About This Course
Pandas Crash Course Pre-Work: Introduces the core Python tools needed for the virtual Pandas training sessions, including the IPython notebook, Python language essentials (data structures, control flow, and code organization), and array computing with NumPy. The content has been curated through many years of experience with thousands of students over the past decade, and is presented by some of the most respected scientific software developers in the Python language today.
Programming experience in some language (such as R, MATLAB, SAS, Mathematica, Java, C, C++, VB, FORTRAN, or similar) is recommended. In particular, participants should be comfortable with general programming concepts like variables, loops, and functions. Experience with Python is helpful (but not required).
Corran obtained his B.S. from the University of New South Wales and his Ph.D. in pure mathematics from UCLA. He has held teaching positions at the University of Nevada, Las Vegas as well as Texas A & M. His academic areas of concentration included functional analysis and operator algebras. As Chief Scientist at Compudigm International, Corran worked on enterprise data visualization and redictive modeling using self-organizing maps. Corran has been programming in Python since 1995, when he was a teaching assistant in UCLA's Program in Computing courses.
With experience in complex system modeling and scientific computing, Jonathan has contributed to Enthought's fluid dynamics applications as well as course materials and training tools. Prior to working at Enthought, he was an instructor and research assistant in particle physics and astrophysics at the University of Texas and Brussels University. Jonathan holds a M.S. in physics and a Ph.D. in particle physics and cosmology from the University of Paris, France.
Eric has a broad background in engineering and software development and leads Enthought's product engineering and software design. Prior to co-founding Enthought, Eric worked in the fields of numerical electromagnetics and genetic optimization in the Department of Electrical Engineering at Duke University. He has taught numerous courses about Python and how to use it for scientific computing. He also serves as a member of the Python Software Foundation. Eric holds M.S. and Ph.D. degrees from Duke University in electrical engineering and a B.S.E. in mechanical engineering from Baylor University.