Department of Mathematics and Statistics

Supplemental Content for MS in Data Science

There is a rapidly growing regional and national demand for qualified data scientists. Our program equips professionals with high-level skills in data science, including analytics, communication, and work in a diverse team environment.

Who is this program for?

  • Both undergraduate and graduate degree holders with an interest in computing and data analytics.
  • Working professionals involved in analytics.
  • We welcome prospective students from a diverse set of academic and vocational backgrounds.

Program highlights

  • 30 required credit hours.
  • Develop your technical skills in the areas of computer science, mathematics, and statistics through a set of core required courses.
  • Select a concentration track in the areas of business analytics, computation, geographic information systems (GIS), public health, prescriptive analytics, or predictive analytics.
  • Choose to complete a practicum or a thesis for your capstone project.

Upon program completion, you will be able to:

  • Collect, prepare, visualize, and analyze data.
  • Interpret results in an interdisciplinary context.
  • Use critical thinking skills and apply knowledge and methods when analyzing real-world problems and developing state-of-the-art solutions.
  • Communicate findings effectively to key stakeholders.
  • Formulate and lead teams that can integrate the essential body of knowledge to produce solutions to real-world problems.
  • Understand and take into account ethical concerns associated with data collection.
  • Develop a strong sense of community identity, gaining perspectives by belonging and actively contributing to the scientific community.

Applicant Qualifications 

GPA of a 3.0 or higher from an undergraduate degree is required/recommended.

One course from each of the three following categories:

Calculus

  • MAT 152 – Calculus A
  • MAT 153 – Calculus B

Probability and Statistics

  • MAT 210 – Business Statistics
  • MAT 220 – Statistics for Biological Sciences
  • MAT 282 – Statistical Inference
  • MAT 380 – Probability and Statistics

Computer Science

  • COS 160 – Structured Problem Solving: Java
  • COS 184 – Python Programming

 

Application Procedures and Required Materials

Application Deadlines

  • August 1 for Fall semester; December 15 for Spring semester
  • International Student deadlines: July 1 for Fall Semester, November 1 for Spring semester

All materials need to be submitted by the deadline. Applications are read on a rolling basis.

Applicants are required to provide the following materials: 

  • Application: Online Application
  • Application fee: Waived for all applicants 
  • Transcripts: Official transcripts from all colleges or universities attended, excluding the seven campuses of the University of Maine System (UMS transcripts are accessible to USM). A transcript is official when sent directly from the institution. 
  • Resume: Submit a resume or CV that outlines professional, volunteer, and community experience.
  • Essay: Why are you pursuing a degree in data science? How does this degree align with your professional goals?
  • Letters of Recommendation:  Three letters of recommendation. Recommendations should be from individuals who are qualified, through direct experience with your academic or professional work, to comment on your ability to undertake graduate study and your chosen profession.  
  • TOEFL or IELTS: Students whose first language is not English may be required to take the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS) and submit official scores as part of the application process. See additional information below regarding test scores.

   

Where to send transcripts and application materials

Official transcripts and other supporting documents can be sent to:

Application Processing Center
University of Maine System
P.O. Box 412
Bangor, ME 04402-0412

Colleges and universities that participate in the electronic submission of transcripts can send official transcripts to edocs@maine.edu. Resumes, essays, and other documents can also be sent to edocs@maine.edu

For a transcript or recommendation to be considered official, it must be sent by the institution or the person writing the recommendation.

 

International Applicants 

In addition to the standard application materials, international students must also provide the following materials:

  • College transcript evaluation: official course-by-course evaluation of college-level transcripts from a NACES (National Association of Credential Evaluation Services) approved transcript analysis agency
  • Declaration of Finances form accompanied by the appropriate financial documentation
  • International students whose first language is not English are required to take the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS) and submit official scores as part of the application process. Only applicants with TOEFL scores of 79 or higher on the internet-based test 550 or higher on the paper-based test, or 213 or higher on the computer-based test; or IELTS scores of 6.5 or higher will be considered for admission to a graduate program.


Applicants whose first language is not English 

Students whose first language is not English are required to take the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS) and submit official scores as part of the application process. Only applicants with TOEFL scores of 79 or higher on the internet-based test, 550 or higher on the paper-based test, or 213 or higher on the computer-based test; or IELTS scores of 6.5 or higher will be considered for admission to a graduate program.

Core Coursework

Students must successfully complete the following five required courses (15 credits):

  • Mathematics for Data Science (3 cr.)
  • Machine Learning (3 cr.)
  • Computing for Data Science (3 cr.)
  • Statistical Learning (3 cr.)
  • Introduction to Data Science (3 cr.)

Concentration Tracks

Students must select from one of the following concentration tracks and complete three courses
(9 credits) from within that track.

Business Analytics

  • BUA 601 Data Analysis for Business
  • BUA 676 Market Research and Analysis
  • BUA 680 Foundations of Business Intelligence and Analytics
  • BUA 681 Data Management and Business Analytics
  • BUA 682 Data Pre-processing for Business Analytics
  • BUA 683 / MBA 677 Information Visualization
  • BUA 684 Business Data Mining and Knowledge Discovery
  • BUA 685 Problem Formulation and Decision Analysis
  • BUA 686 Predictive Analytics and Business Forecasting
  • MBA 615 Ethical and Legal Issues in Business
  • MBA 623 Financial Engineering
  • MBA 629 Financial Modeling
  • MBA 669 Advanced Marketing Research

Computation

  • COS 532 Deep Learning
  • COS 558 Database Systems
  • COS 571 Advanced Database Systems
  • COS 572 Artificial Intelligence and Data Mining
  • COS 582 Design and Analysis of Computing Algorithms

Geographic Information Systems

  • GEO 540 Digital Mapping
  • GEO 605 Remote Sensing
  • GEO 608 GIS Applications I
  • GEO 618 GIS Applications II

Public Health

  • MPH 535 Introduction to Epidemiologic Research
  • MPH 545 Applied Biostatistical Analysis
  • MPH 650 Public Health Research and Evaluation
  • MPH 677 Regression Models in Health Sciences
  • STA 501 Ethical Issues in Biostatistics
  • STA 580 Applied Statistical/Biostatistical Methods

Prescriptive Analytics

  • STA 561 Deterministic Models in Operations Research
  • STA 562 Stochastic Models in Operations Research
  • STA 563 System Modeling and Simulation
  • STA 564 Queuing Networks

Predictive Analytics

  • STA 581 Statistical Quality Control
  • STA 582 Introduction to Longitudinal Data Analysis
  • STA 583 Sample Survey Design and Analysis
  • STA 584 Advanced Design and Analysis of Experiments
  • STA 585 Linear Models and Forecasting
  • STA 586 Predicting Modeling with Big Data
  • STA 588 Introduction to Statistical Data Mining
  • STA 589 Survival Analysis

Cybersecurity

  • CYB 501 Cybersecurity Fundamentals
  • CYB 557 Cyber Laws, Policies, and Ethics
  • CYB 583 Databases and Application Defense


Capstone

Students must complete either a thesis (DSC 698, 6 cr) under the supervision of their
concentration track advisers or a practicum/project (DSC 697) plus one additional data science
course.


Ethics

Students must complete ethics training prior to graduation.

Learn more

For more information about our program, please content one of the Program Coordinators:

For information about the admissions process please contact our Office of Admissions.