DAY 1:
Introduction:
35 min : Scientific computing in python (standard overhead talk)
45 min: The core tools -- ipython, numpy, matplotlib and scipy. (type along)
Break: 15 min
Exercises session 1:
45 min: Working with data files, web based resources, date handling,
CSV files, and record arrays. Word counting exercise.
(urllib, csv, dateutils, matplotlib.mlab)
45 min: Numerical integration, trapz and Newton's quadrature (scipy.integrate)
Lunch Break: 45 min
Exercises session 2:
45 min: Linear algebra: Moire Glass patterns
45 min: Statisical distributions, random numbers, central limit theorem (scipy.stats)
45 min: Descriptive statistics and graphs: mean, variance, skew,
kurtosis, histograms, autocorrelation, power spectra,
spectrogram (scipy.stats, matplotlib.mlab and pylab)
Break: 15 min
Exercises session 3:
60 min: Interpolation, data modeling and optimization (scipy.interpolate and scipy.optimize)
45 min: Using code from other languages (FORTRAN, C, C++) --
Presentation (pyrex, weave, f2py, ctypes)
DAY 2:
Exercise Session 4:
45 minutes: screen scraping - extracting data from web pages (BeautifulSoup)
