The need to work with and understand data is skyrocketing, as is the demand for trained professionals in data science. Employment of computer and information research scientists is projected to grow 22% from 2020 to 2030, and the median pay for computer and information research scientists in 2021 was $131,490, according to the U.S. Bureau of Labor Statistics.
A new University of Hawaiʻi at Mānoa data science certificate program will equip students and industry professionals with the skills and knowledge they need for this high-demand industry and lucrative careers.
The programming intensive certificate will be offered starting in the fall 2022 semester by the Department of Information and Computer Sciences (ICS) in collaboration with the Hawaiʻi Data Science Institute and other UH Mānoa data-intensive departments. It is open to undergraduate students in any department and working professionals.
“In the ‘80s, computer literacy was predicted to be crucial to the 20th century workforce. In the past few years, the new required literacy has become data literacy. You need it to stay competitive in just about every profession—everything from running an electric grid, to designing a new car, discovering new cures for diseases, predicting the spread of viruses, to selling products on the Internet,” said Jason Leigh, ICS professor and co-director of the Hawaiʻi Data Science Institute.
Students will receive training in modern computational tools for manipulating, visualizing and extracting insights from data. Courses include machine learning methods, data visualization, data science and machine learning fundamentals.
“Technology for artificial intelligence, machine learning and visualization is advancing rapidly, and impacting every industry,” said Peter Sadowski, an assistant professor in ICS who teaches courses in machine learning. “This program enables students from a variety of backgrounds to participate in the courses which cover the latest techniques and tools.”
Elective courses available through the program allow for domain specific knowledge such as climate modeling, data analysis and applications, bioinformatics, computational physics and more.
—By Maria Dumanlang