Hawai‘i Pacific University Master of Science in Data Science online
The online MS in Data Science program from Hawai‘i Pacific University prepares students to interpret both conventional and unconventional data, strengthen their creative problem-solving skills, and apply cutting-edge data analysis expertise to nearly every industry. Students are brought into the modern era of data analytics through a curriculum that examines AI and machine learning, big data analytics, high-performance computing, cloud computing, and more.
About the program
- Cutting-edge curriculum: Learn key skills such as data analysis, data mining, data visualization, programming, machine learning, and more.
- Expert faculty: Learn from instructors who have years of experience and are up to date on in-demand topics such as data ethics, machine learning, and artificial intelligence.
- Project-based capstone: Showcase knowledge of key data science skills such as statistical modeling, programming, and data visualization.
About Hawai‘i Pacific University
Founded in 1965, Hawai‘i Pacific University is an international learning community located in downtown Honolulu, Hawai‘i. People from all 50 U.S. states, U.S. territories, and over 60 countries become students here — in-person and online — and experience what makes it so unique: its strong academic programs, small class sizes, compassionate mentorship, and diverse faculty and student population.
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Curriculum
The curriculum combines traditional data analysis with computer programming to ensure that graduates are equipped with the skills they will need to thrive in an evolving industry. Topics include AI/machine learning, big data analytics, high-performance computing, and cloud computing. The skills students acquire during the program can help open doors to diverse and lucrative employment opportunities in finance, technology, and sciences.
- 30 total credits: The curriculum consists of 10 courses, and a capstone designed to strengthen your data science portfolio.
- Optional prerequisite courses: Review foundational skills in statistics, programming, computing, and linear algebra. These prerequisites are highly recommended.
- Evolving technologies: Distinguish learning problems, select machine learning and deep learning models, and implement a training algorithm.
- Ethical considerations: Apply a framework to evaluate ethical issues in artificial intelligence and data science.
Admissions
Applicants to Hawai‘i Pacific University’s MS in Data Science program must have earned a bachelor’s from a regionally accredited institution, and an undergraduate GPA of at least 3.0 in the last 60 credits is strongly preferred. Applicants with a bachelor’s degree in math, computer science, engineering, business, social science, natural sciences — or another discipline that requires quantitative skills — are strongly preferred. However, applicants from all education and professional backgrounds are welcome to apply.
There are three cohort start dates per year (January, May, September). The following materials are required for all applications to the online MS in Data Science:
Online application
Transcripts
Résumé
Optional materials that can be provided if you believe they will strengthen your application include:
Letters of recommendation (optional)
Essay (optional)
Support every step of the way
Hawai‘i Pacific University offers a variety of academic and career support resources for students as they jumpstart or pivot their careers. In addition, Hawai‘i Pacific University’s passionate graduates and their alumni network can help current students and graduates alike connect and engage with professionals across nearly every industry. Hawai‘i Pacific University supports members of the military and their families throughout their academic and professional journey through a variety of financial and career resources.
The faculty at Hawai‘i Pacific University are also committed to supporting students from undergraduate programs who are eager to apply data science skills in their field. For example, students with a background in marine science can use new insights from climate data, bioinformatics, epidemiology, and more to expand their impact in the field.