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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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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Credits

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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

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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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Course Title

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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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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

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Course Title

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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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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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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)