Master of Science in Data Science

Master of Science in Data Science

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The University of Guam M.S. in Data Science program is a comprehensive and cohort-based study requiring 30 credit hours. Delivered in a face-to-face format, the curriculum places a strong emphasis on the practical applications of statistical methodology, computational science, and diverse domains. It includes a range of topics, including statistical modeling, machine learning, optimization, data management, analysis of large datasets, and data acquisition.

Throughout the program, students will explore reproducible data analysis, collaborative problem-solving, and honing visualization and communication skills. Also, the curriculum addresses ethical and security issues intrinsic to data science. Students will have developed expertise in applying data science techniques to solve real-world problems across various domains.

Program Objectives

Master of Science in Data Science program objectives are:

To establish the first regional graduate program in Data Science that provides affordable education options to local students. The program will offer lower resident tuition rates and access to financial aid and support programs compared to similar off-island programs. Additionally, the program will leverage existing UOG grants such as U54, EPSCOR, and NASA to support students.
To equip students with the necessary skills to work as data analysts in both academic and industry settings. Graduates can contribute to existing research initiatives at UOG, further enhancing research capabilities at the university.

Program Learning Outcomes

Students completing the Master of Science in Data Science Program at UOG will be able to:

Design and execute statistical experiments and hypothesis tests to extract meaningful insights from data.
Analyze and interpret complex statistical data using advanced statistical methodologies and tools.
Visualize data for exploration, analysis, and communication.
Develop and implement predictive models and machine learning algorithms to make data-driven decisions.
Communicate statistical analyses, findings, and recommendations to both technical and non-technical audiences effectively.
Collaborate with interdisciplinary teams to design, implement, and evaluate statistical projects.

Application Requirements

Applicants must have the following minimum qualifications, to be eligible to apply to the program:

Earned baccalaureate degree in mathematics, computer science, biology, chemistry, statistics, psychology, or public health from an accredited college or university.
Graduate admission application and application fee
Official transcripts of all coursework completed.
At least two letters of recommendation
Current resume
Minimum cumulative undergraduate grade point average of 3.0.

In addition, undergraduate students must complete the following prerequisites or equivalent before entering the program:

Multivariate Calculus, MA-205
Linear Algebra, MA-341
Statistics course, MA-387+MA-387L, or BI-412+BI-412L

Or Bridge Course (no credit toward degree)

The bridge course will cover calculus, linear algebra and statistics topics necessary for data science courses. The Bridge Course will take place during the UOG summer Session C, preceding the program's start.

Calculus topics and learning outcomes:

Vector calculus and multivariate integration
Partial derivatives and gradient descent
Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients

Linear algebra learning outcomes:

Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
Apply common vector and matrix algebra operations like dot product, inverse, and determinants
Express certain types of matrix operations as linear transformation and apply concepts of eigenvalues and eigenvectors to simple machine learning problems

Statistics topics:

Calculate the descriptive statistics
Understand the properties of commonly used probability distributions in machine learning and data science
Conduct various statistical tests including a T test, an ANOVA, and regression analysis
Interpret the results of your statistical analysis after conducting hypothesis testing.

M.S. in Data Science Degree Requirements (30 credit hours)

Required Courses (17-20 credit hours)

MA-541 REGRESSION MODELS AND APPLICATIONS (4 credit hours)

MA-551 INTRODUCTION TO PROBABILITY THEORY (3 credit hours)

MA-552 INTRODUCTION TO MATHEMATICAL STATISTICS (3 credit hours)

MA-564 MULTIVARIATE ANALYSIS (3 credit hours)

MA-571 STATISTICAL RESEARCH AND CONSULTING (1-3 credit hours)

MA-581 MACHINE LEARNING FOR DATA SCIENCE (3 credit hours)

Elective Courses (10-13 credit hours)

AL-505 NUTRITIONAL EPIDEMIOLOGY (3 credit hours)

EV-558 ADVANCED GEOSPATIAL METHODS (4 credit hours)

MA-453 OPERATIONS RESEARCH (3 credit hours)

BA-622 MULTIVARIATE ANALYSIS (3 credit hours)

CS-420 COMPUTER AND NETWORK SECURITY (3 credit hours)

MA-505 INTRODUCTION TO SAS (1 credit hour)

MA-500 INTRODUCTION TO R (1 credit hour)

AL-594 CANCER HEALTH DISPARITIES (3 credit hours)

The master's program offers flexibility by not requiring a thesis. Instead, students can pursue alternative capstone projects or practical experiences aligned with their interests and goals.

Sample Schedule for Covering Requirements

Below is a sample schedule of the program across four semesters (two years), offering flexibility in personalizing your educational journey:

Fall 2024

Spring 2025

Fall 2025

Spring 2026

MA-551 (3 credits)

MA-541 (4 credits)

MA-500 (1 credit)

MA-505 (1 credit)

MA-552 (3 credits)

MA-564 (3 credits)

Elective (3 credits)

MA-581 (3 credits)

MA-571 (2 credits)

Elective (3 credits)

MA-571 (1 credit)

Elective (3 credits)

Total credits: 9

Total credits: 8

Total credits: 4

Division Chair

Aquino J. Camacho Leslie

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Master of Science in Data Science