Mar 29, 2024  
2022-2023 Graduate Catalog 
    
2022-2023 Graduate Catalog [ARCHIVED CATALOG]

Data Science and Engineering


Return to {$returnto_text} Return to: Graduate Programs, Certificates, Specializations, Emphases

Graduate Catalog Description for Data Science and Engineering

 

 

Return to: Graduate Programs, Certificates, Specializations, Emphases

The Data Science and Engineering programs offered at the University of Maine are intended to meet the growing demand for graduates with core skills in managing and analyzing complex data and analytics challenges. The graduate programs provide a pathway for students from diverse fields to transition to multiple data science and engineering career paths by providing them with core graduate-level courses across the entire spectrum of the data lifecycle.

In support of the interdisciplinary spirit of data science and engineering, the program is designed to accommodate students from a wide range of undergraduate degrees or other graduate degree backgrounds with options for specialization in different domains.  A collection of courses with a variety of in-class and online options support students in residence as well as meet the needs of people currently in the workforce or who are otherwise place-bound and need training or retraining in the area of Data Science and Engineering.

Graduate programs offered include the Master of Science in Data Science and Engineering (thesis and coursework-only options) and the Graduate Certificate in Data Science and Engineering. For high-performing qualifying undergraduate students that may be pursuing any degree program, an Accelerated Four-Plus-One option exists allowing completion of an undergraduate degree and the MSDSE in five years.

 

Program Objectives

Graduates of the master’s program achieve the following learning objectives and outcomes:

  • an appreciation of data sources, the data acquisition process, data types, data quality, and methods for cleaning.
  • an understanding of issues impacting the efficient processing, representing, storing, managing, and retrieval of large amounts of data.
  • an understanding of how to leverage modern computational infrastructures and software tools to perform large-scale data analysis and machine learning.
  • an understanding of common analytical tools, their methods, their effective use, and the strengths and limitations of each.
  • the skills to effectively explore and present data to different audiences through visual and multimodal methods.
  • a familiarity with data security, curation, and preservation strategies
  • the ability to form questions for analysis from an understanding of the characteristics and goals of different application domains
  • an understanding of artificial intelligence and its applications
  • an awareness of the ethical issues, risks, and responsibilities related to data science. 

 

Master of Science in Data Science and Engineering  

The University of Maine offers both thesis and course-work only options in the Master of Science in Data Science and Engineering. All work for a master’s degree must be completed within six years. The timing starts with the first semester of registration after admission to the Master of Science in Data Science and Engineering.

The thesis option is the scientific track, typically requiring a strong engineering, computer science, human-computer interaction, or mathematics undergraduate background. Prospective master’s students with other disciplinary backgrounds are expected to make up the requisite math and engineering courses that would allow them to succeed in the graduate curriculum. The thesis option includes a substantial piece of individual research as a basis for a master’s thesis.

The coursework-only option is aimed at students who desire to focus primarily on coursework rather than research at the master’s level. The formal coursework is complemented by an internship requirement or a one-semester project in which the student must demonstrate that he or she can apply acquired knowledge for implementing a particular solution.

 

Degree Requirements

Applicants to the data science and engineering program should have at least one college level statistics course in their backgrounds. Admitted students have the opportunity to become familiar with various data science, data mining, data engineering, business analytics, machine learning, and artificial intelligence topics. Computer programming, statistics germane to data science, and systems knowledge may be picked up as part of the program if applicants don’t already have these foundations. Applicants with undergraduate degrees in computer science, engineering, math, and similar fields (i.e., those with two semesters of calculus and calculus-based statistics) have the opportunity to pursue higher level machine learning and artificial intelligence fundamentals and theory courses along with applications of advanced AI methods addressing real-world problems. The multiple paths to graduation and multi-disciplinary course opportunities make the program highly flexible in meeting individual student needs.

 

Master (Coursework-Only Option)

A candidate must complete 30 graduate course credits on-campus or online on a program of study approved by advisors that includes:

  • A specified foundation course in each of statistics, programming, and systems unless waived based on previous coursework
  • DSE 510 Practicum in Data Science and Engineering (3cr)
  • 12 course credits drawn from at least four of the five Theme Areas
  • At least one course must include a substantial practical experience. Options include SIE 589 Graduate Project, SIE 590 Information Systems Internship, or a course from an approved list.
  • Further course credits from within the Foundation Courses, Theme Areas, or Domain Specializations to bring the total to 30 credits
  • No more than 6 course credits, if any, at the 400 level

 

Foundation Courses

Statistics Foundations

Programming Foundations

Systems Foundations

 

Theme Area Courses

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

 

Master (Thesis Option)

A candidate must complete 30 graduate course credits on a program of study approved by advisors that includes:

  • Specified foundation courses in each of statistics, programming, and systems unless waived based on previous coursework
  • DSE 510 Practicum in Data Science and Engineering (3cr)
  • SIE 501 Introduction to Graduate Research (1cr)
  • SIE 502 Research Methods (1cr)
  • INT 601 Responsible Conduct of Research (1cr)
  • 12 course credits drawn from at least four of the five theme areas
  • 6 credits of thesis
  • Further course credits from within the foundation courses, theme areas, or domain specializations to bring the total to 30 credits
  • No more than 6 course credits, if any, may be at the 400 level

 

For either master’s degree option, a maximum of six credit hours of graduate course work taken prior to enrollment in the master’s program, whether at this university or another, may be counted toward the master’s degree assuming that the course(s) did not count toward a completed undergraduate or graduate degree and if the student’s graduate advisory committee formally approves acceptance of the courses on the student’s Program of Study.

 

Admission Requirements

Admission to the MS Data Science and Engineering is competitive. In the admission process, the graduate faculty considers the potential of applicants to complete a program successfully and achieve a position of leadership in the private, public or research sectors.

Students with undergraduate degrees in any field may apply. The bachelor’s degree should be from an accredited four-year U.S. accredited college or university with a 3.0 cumulative or higher GPA, or equivalent international university degree with comparable academic performance (exceptions considered on case-by-case basis)

Applications are accepted on a rolling basis and no strict deadlines apply. Thesis-based MS students applying for campus-wide research assistantships or scholarships should take and submit the GRE and complete their application packets by January 1 for fall admission. We generally seek students that score at the mean or above on the verbal, quantitative and analytical segments of the GRE exam and in the 50th percentile or above on the exam overall. Exceptions are considered on a case-by-case basis.

 

Required information in the MSDSE online application should include transcripts from previous institutions, test scores (if required), current resume that includes contact information for three references, an essay, and the application fee. For detailed instructions, see Further Admission Information

 

Accelerated Four Plus One Program: Early Admission for UMaine Undergraduate Students

Undergraduate students from any degree program at the University of Maine may apply as early as the summer before their junior year for admission to the MS Spatial Information Science and Engineering (Coursework-Only Option) graduate degree program. Applications for conditional “early admission” should be received preferably by the middle of the first semester of the junior year and are not accepted after the senior year has commenced. The final year in completing the Master’s degrees may be taken either on-campus or online.

 

By taking a course overload of three credits in the second semester of the Junior year and course overloads in each of the semesters of the Senior year, a motivated student typically may acquire 9 credits (but no more than 12) for graduate school (at undergraduate tuition rates) prior to acquiring their undergraduate degree assuming that they receive a B or better in the courses. These courses, if chosen appropriately, may double count toward both the undergraduate and graduate degree. By taking a 3-credit Information Systems Internship graduate course with a corporation, agency or non-profit organization during the summer, a student may readily complete the coursework master’s degree in a single year after their undergraduate degree. This master’s degree will be highly complementary to an undergraduate degree in almost any field and attractive to employers.

 

To apply for early admission before or during the junior year, an applicant should expect to have an overall minimum undergraduate grade point average of 3.25, must have completed the University of Maine General Education Requirement in Math and must have three letters of recommendation from current or previous university instructors. Apply using the Application for Admission to the DSE Four Plus One Program. Continuation in the graduate program is based primarily on performance in the graduate courses and overall grade point average upon graduation from the undergraduate program. Accepted Four Plus One students must complete the full graduate application in their senior year. The GRE exam is typically waived for these accepted high performing students. Below a 3.0 accumulated undergraduate grade point average should be assumed cause for discontinuation in the graduate program.

 

Students with two or fewer semesters remaining to complete their undergraduate degree program do not qualify for the accelerated “four-plus-one program” but their applications will be considered as applications within the regular graduate admissions process. In this case, one may transfer up to two graduate courses prior to formal admission assuming those courses did not count toward another degree.

 

Financial Assistance  

In addition to University fellowships and scholarships listed elsewhere in this Catalog, the advising professor or other DSE  graduate faculty may offer graduate research assistantships to qualified students on externally funded research projects. A very limited number of teaching assistantships may be available. Consult as well Funding at the Graduate School web site.

 

Data Science and Engineering Graduate Faculty

Ali Abedi

Professor, Electrical and Computer Engineering

Kate Beard-Tisdale

Professor, Spatial Computing

Kathleen P. Bell

Professor, Economics

Sudarshan Chawathe

Associate Professor, Computer Science

Phillip Dickens

Associate Professor, Computer Science

Matthew Dube

Assistant Professor, Computer Information Systems

Richard Eason

Associate Professor, Electrical and Computer Engineering

Max Egenhofer

Professor, Spatial Computing

Keith Evans

Associate Professor, Economics

Sepidah Ghanavati

Assistant Professor, Computer Science

Nicholas Giudice

Professor, Spatial Computing

Ramesh C. Gupta

Professor, Mathematics and Statistics

Pushpa Gupta

Professor, Mathematics and Statistics

Torsten Hahmann

Associate Professor, Spatial Computing

Daniel Hayes

Associate Professor, Forest Resources

David Hiebeler

Professor, Mathematics and Statistics

Raymond Hintz

Professor, Surveying Engineering Technology

Don Hummels

Professor, Electrical and Computer Engineering

Jon Ippolito

Professor, New Media

Shaleen Jain

Professor, Civil and Environmental Engineering

Tora Johnson

Environmental and Biological Sciences, University of Maine at Machias

Nory Jones

Professor, Maine Business School

Andre Khalil

Professor, Chemical and Biological Engineering

Benjamin King

Assistant Professor, Bioinformatics

Anne Kelly Knowles

Professor, History

Cyndy Loftin

Associate Professor, Wildlife, Fisheries, and Conservation Biology

Yonggong (Tim) Lu

Associate Professor, Maine Business School

Jonathan Malacarne

Assistant Professor, Economics

Craig Mason

Professor, Education and Applied Quantitative Methods

Brian McGill

Professor, Biological Science

Silvia Nittel

Associate Professor, Spatial Computing

Harlan Onsrud

Professor, Spatial Computing

Nigel Pitt

Professor, Mathematics and Statistics

Parinaz Rahimzadeh-Bajgiran

Assistant Professor, Forest Resources

Nimesha Ranasinghe

Assistant Professor, Spatial Computing

Andrew Reeve

Professor, Earth and Climate Sciences

Penny Rheingans

Professor, Computer Science

Judith Rosenbaum

Associate Professor, Communication and Journalism

Mike Scott

Lecturer, New Media

Bruce Segee

Professor, Electrical and Computer Engineering

Salimeh Yasaei Sekeh

Assistant Professor, Computer Science

Ali Shirazi

Assistant Professor, Civil and Environmental Engineering

Andrew Thomas

Professor, School of Marine Sciences

Roy Turner

Associate Professor, Computer Science

Vince Weaver

Associate Professor, Electrical and Computer Engineering

J. Michael Weber

Professor, Maine Business School

Zheng (David) Wei

Assistant Professor, Mathematics and Statistics

Aaron Weiskittel

Professor, School of Forest Resources

Thomas Wiesen

Assistant Professor, Economics

Manuel Woersdoerfer

Assistant Professor, Maine Business School

Terry S. Yoo

Associate Professor, Computer Science

Yifeng Zhu

Professor, Electrical and Computer Engineering

Return to {$returnto_text} Return to: Graduate Programs, Certificates, Specializations, Emphases