TIME & PLACE: Tu,Th 3:30-5:00 SBO

INSTRUCTOR: Webster Cash   Duane D245

TA: tbd

HOURS: Before and after class or by appointment. At SBO so we have computer access.


Think Stats,

Python for Everyone, 
Horstmann & Necaise
Statistics, Data Mining and Machine Learning in Astronomy, 
Ivezic, Connolly, VanderPlas & Gray
An Introduction to Error Analysis,
Data Reduction and Error Analysis for the Physical Sciences,
Bevington and Robinson

EXAMS: No Exams.

HOMEWORK: There will be homework assignments on a regular basis. They will be graded and will count toward your course grade. Doing these problems is the core of the course. By working through the problems you will become facile with applications programming.

PROJECT: Several weeks into the course, after you have a basic idea of what Python can do, you will need to choose some kind of project. The project will involve writing a system of linked Python  procedures that will be useful in some context. For example: a statistical analysis package, or a photo image analysis system, or an astronomy simulation system. The project will be due after Spring Break and no later than the last day of class.

COURSE GRADE: Your grade will be based upon the homeworks grades and the project grade in roughly equal proportion. Precise balance will be announced mid-semester.

PREPARATION: Freshman level physics and integral calculus will prove very helpful but are not specifically required.  It is expected that you have basic computer programming skills.  It is advantageous to have Python experience, but not necessary.


This year we are, for the first time, assuming knowledge of programming.  That means we will be able to increase the sophistication of the statistics presented. Taylor was used in the past, but it is a little to simplified.  We will be trying "Think Stats" this year.  The Optional texts can be very helpful but you may not need them at the same level.