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)