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PyCamp Syllabus

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The outline of PyCamp. The organization of topics in which you will step into Python programming. This organization is unique compared to other course outlines. Rather than learning the entire syntax of the Python language before applying it to problem solving, you will lean the Pythonic way of programming by examining solutions to common programming problems.

Contents

DAY ONE: Get Up to Speed
DAY TWO: Service Oriented Architecture
DAY THREE: Pythonic Methodology
DAY FOUR: Scientific Computing I
DAY FIVE: Scientific Computing II

DAY ONE: Get Up to Speed

The Shell • Python Programs • Sequences • Objects I • Objects II • Flow Control • back to contents

The Shell

  • Installing
  • Invoking

Python Programs

  • Functions
  • Docstrings
  • Objects
  • Structure
  • Modules

Sequences

  • Dictionaries
  • Lists
  • Tuples
  • Assignment
  • Strings
  • List Comprehensions
  • Slices

Objects I

  • Arguments
  • Builtins
  • References
  • Filters
  • Short Circuits
  • Lambdas

Objects II

  • Imports
  • Classes
  • Subclasses
  • Methods
  • Attributes
  • Visibility

Flow Control

  • Exceptions
  • Files
  • Interation
  • System modules
  • OS Modules

DAY TWO: Service Oriented Architecture

Regular Expressions • HTML • XML • Streams • REST • SOAP • back to contents

Regular Expressions

  • Matches
  • Globs
  • Repetition
  • Sets
  • Logic
  • Groups

HTML

  • SGML
  • Document Object Model
  • Locals and Globals
  • Formatting
  • URL quoting
  • Transforms

XML

  • Pacakges
  • Parsing
  • Unicode
  • Searching Elements
  • Accessing Attributes and Children

Streams

  • Abstraction
  • Standard IO
  • Pipes
  • Command Line Flags

REST

  • HTTP
  • Web Services
  • User Agents
  • Caching
  • Redirection
  • Compression

SOAP

  • SoaPy
  • Remote Methods
  • WSDL
  • Google API
  • Wire Tracing

DAY THREE: Pythonic Methodology

Extreme Programming • Functional Programming • Performance • Debugging • back to contents

Extreme Programming

  • Test Driven Development
  • Refactoring

Functional Programming

  • Object Mapping
  • Dynamic Code

Performance

  • Benchmarking
  • Tuning
  • Profiling

Debugging

  • The Python Debugger
  • Setting Breakpoints
  • Single Stepping
  • Inspecting the Stack

DAY FOUR: Scientific Computing I

NumPy I • NumPy II • NumPy III • ScientificPython I • ScientificPython II • ScientificPython III • back to contents

NumPy I

  • Arrays
  • Indexing
  • Functions and Operators
  • Typecodes

NumPy II

  • Scalars
  • Vectors
  • Slices

NumPy III

  • Randomization
  • Gnuplot
  • Curve Fitting
  • Structured Grids
  • Tools

ScientificPython I

  • Derivatives
  • Roots
  • Interpolation
  • Least Squares
  • Polynomials

ScientificPython II

  • Geometry
  • Quaternions
  • Tensors
  • Transforms

ScientificPython III

  • Signal Models
  • Statistics
  • NetCDF
  • Protein Data Bank
  • VRML

DAY FIVE: Scientific Computing II

SciPy I • SciPy II • Tools •

SciPy I

  • FFT
  • Integration
  • ODEs
  • B-Splines
  • Convolution

SciPy II

  • Hilbert Transforms
  • Matrices
  • Eigenvalues and Eigenvectors
  • Decomposition

Tools

  • PyMat (MatLab)
  • PyTables (HDF)
  • RPy
  • matplotlib
  • Python Cartographic Library
  • Thuban
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2009-01-22
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