Computational Statistics in Python
Getting started with Python and the IPython notebook
Functions are first class objects
Function argumnents
Higher-order functions
Anonymous functions
Pure functions
Recursion
Iterators
Generators
Decorators
The operator module
The functools module
The itertools module
The toolz, fn and funcy modules
Exercises
Data science is OSEMN
Working with text
Preprocessing text data
Working with structured data
Using numpy
Using Pandas
Using R from IPython
Computational problems in statistics
Computer numbers and mathematics
Algorithmic complexity
Linear Algebra and Linear Systems
Linear Algebra and Matrix Decompositions
Change of Basis
Optimization and Non-linear Methods
Practical Optimizatio Routines
Fitting ODEs with the Levenberg–Marquardt algorithm
Algorithms for Optimization and Root Finding for Multivariate Problems
Expectation Maximizatio (EM) Algorithm
Monte Carlo Methods
Resampling methods
Markov Chain Monte Carlo (MCMC)
Using PyMC2
Using PyMC3
Using PyStan
Animations of Metropolis, Gibbs and Slice Sampler dynamics
C Crash Course
Code Optimization
Using C code in Python
Using functions from various compiled languages in Python
Julia and Python
Converting Python Code to C for speed
Optimization bake-off
Writing Parallel Code
Massively parallel programming with GPUs
Writing CUDA in C
Distributed computing for Big Data
Hadoop MapReduce on AWS EMR with mrjob
Spark on a local mahcine using 4 nodes
Modules and Packaging
Tour of the Jupyter (IPython3) notebook
Polyglot programming
What you should know and learn more about
Wrapping R libraries with Rpy
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