ASTR3800 Introduction to Scientific Data Analysis and Computing
Introduction to Scientific Data Analysis and Computing.
Covers analytical and numerical techniques used in scientific data analysis, including errors of measurement and the propagation of error, data distributions, functional fitting, sources of noise in instruments and data, spectral analysis, image processing, and testing theoretical compliance. The computer work is done in Python, but this is not a programming course, it is a data analysis course.
The overall goals of the course are:
- Make you “smart” about data. Understand the errors associated with it, how you can process “raw” data and when you should or should not believe derived results.
- Train you to check your own work. Do the units and order of magnitude make sense?
- Prepare you to write and use simple computer programs to analyze and understand data.
- Prepare you to serve as a research assistant to a CU faculty member, or for other jobs that involve data analysis.
Class meets: Tue. 3:30-5:20, Thurs. 3:30-5:20 in the computer lab at Sommers Bausch Observatory.
Classes will open with lecture and discussion, and continue with practice using the cosmos lab computers. All the cosmos machines have the programming language Python installed. The class will use real, interesting data whenever possible.
Required Text: Taylor, An Introduction to Error Analysis, 2nd. Ed., by retired CU prof. John Taylor.
Another recommend text, slightly more advanced but very useful, is Bevington and Robinson, Data Reduction and Error Analysis for the Physical Sciences.
Grading: 15% midterm, 25% final, 20% homework plus in-class exercises, 5% “Stellar Parallax,” 10% “Orrery Extra-Solar Planets,” and 25% a final project chosen by you. The final project will be a data analysis project written in the form of a scientific paper.
The nature of the class and the in-class exercises make it difficult to succeed if you don’t attend all or almost all of the classes. There are no “make-up” points.