Master of Science in Data Science
Master of Science in Data Science
Master of Science in Data Science
The University of Guam Master of Science 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.
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.
ADMISSION 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:
|
Course |
Course Title |
Credits |
Term Offered |
| MA205 | MULTIVARIABLE CALCULUS + This course covers the calculus of functions of several variables, including partial
differentiation and multiple integration. It also covers introductory topics in vector
calculus, including vector fields, line integration, Green's Theorem, curl and divergence,
surface integrals, Stokes' Theorem, and the Divergence Theorem. Prerequisite: Grade
of C or better in MA204.
|
5 credit hours | FALL/SPRING/ALL YEARS |
| MA341 | LINEAR ALGEBRA + Topics covered include vectors, systems of linear equations, matrices, eigenvalues
and eigenvectors, vector spaces, determinants and linear transformations. Prerequisite:
Grade of C or better in MA204, or A in MA203.
|
3 credit hours | FALL/SPRING/ALL YEARS |
Choice of:
|
Course |
Course Title |
Credits |
Term Offered |
| MA387 | STATISTICS FOR SCIENCES + The topics include exploring data in graphs and in numerical values, introducing basic
probability theory for statistics, sampling distributions, estimation theory, testing
hypothesis, correlation, linear regression, variance analysis, and non-parametric
statistics. The course consists of three hours of lecture weekly. The lab, MA387L
must be taken concurrently. Prerequisite: Grade C or better in MA161A or higher. Students
enrolled in MA387 Statistics for Sciences for credit may not also earn credit for
MA385 Applied Statistics.
|
3 credit hours | FALL/SPRING/ALL YEARS |
| MA387L | STATISTICS FOR SCIENCE LABORATORY + MA387L is the laboratory part of MA387and MUST be taken concurrently. The purpose
of lab is to reinforce concepts learned in lecture, with an emphasis on translating
familiar statistical problems into SPSS tasks. It emphasizes the principles and criteria
for selecting the appropriate statistical techniques as well as making proper conclusions.
Students will get hands-on experience applying the topics covered to real datasets.
Corequisite: Must take MA387 concurrently.
|
1 credit hour | FALL/SPRING/ALL YEARS |
OR
|
Course |
Course Title |
Credits |
Term Offered |
| BI412 | BIOMETRICS + This is a basic course in the design and analysis of biological experiments. Emphasis
is given to analysis of biological and medical data. The course consists of three
hours of lecture weekly. The lab, BI412l MUST be taken concurrently. Prerequisite:
MA115 and BI321. Corequisite: BI412L.
|
3 credit hours | FALL ONLY/ALL YEARS |
| BI412L | BIOMETRICS LABORATORY + BI412L is the laboratory portion of BI412 and MUST be taken concurrently. The course
consists of one three-hour laboratory period per week. Prerequisite: MA115 and BI321.
Corequisite: BI412.
|
1 credit hour | FALL ONLY/ALL YEARS |
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.
DEGREE REQUIREMENTS
All Data Science classes take place on campus in a face-to-face format, with the exception of MA-500 and MA-505, which are eight-week online courses. All required math courses will be held at 4 p.m. Elective courses may take place in the morning or other times of day.
COURSE REQUIREMENTS (30 credit hours)
|
Course |
Course Title |
Credits |
Term Offered |
| MA541 | REGRESSION MODELS AND APPLICATIONS + This course includes: linear models, including t-tests, ANOVA, regression, and multiple
regression. Residual analyses, transformations, goodness of fit, interaction and confounding.
Introduction to generalized linear models: mixed, hierarchical and repeated measures.
Binary regression, extensions to nominal and ordinal milticategory responses, count
data, Poisson and negative binomial regression, log-linear models. Prerequisites:
MA-341 and MA387, BI412 or BI507.
|
4 credit hours | FALL ONLY/EVEN YEARS |
| MA551 | INTRODUCTION TO PROBABILITY THEORY + This course covers probability spaces; combinatorial analysis; independence and conditional
probability; discrete and continuous random variables including binomial, Poisson,
exponential and normal distributions; expectations; joint, marginal and conditional
distribution functions; moment generating functions; law of large numbers; central
line theorems. Prerequisite: MA-205.
|
3 credit hours | FALL ONLY/EVEN YEARS |
| MA552 | INTRODUCTION TO MATHEMATICAL STATISTICS + This course covers the teaory and practical applications of the theory of sampling,
statistical inference, including sufficiency, estimation, and testing. Topics include
common statistical distributions, sampling, maximum likelihood and moment estimators,
unbiased estimators, hypothesis testing, and Bayesian inference. Prerequisites: MA-551
and instructor's consent.
|
3 credit hours | SPRING ONLY/ODD YEARS |
| MA564 | MULTIVARIATE ANALYSIS + An introduction to multivariate statistical analysis, such as Multivariate ANOVA,
Principal Component analysis, factor analysis, cluster analysis, discriminant analysis,
possibly structural equation modeling (SEM). Prerequisites: MA-541 and instructor's
consent.
|
3 credit hours | SPRING ONLY/ODD YEARS |
| MA571 | STATISTICAL RESEARCH AND CONSULTING + This course is designed to teach students the skills and techniques needed to conduct
statistical research and provide statistical consulting services. Students will learn
how to design studies, collect and analyze data, and communicate results effectively
to clients. Through campus-wide consulting program, students will work with researchers
from various disciplines providing recommendations for statistical methodologies appropriate
for their research: analyzing client data, preparing written reports and manuscripts.
|
1 - 3 credit hours | FALL/SPRING/ALL YEARS |
| MA581 | MACHINE LEARNING FOR DATA SCIENCE + This course focuses on the practical applications of machine learning techniques to
real-world problems. Students will gain knowledge on how to apply and evaluate different
machine learning algorithms, including linear models, k-means, support vector machines,
decision trees, random forests, neural networks, and more. They will also learn how
to analyze and manipulate real-world datatasets, design learning algorithms, train,
and assess machine learning models. Prerequisite: MA-541.
|
3 credit hours | FALL ONLY/ODD YEARS |
|
Course |
Course Title |
Credits |
Term Offered |
| AL505 | NUTRITIONAL EPIDEMIOLOGY + This is a 3-credit course that explores the complex relationships between diet and
the major diseases of Western civilization, such as cancer and atherosclerosis. Topics
that will be covered include: research strategies in nutritional epidemiology; methods
of dietary assessment (using data on food intake, biochemical indicators of diet,
and measures of body size and composition); reproducibility and validity of dietary
assessment methods; nutrition surveillance; and diet-disease associations. Prerequisites:
BI/EV507.
|
3 credit hours | SPRING ONLY/ODD YEARS |
| BA622 | STATISTICAL ANALYSIS AND ECONOMETRIC TECHNIQUES + The course begins with the basic concepts and methods of management science that relies
on statistical analysis techniques as well as the art of decision-making under circumstances
of constrained optimization. It introduces statistical ideas as they apply to managers.
Two ideas dominate: describing data and modeling variability and randomness using
probability models. The course provides tools and data analysis models for decision
making that use hypothesis testing, linear programming and simulation. It also provides
an understanding of the definitions and limitations of a variety of standard econometric
measures.
|
3 credit hours | SUMMER/ALL YEARS |
| EV558 | ADVANCED GEOSPATIAL METHODS + This course focuses on applications of geospatial technologies, including geographic
information systems (GIS), remote sensing, and the global positioning system (GPS).
It emphasizes applications of geospatial technologies to environment science and related
fields. Topics include geospatial data collection and processing, visualization, analysis,
and modeling; geospatial statistical analysis; mobile cloud based geospatial applications;
and integration of geospatial technologies. Students will gain an understanding of
Advanced Geospatial Techniques; demonstrate abilities to geospatial data collection,
processing, and analysis by the means of GIS, remote sensing and GPS; and be able
to solve practical problems in environmental science and related fields using geospatial
technologies. The course aims to equip students with understanding and experience
with the practical use of geospatial technologies in natural sciences, particularly
environmental science. Prerequisites: Recommended prerequisites for Environmental
Science Graduate Program, and fundamentals of GIS or equivalent, or consent of instructor.
Undergraduate students may enroll in the course with the permission of instructor.
|
4 credit hours | SPRING ONLY/ALL YEARS |
| MA505 | INTRODUCTION TO SAS + This course introduces students to basic knowledge in programming, data management,
and exploratory data analysis using SAS software. Topics covered include data import
and export, data cleaning and validation, basic statistical analysis, and data visualization.
|
1 credit hour | FALL ONLY/ALL YEARS |
| MA500 | INTRODUCTION TO R + This course will help build an understanding of the basic syntax and structure of
the R language for statistical analysis and graphics.
|
1 credit hour | FALL ONLY/ALL YEARS |
| MA510 | PYTHON FOR DATA SCIENCE + In this course, students will learn the basic syntax and data structures of the Python
language required for Data Science, and how to utilize Numpy and Pandas for data analysis
and statistical processing. Additionally, students will learn basic SQL for manipulating
data in databases.
|
2 credit hours | SPRING ONLY/ALL YEARS |
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 | Spring | Fall | Spring |
|---|---|---|---|
|
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: 9 | Total credits: 8 | Total credits: 4 |
GRADUATE CERTIFICATE REQUIREMENTS
The Data Science Graduate Certificate is a shorter, more focused program compared to the MS in Data Science. It provides students with key competencies in statistical modeling, machine learning, and data visualization while maintaining a strong emphasis on ethical data use and communication. The program consists of 15 credit hours, covering fundamental and applied aspects of data science. Courses will be offered in a face-to-face format with practical applications in real-world data analysis. All courses are scheduled at 4pm or later to accommodate working individuals.
COURSE REQUIREMENTS (15 credit hours)
|
Course |
Course Title |
Credits |
Term Offered |
| MA541 | REGRESSION MODELS AND APPLICATIONS + This course includes: linear models, including t-tests, ANOVA, regression, and multiple
regression. Residual analyses, transformations, goodness of fit, interaction and confounding.
Introduction to generalized linear models: mixed, hierarchical and repeated measures.
Binary regression, extensions to nominal and ordinal milticategory responses, count
data, Poisson and negative binomial regression, log-linear models. Prerequisites:
MA-341 and MA387, BI412 or BI507.
|
4 credit hours | FALL ONLY/EVEN YEARS |
| MA564 | MULTIVARIATE ANALYSIS + An introduction to multivariate statistical analysis, such as Multivariate ANOVA,
Principal Component analysis, factor analysis, cluster analysis, discriminant analysis,
possibly structural equation modeling (SEM). Prerequisites: MA-541 and instructor's
consent.
|
3 credit hours | SPRING ONLY/ODD YEARS |
|
Course |
Course Title |
Credits |
Term Offered |
| MA500 | INTRODUCTION TO R + This course will help build an understanding of the basic syntax and structure of
the R language for statistical analysis and graphics.
|
1 credit hour | FALL ONLY/ALL YEARS |
| MA505 | INTRODUCTION TO SAS + This course introduces students to basic knowledge in programming, data management,
and exploratory data analysis using SAS software. Topics covered include data import
and export, data cleaning and validation, basic statistical analysis, and data visualization.
|
1 credit hour | FALL ONLY/ALL YEARS |
| MA510 | PYTHON FOR DATA SCIENCE + In this course, students will learn the basic syntax and data structures of the Python
language required for Data Science, and how to utilize Numpy and Pandas for data analysis
and statistical processing. Additionally, students will learn basic SQL for manipulating
data in databases.
|
2 credit hours | SPRING ONLY/ALL YEARS |
| MA551 | INTRODUCTION TO PROBABILITY THEORY + This course covers probability spaces; combinatorial analysis; independence and conditional
probability; discrete and continuous random variables including binomial, Poisson,
exponential and normal distributions; expectations; joint, marginal and conditional
distribution functions; moment generating functions; law of large numbers; central
line theorems. Prerequisite: MA-205.
|
3 credit hours | FALL ONLY/EVEN YEARS |
| MA552 | INTRODUCTION TO MATHEMATICAL STATISTICS + This course covers the teaory and practical applications of the theory of sampling,
statistical inference, including sufficiency, estimation, and testing. Topics include
common statistical distributions, sampling, maximum likelihood and moment estimators,
unbiased estimators, hypothesis testing, and Bayesian inference. Prerequisites: MA-551
and instructor's consent.
|
3 credit hours | SPRING ONLY/ODD YEARS |
| MA571 | STATISTICAL RESEARCH AND CONSULTING + This course is designed to teach students the skills and techniques needed to conduct
statistical research and provide statistical consulting services. Students will learn
how to design studies, collect and analyze data, and communicate results effectively
to clients. Through campus-wide consulting program, students will work with researchers
from various disciplines providing recommendations for statistical methodologies appropriate
for their research: analyzing client data, preparing written reports and manuscripts.
|
1 - 3 credit hours | FALL/SPRING/ALL YEARS |
| MA581 | MACHINE LEARNING FOR DATA SCIENCE + This course focuses on the practical applications of machine learning techniques to
real-world problems. Students will gain knowledge on how to apply and evaluate different
machine learning algorithms, including linear models, k-means, support vector machines,
decision trees, random forests, neural networks, and more. They will also learn how
to analyze and manipulate real-world datatasets, design learning algorithms, train,
and assess machine learning models. Prerequisite: MA-541.
|
3 credit hours | FALL ONLY/ODD YEARS |
Students can choose other data science related courses with the consent of the program.
SAMPLE SCHEDULE FOR COVERING REQUIREMENTS
Below is a sample schedule of the certificate program across three semesters (12 months), offering flexibility in personalizing your educational journey:
| Fall | Spring | Fall |
|---|---|---|
|
MA-541 (4 credits) MA-500 (1 credit) MA-505 (1 credit) |
MA-510 (2 credits) MA-564 (3 credits)
|
MA-581 (3 credits) MA-571 (1 credit)
|
| Total credits: 6 | Total credits: 5 | Total credits: 4 |
Program Chair
Faculty
Aquino J. Camacho Leslie
Office Location: Expertise