TIME & PLACE: Monday, Wednesday 3:30-5:00 SBO

INSTRUCTOR: Webster Cash   Duane D245


HOURS: Before and after class or by appointment. 


TEXTS  (Highly Recommended, not required):
Statistics and Analysis of Scientific Data, 
Massimiliano Bonamente

An Introduction to Error Analysis, John Taylor

Python for Everyone, 
Horstmann & Necaise
Statistics, Data Mining and Machine Learning in Astronomy, 
Ivezic, Connolly, VanderPlas & Gray
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: Before Spring Break 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. It is assumed that you have taken ASTR2600 or have equivalent skills.

COMPUTING:  Access to the workstations and Unix-based computers of the Observatory is provided.  However, most students prefer to install Python (for free) on their laptops and work there.  Help will be provided to get that going during the first week of class if needed.