Research Methods: Difference between revisions

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This course is a topical survey of research methodologies that in the Spring 2015 term was drawn primarily from optical physics, but are 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:
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 for data analysis, instrument control, and image processing (Kielkopf)
* Python and javascript for data analysis, instrument control, and image processing (Kielkopf)
* Lasers (Mendes)
* Advanced Python (Freelon)
* Ultrafast optics (Smadici)
* Root (Banerjee)


Course materials are here:
Some on-line course materials are here:


* [http://prancer.physics.louisville.edu/classes/650/syllabus/p650_sp15.pdf Syllabus]
* [http://prancer.physics.louisville.edu/classes/650/syllabus/p650_sp17.pdf Syllabus]
* [http://prancer.physics.louisville.edu/astrowiki/index.php/Python_for_Physics_and_Astronomy Programming with Python for data analysis, modeling, and instrument control (Kielkopf)]
* [http://prancer.physics.louisville.edu/astrowiki/index.php/Python_for_Physics_and_Astronomy Programming with Python for data analysis, modeling, and instrument control (Kielkopf)]
* [http://prancer.physics.louisville.edu/homework/index.html Upload Python homework]




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*[http://matplotlib.org/ Matplotlib]
*[http://matplotlib.org/ Matplotlib]
*[http://www.stsci.edu/institute/software_hardware/pyfits Pyfits]
*[http://www.stsci.edu/institute/software_hardware/pyfits Pyfits]
*[http://www.astropy.org/ Astropy]
*[http://scikits.appspot.com/scikits Scikits - Scipy toolkits]
*[http://scikits.appspot.com/scikits Scikits - Scipy toolkits]
*[http://www.enthought.com/products/edudownload.php Enthought academic Python distribution]
*[https://www.enthought.com/academic-subscriptions/ Enthought academic Python distribution]




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*[http://www.w3schools.com/js/default.asp JavaScript tutorial]
*[http://www.w3schools.com/js/default.asp JavaScript tutorial]
*[http://www.dartlang.org/  Dart from Google]
*[https://threejs.org/ Threejs.org]

Latest revision as of 04:23, 12 August 2017

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:


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


Useful On-Line Resources

Python External Sites


Web Application Programming

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