Teaching Data Science

We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general programming concepts by using the Python programming language with an emphasis on data preparation, processing, and presentation. The course had no prerequisites, and students were not expected to have any programming experience. This introductory course was designed to cover a wide range of topics, from the nature of data, to storage, to visualization, to probability and statistical analysis, to cloud and high performance computing, without becoming overly focused on any one subject. We conclude this article with a discussion of lessons learned and our plans to develop new data science courses.Comment: 10 pages, 4 figures, International Conference on Computational Science (ICCS 2016

Evolution in the Clustering of Galaxies for Z < 1

Measuring the evolution in the clustering of galaxies over a large redshift range is a challenging problem. For a two-dimensional galaxy catalog, however, we can measure the galaxy-galaxy angular correlation function which provides information on the density distribution of galaxies. By utilizing photometric redshifts, we can measure the angular correlation function in redshift shells (Brunner 1997, Connolly et al. 1998) which minimizes the galaxy projection effect, and allows for a measurement of the evolution in the correlation strength with redshift. In this proceedings, we present some preliminary results which extend our previous work using more accurate photometric redshifts, and also incorporate absolute magnitudes, so that we can measure the evolution of clustering with either redshift or intrinsic luminosity.Comment: 6 pages, 6 figures requires paspconf.sty. To be published in "Photometric Redshifts and High Redshift Galaxies", eds. R. Weymann, L. Storrie-Lombardi, M. Sawicki & R. Brunner, (San Francisco: ASP Conference Series