Catalog 2024–2025

Data Science

Program Coordinator: Travis Mandel, Ph.D.
Email: tmandel@hawaii.edu

Website: datasci.uhh.hawaii.edu

Associate Professors:

Jr. Specialist:

Data science is the study of how to collect, process, store, analyze, and visualize data to solve real-world problems. Students who study data science will be well-equipped to to tackle problems in any number of domains, utilizing the power of data to drive decisions and instigate change, whether it be better understanding the world around us, attacking climate change, or making better business decisions. The study of data science involves growing three skillsets: computational skills, statistical skills, and domain expertise. Our major offers in-depth exploration of each of these components: on the computational side, students receive training in programming, machine learning, processing various kinds of data (images, text, etc.). On the statistical side, students receive training that goes beyond basic statistical analysis to include more advanced techniques such as matrix algebra and Bayesian statistics. Finally, students receive training in a domain of their choice - the program currently offers four tracks (Business, Astronomy, Computational, and Statistical) to give students more in-depth training in a specific area. Data scientists are in high demand on the job market and this is the perfect major for someone who wants to discover new ways to solve challenging problems in science, business, and tech.

The data science program at UH Hilo is designed to be an interdisciplinary major which teaches students the skills needed to collect, process, store, analyze, and visualize data to drive real-world decisions. The core courses in our Data Science major teach students the computational and statistical principles needed to effectively work with data of various kinds (including images, text, tabular data, etc.) Our proposed major contains four tracks (Business, Astronomy, Computational, and Statistical) in which students receive more in-depth training needed to tackle a data from a certain domain. We plan to add more tracks to the major over time as student interest increases. At just over 50 credits, Data Science is designed to be an excellent double major option as well as being attractive to transfer students and technically-minded students as a single major.

Program Objectives

  • To provide students with practical hands-on training and experience in using data science tools to solve real-world problems. Students will learn how to acquire, archive, analyze and report on data with a variety of techniques encompassing statistical, machine learning, and other approaches.
  • To grow a 21st century workforce equipped with data science skills in Hawaii and beyond. There is a growing need for these skills in almost every sector, but most pressingly in business and the natural sciences.
  • To provide students with a deeper understanding of the mathematical and computer science foundations of data science, including probability, statistics, computer vision, machine learning, and artificial intelligence.
  • To create a cross-disciplinary environment in which students learn to apply their data science skills in a variety of domains to nurture academic collaboration and growth.

Program Learning Outcomes

Students who complete a data science major will be able to:

  1. Explain the mathematical foundations of data science, including probabilistic reasoning as well as Bayesian and Frequentist statistics, and utilize these techniques to solve data science problems.
  2. Independently create computer programs which analyze complicated real-world datasets, as well as explain and modify data science programs written by others.
  3. Identify and apply machine learning and artificial intelligence techniques to effectively solve real-world problems; describe how these techniques work on a technical level.
  4. Identify the optimal data science tools required for various analytical procedures and data visualization tasks, and apply them effectively to accomplish these tasks.
  5. Describe the appropriate usage and limits of data science, e.g. explain what kinds of questions can be asked and answered versus those that cannot be addressed.
  6. Manipulate a wide variety of common data types to effectively accomplish an objective: e.g., traditional experiments, image collections, natural language text, and real-time time-series processing.
  7. Articulate privacy and security and ethical issues surrounding data of various types, and describe approaches to mitigate these issues
  8. Retrieve and store data in a variety of different formats, including databases and popular file formats.
  9. Apply and integrate knowledge from numerous disciplines, including those outside of mathematics and computer science such as business, natural sciences, health, and social sciences.
  10. Utilize existing data science and informatics software effectively.
  11. Communicate insights from large datasets to others, in written, oral, and visual form

Curricula