The Online Master of Science in Data Science from Hawai‘i Pacific UniversityThe Online Master of Science in Data Science from Hawai‘i Pacific UniversityThe Online Master of Science in Data Science from Hawai‘i Pacific University

The online MS in Data Science program from Hawai‘i Pacific University will prepare you to handle unconventional data, strengthen creative problem-solving skills, and apply cutting-edge data analysis expertise to nearly every industry. 

Through online classes led by expert faculty, you will explore both foundational data analysis techniques as well as emerging applications of data collection and interpretation. The curriculum is designed to bring you into the modern era of data analysis by going beyond traditional data science approaches. That means covering topics such as AI, machine learning, big data analytics, GPU-based high-performance computing, cloud computing, and more. 

You’ll graduate prepared to showcase your skills to future employers and become a responsible data leader in your field. 

Expert Faculty

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Our faculty are up to date on in-demand topics in the field, such as data ethics, machine learning, and artificial intelligence.

12-Month Program

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Jumpstart your data science career by graduating in as few as 12 months.

No GRE/GMAT Required

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Our holistic approach to reviewing applications means we value more than just test scores. 

Prerequisite Courses Available

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Brush up on key programming and math subjects in optional prerequisite courses.

Applicants to the online MS in Data Science program must have earned a bachelor’s from a regionally accredited institution. 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.

In addition, prerequisite courses enable admitted students to gain additional knowledge and skills they can use to start their degree. These courses include Introduction to Statistics, Programming Language/Introduction to Computing, and Linear Algebra. The pre-requisites are highly recommended but not required. Applicants will be evaluated in a holistic approach. 

The curriculum consists of 10 courses including a capstone worth a total of 30 credits. The online MS in Data Science curriculum combines traditional data analysis with computer programming to ensure that graduates are equipped with the skills required of them by the evolving landscape of the industry. 

Topics include AI, machine learning, big data analytics, high-performance computing, and cloud computing. The skills acquired during the program can open doors to diverse and lucrative employment opportunities in finance, technology, and sciences.

The ten required core courses include:

• Applied Statistics and Data Science (3 credits)

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  This course offers a comprehensive overview of three distinct yet interconnected perspectives: classical statistics, Bayesian statistics, and data science/machine learning (DSML). Classical statistics emphasizes rigorous inferences rooted in the frequentist school whereas the Bayesian school offers a probabilistic framework that enables the incorporation of prior knowledge, updating beliefs, and modeling uncertainty. DSML aims to extract insights and patterns from data and build predictive models.

• Data Science and Machine Learning (3 credits)

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  This course provides an overview of modern data science and machine learning techniques, contrasting them with a traditional statistical approach. Students will learn how analysts can transition from classical statistics to more advanced predictive modeling and algorithmic data analysis. The course will cover both the theoretical and applied aspects of power DSML tools, such as neural networks, support vector machine, decision tree, random forest, gradient boost, XGBoost, model selection, model averaging, cluster analysis, and text mining. Upon completing this course, students will understand how to leverage modern modeling techniques to extract insights, predict outcomes, and optimize decisions.

• Programming for Data Scientists (Python) (3 credits)

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  An introduction to programming in the popular Python programming language. Topics include data types, simple statements, control structures, strings, functions, recursion, the Python interpreter, system command lines and files, module imports, object types, dynamic typing, scope, classes, operator overloading, exceptions, testing, and debugging. The course will enable students to program fluently in Python and move on to advanced topics such as programming collective intelligence and natural language processing.

• Data Visualization (3 credits)

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  This course covers principles and tools for effectively visualizing and communicating data-driven insights. Aligned with John Tukey’s exploratory data analysis paradigm, emphasis will be placed on using visualizations to ask and answer “what-if” questions about data. Topics of this course include, but are not limited to, univariate data visualization, high-dimensional data visualization, visualization for trend-based data, visualization for spatial data, and dashboarding. Through hands-on assignments, students will gain skills in creating insightful, impactful data graphics using leading dynamic visualization tools. The focus will be on extracting and communicating patterns from data through interactivity and synthesis of complex information.

• Data Wrangling with SQL (3 credits)

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  This hands-on course will provide students with the skills to wrangle, clean, transform, and munge data using structured query language (SQL). Students will learn SQL programming techniques to deal with common data issues such as missing values, duplicate records, parsing errors, inconsistent formats, and integrating from different sources.

• Text Mining and Unstructured Data (3 credits)

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  This course introduces techniques for extracting insights from unstructured textual, visual, audio, and video data. Students will learn text mining tools to analyze patterns in textual corpora, as well as acquire skills for organizing and making sense of other unstructured data types. Topics include, but are not limited to, text mining algorithms like classification, clustering, and sentiment analysis, Web scraping and collection of online text data, audio and video feature extraction techniques, as well as image classification and object recognition. Through hands-on assignments and projects, students will gain practical experience applying text mining, computer vision, and other unstructured data analysis techniques on real-world datasets.

• Cloud Computing Platforms, Applications, and Data Security (3 credits)

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  This course provides students with an overview of vendor-independent cloud computing technology concepts and methods. Several cloud providers along with their tools will be referenced. Students will learn specifics about software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS), server and desktop virtualization and more. Specific topics include cloud-related security risks and threats, cloud architecture and design, and operations and support.

• AI and Machine Learning (3 credits)

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  This course provides a broad overview of the fields of artificial intelligence and machine learning. Students will learn fundamental concepts and algorithms that enable computers to mimic human intelligence for tasks like pattern recognition, prediction, optimization, and decision-making. Topics in this course include, but are not limited to, supervised learning algorithms, unsupervised learning algorithms, reinforcement learning for sequential decision-making, deep learning using multiple hidden layers, natural language processing for text and speech, computer vision for image and video processing, generative AI (e.g., ChatGPT, Midjourney, Stable Diffusion etc.). In this course, students will gain hands-on experience applying AI techniques and machine learning algorithms to build intelligent systems. Programming will be done in languages like Python.

• Data Ethics (3 credits)

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  This course provides an overview of ethical issues related to data, with a particular emphasis on artificial intelligence, machine learning, and big data. Students will gain an understanding of current debates, frameworks, and regulations regarding data ethics. Key topics include privacy and confidentiality, transparency and explainability, bias and fairness, copyright and intellectual properties, as well as safety and misuse prevention.

• Capstone Project (3 credits)

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  This capstone course provides the culminating experience for students in the MS in Data Science program. Soft skills such as effective communication are indispensable, and therefore teamwork is recommended over individual projects. Working individually or in a team, students will conceptualize, propose, and execute an end-to-end data science project using real-world big data. The project will integrate skills and concepts learned throughout the program, including statistical analysis, machine learning, and communication of results. Under instructor’s guidance, students will identify a problem amenable to data science techniques, acquire appropriate datasets, perform exploratory data analysis, implement data cleaning, and feature engineering pipelines, train machine learning models, and measure model performance. The final project must be approved by a committee consisting of at least two of the MSDS faculty. Students are encouraged to submit the product to a data science conference or a peer-reviewed journal.

The capstone project requires students to integrate all they learned about data science during the program. To complete their capstone project, students have to showcase their knowledge of key data science skills such as statistical modeling, programming, and data visualization. In other words, the capstone ensures and serves as proof that students have a strong understanding of data science. 

Apply Data Science Expertise to Your Field

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In the online MS in Data Science program, you’ll find an intuitive platform, comprehensive support, and top-notch education designed for real people with real lives.

Hawai‘i Pacific University faculty are compassionate, engaged, and dedicated to empowering students throughout their education and professional career journey. They offer high quality and personalized instruction. They support all students, from the first class to long after graduation. 

The online MS in Data Science faculty bring years of experience to the classroom and are up to date on in-demand topics in the field such as data ethics, machine learning, and artificial intelligence. 

In the online MS in Data Science program you will gain the analytical and technical skills you need to pursue a wide range of careers not only in technology, but across many industries such as business, energy, finance, health care, and marketing. You can also apply your data analysis skills cross functionally in fields such as marine and environmental science.

Nearly every field has a need for highly skilled professionals with data analysis expertise. Plus, professionals with bachelor’s degrees in unrelated fields may find that adding data analysis to their portfolio can increase their job opportunities in their respective field.

Careers that the online MS in Data Science program can prepare you to pursue include:

Strengthen your data science résumé and stand out to future employers. Request information about our online Master of Science in Data Science today.