Welcome to the course website for GE/AY 117, Winter 2018.  This website contains readings and problem sets for each week, as well as the course syllabus.

Textbook: Data Analysis: A Bayesian Tutorial by D. S. Sivia (2nd edition).  This book will be on reserve in the geology library and is available online here (if you are connected to the Caltech network).  Note:  the current link has limited downloads, we are working on getting an unlimited link and will post an update soon.  Other helpful resources available at the library include Bayesian Data Analysis (3rd edition) by Gelman et al. and Bayesian Logical Data Analysis for the Physical Sciences by Phil Gregory.

In modern astronomy and planetary science, vast quantities of data are often available to researchers. The challenge is converting this information into meaningful knowledge about the universe. The primary focus of this course is the development of a broad and general tool set that can be applied to the student's own research.  As such, it is important that students taking this class have at least a basic background in programming in a language such as Python, IDL, Matlab, C/C++, Fortran, etc.  Ideally, you will already have a research problem and accompanying data set in mind that you would like to tackle using the methods developed in this class, but “canned” projects will also be available as additional options.  We will use case studies from the astrophysical and planetary science literature as our guide as we learn about common pitfalls, explore strategies for data analysis, understand how to select the best model for the task at hand, and learn the importance of properly quantifying and reporting the level of confidence in one's conclusions.  Students will also practice scientific communication by producing a conference-style poster and accompanying methods write-up describing the results of their final project.