Research Methods

This course is a topical survey of research methodologies that in the Spring 2017 term focused on computing and programming methods broadly applicable for graduate students who are engaged in doctoral level research in the physical sciences and engineering. With the objective of providing useful tools and a perspective on how advanced scientific research is conducted, we will cover three topics in roughly 5 week sequential segments:

• Python and javascript for data analysis, instrument control, and image processing (Kielkopf)
• Advanced Python (Freelon)
• Root (Banerjee)

Some on-line course materials are here:

• Syllabus
• Programming with Python for data analysis, modeling, and instrument control (Kielkopf)

This is a required course for students in the doctoral program in Physics & Astronomy.

Useful On-Line Resources

• Writing with LaTeX
• NASA Astrophysics Data System Abstract Service (ADS)
• New astrophysics papers on arxiv.org's astro-ph
• SIMBAD astronomical database
• Coordinated universal time (UTC)
• Space Telescope Science Institute
• Mikluski Archive for Space Telescopes (MAST)
• Sloan Digital Sky Survey

Python External Sites

• Python official website
• Python tutorial
• Python language reference
• Numpy
• Scipy
• Matplotlib
• Pyfits
• Astropy
• Scikits - Scipy toolkits
• Enthought academic Python distribution

Web Application Programming

• JavaScript tutorial
• Threejs.org