1,700 research outputs found
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
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