Fall | Graduate | 9 Units | Prereq: None
This course is intended for students with science and engineering backgrounds, who actively conduct research on hardtech problems and do not currently possess — yet are motivated to develop — a strong background in data science. Students will develop competency in applying advanced statistics and machine-learning tools to common classes of problems encountered during hardtech development. The class will consist of three parts: (i) applied data-science fundamentals, including data curation, data visualization, statistics, machine learning, and an overview of software engineering; (ii) relevant statistics and machine-learning tools; and (iii) application examples to materials, systems, and complex systems. Students will immediately apply this toolset in their research, first through class deliverables (hands-on activities, PSets, and project), and eventually in their daily research. The prerequisite for this course is moderate to strong technical domain expertise in one or more science and/or engineering disciplines.