr
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| COS435/ COS535 (COS598) | Information Privacy Engineering | 3 | college level knowledge of IT or software development | Yes | Fall – Ghanavati |
| DIG500 | Introduction to Digital Curation | 3 | None listed | Yes | Fall – Ippolito/ Wolf/ Bird |
| DIG510 | Metadata Systems | 3 | DIG500 recommended | Yes | Summer – Bell |
| DIG550 | Digital preservation | 3 | DIG500, 510, & 540 recmmndd | Yes | Spring – Ippolito/ Bird |
| SIE525 | Information Systems Law | 3 | Program admission or instr permission | Yes | Spring – Onsrud |
| CYB501 | Cybersecurity Fundamentals | 3 | Graduate standing | Yes | Fall, Spring – Felch Augusta |
| CYB520 | Cybersecurity Policy and Risk Management | 3 | Graduate standing | Yes | Spring – Felch Augusta |
| CYB551 | Cybersecurity Investigations | 3 | Graduate standing | Yes | Spring – Sussman Augusta |
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
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Sem/Instr
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| SIE505 | Formal Foundations for Information Science | 3 | SIE 550 or instr permission | Yes | Spring – Hahmann |
| SIE509 | Principles of Geographic Information Systems | 3 | Program admission or instr permission | Yes | Fall – Beard/ Egenhofer |
| SIE510 | GIS Applications | 3 | SIE 509 or instr permission | Yes | Spring – Beard |
| SIE512 | Spatial Analysis | 3 | Intro stats or instr permission | Yes | Fall – Beard |
| SIE555 | Spatial Database Systems | 3 | SIE 550 and programming | Yes | Fall – Nittel |
| SIE558 | Real-time Sensor Data Streams | 3 | Programming or instr permission | Yes | Fall – Nittel |
| INT527 | Integration of GIS and Remote Sensing Analysis in Natural Resource Applications | 3 | Permission and grad standing | No | Spring – Hayes/ Loftin |
| CIS461 /DSC461 | Spatial-Temporal Information Science | 3 | CIS360 or permission | Yes | Spring – Dube Augusta |
| GEO605 | Remote Sensing | 3 | Graduate standing | Yes | Pavri – Spring USM |
| ANT521 | Geographic Information Systems I | 3 | Instructor permission | Yes | Fall, Spring – Johnson Machias |
| ANT522 | Geographic Information Systems II | 3 | ANT521 or permission | Yes | Fall, Spring – Johnson Machias |
| GIS420 | Remote Sensing & Image Analysis | 3 | ANT522 or permission | Yes | Fall – Johnson Machias |
| GIS426 | Community Applications of GIS | 3 | ANT522 or permission | Yes | Fall – Johnson Machias |
| GIS428 | Web-Based Maps, Applications & Services | 3 | ANT521 and ANT522 or permission | Yes | Spring -Bistrais Machias |
Domain B: Bioinformatics / Biomedicine
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Online
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Sem/Instr
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| BMB502 | Introduction to Bioinformatics | 3 | BMB280 or permission | Yes | Spring – King |
| BMB520 | Introduction to Image Analysis | 3 | Program admission | No | Fall – Kelley |
| BMS625 | Foundations of Biomedical Science and Engineering | 1 | None listed | No | Fall – Dube |
| ECE583 | Coding and Information Theory | 3 | ECE 515 or permission | Yes | Spring – Abedi |
| SIE505 | Formal Foundations for Information Science | 3 | SIE 550 or instr permission | Yes | Spring – Hahmann |
Domain C: Business Information
| Number
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Course Title
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Credits
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Prerequisites
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Online
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Sem/Instr
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| BUA680 | Foundations of Business Intelligence and Analytics | 3 | Intro stats | Yes | Fall, Spring – Lu MBS |
| BUA684 | Business Data Mining and Knowledge discovery | 3 | Intro stats and some programming | Yes | Spring – Lu MBS |
| BUA685 | Problem Solving and Decision Analysis | 3 | Intro stats, econ prin and some programming | Yes | Fall – Lu MBS |
| BUA686 | Predictive Analytics and Business Forecasting | 3 | Intro stats and some programming | Yes | Summer – Lu MBS |
| CIS450/ BUA450/ DSC450 | Data Mining | 3 | CIS255 or 352 or 360 or 449 or permission | Yes | Fall – Dube Augusta |
Domain D: Social and Behavioral Data Science
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Online
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Sem/Instr
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| HTY665 | Digital and Spatial History | 3 | Grad standing | Yes | Spring -Knowles |
| CMJ593 | Special Topics in Communication: Social Media and Digital Cultures | 3 | Instructor permission | No | Fall – Rosenbaum |
Domain E: Engineering Analytics
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Course Title
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Credits
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Prerequisites
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Online
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Sem/Instr
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| ECE515 | Random Variables and Stochastic Processes | 3 | grad standing, ECE 316 or equivalent | Yes | Fall – Abedi |
| CIE598 | Civil Engineering Systems and Optimization | 3 | MAT 126, MAT 127, instr permission | No | Fall – Shirazi |
| ECE573 | Microprogramming | 3 | ECE471 or ECE 475 | No | Fall – Segee |
| ECE523 | Mathematical Methods in Electrical Engineering | 3 | Senior or grad standing in ECE | No | Fall – Segee |
| ECE533 | Advanced Robotics | 3 | ECE 417 or permission | No | Spring – Eason |
| ECE571 | Advanced Microprocessor-based Design | 3 | ECE 471 or instr permission | No | Fall – Weaver |
| ECE574 | Cluster Computing | 3 | Instr permission | No | Spring – Weaver |
| ECE577 | Fuzzy Logic | 3 | ECE477 or permission | No | Spring – Segee |
| ECE583 | Coding and Information Theory | 3 | ECE515 or permission | Yes | Spring – Abedi |
| ECE584 | Estimation Theory | 3 | ECE 515 or instr permission | Yes | Summer – Abedi |
| ECE585 | Fundamentals of Wireless Communication | 3 | ECE484 | Yes | Spring – Abedi |
| ECE590 | Neural Networks | 3 | Instructor permission | No | Fall – Musavi |
| ECE598 (ECE591) | Deep Learning | 3 | Program admission or instr permission | No | Fall – Zhu |
Artificial Intelligence (AI) Concentrations (DRAFT)
The DSE program does not have an officially designated concentration 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 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 equivalent.
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.
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 – 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 – currently COS 598)
- COS 573/473 Computer Vision
- SIE 554 Spatial Reasoning (tentative)
- SIE 585 Ontology Engineering (tentative)
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)