Data Science for Biology and Chemistry
Graduate Certificate
About this program
The Graduate Certificate in Data Science for Biology and Chemistry gives you practical computational and data analysis skills tailored to life sciences. Over 12 units, you complete four core courses — including coding, data science methods, a support course, and professional development — and apply your growth through a research course in your master’s work. You also choose one discipline-specific elective to strengthen your skills in biology or chemistry analysis. This certificate integrates smoothly into your M.S. studies.
Program Curriculum and Requirements
Program Scope
Modern biological and chemical sciences increasingly requires strong computational skills to conduct cutting-edge research. The Data Science for Biology and Chemistry Certificate will enable students to learn computational and programming skills that are relevant to their work in biology and chemistry. This certificate program is a 2-year, 12- unit certificate program that students can complete in line with their Master’s degree program requirements. There are 4 core required courses and 1 elective. Of the 4 core courses, one 3+ unit classes is devoted to learning data and computer science, using assignments and examples drawn from biology and chemistry. One one-unit course is a support courses that provide inclusive environments for students to master coding and data science skills. One one-unit course is a professional development course to connect students to opportunities in basic data science with professionals in academics and industry. There is also 3 units required for research, in which students utilize their computational skills in their research projects required for their MS degrees in Biology and Chemistry. Students also select one elective course (3-4 units) that uses computational analysis.
Program Learning Outcomes
- Develop computational skills needed to create, debug, and run a computer program to perform data analyses.
- Obtain, store, manage and share data in a distributed environment through practical, hands-on experience with programming languages and big data tools.
- Evaluate data, as well as to apply key technologies data science analysis including statistical analysis, machine learning, and data visualizations.
- Develop data science as an aspect of their professional identity, effectively communicate their data analyses and results, and will connect with professionals outside of the University to further their data science careers.
- Apply their data science skills to discipline-specific data and questions to solve real-world problems of high complexity.