Karen Leighly · SM 20
A Portfolio of Data Analytics Classes at University of Oklahoma
We present the design and initial implementation of two data-driven Astronomy courses at the University of Oklahoma. Both courses focus on data analytics skills acquisition using the Python coding language and Jupyter notebooks, and are aimed at providing students with experiences and skills for a wide range of careers. Astronomy 3190 is a elective course for sophomores. The emphasis is on repeated exposure to the data life cycle: data wrangling, visualization, statistical thinking, modeling, computational thinking, and communication skills. Astronomy 5900 is a graduate course taken by upper-division undergraduates and graduate students. The course provides an ambitious romp through statistical inference, Markov Chain Monte Carlo, cluster analysis, regression, principal components analysis, classification, and time series. Both courses use freely available astronomical data from various sources including the Sloan Digital Sky Survey. A discussion of the learning goals and examples of specific activities will be included.