- Introduction to Python
- Getting started with Python and the IPython notebook
- Functions are first class objects
- Data science is OSEMN
- Working with text
- Preprocessing text data
- Working with structured data
- Using SQLite3
- Using HDF5
- Using numpy
- Using Pandas
- 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
- Finding roots
- Optimization Primer
- Using scipy.optimize
- Gradient deescent
- Newton’s method and variants
- Constrained optimization
- Curve fitting
- Finding paraemeters for ODE models
- Optimization of graph node placement
- Optimization of standard statistical models
- Fitting ODEs with the Levenberg–Marquardt algorithm
- 1D example
- 2D example
- Algorithms for Optimization and Root Finding for Multivariate Problems
- Expectation Maximizatio (EM) Algorithm
- Monte Carlo Methods
- Resampling methods
- Resampling
- Simulations
- Setting the random seed
- Sampling with and without replacement
- Calculation of Cook’s distance
- Permutation resampling
- Design of simulation experiments
- Example: Simulations to estimate power
- Check with R
- Estimating the CDF
- Estimating the PDF
- Kernel density estimation
- Multivariate kerndel density estimation
- Markov Chain Monte Carlo (MCMC)
- Using PyMC2
- Using PyMC3
- Using PyStan
- 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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Python as Glue
%load_ext rpy2.ipython
%matplotlib inline
%%R library(lattice) attach(mtcars) # scatterplot matrix splom(mtcars[c(1,3,4,5,6)], main="MTCARS Data")
Matlab works too:
pip install pymatbridge
!pip install --upgrade pymatbridge
Requirement already up-to-date: pymatbridge in /Users/cliburn/anaconda/lib/python2.7/site-packages Cleaning up...
import pymatbridge as pymat ip = get_ipython() pymat.load_ipython_extension(ip)
Starting MATLAB on ZMQ socket ipc:///tmp/pymatbridge Send 'exit' command to kill the server .MATLAB started and connected!
/Users/cliburn/anaconda/lib/python2.7/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated. - use IPython.nbformat for read/write/validate public API - use IPython.nbformat.vX directly to composing notebooks of a particular version """)
%%matlab xgv = -1.5:0.1:1.5; ygv = -3:0.1:3; [X,Y] = ndgrid(xgv,ygv); V = exp(-(X.^2 + Y.^2)); surf(X,Y,V) title('Gridded Data Set', 'fontweight','b');
! pip install oct2py
Requirement already satisfied (use --upgrade to upgrade): oct2py in /Users/cliburn/anaconda/lib/python2.7/site-packages Cleaning up...
%load_ext oct2py.ipython
%%octave A = reshape(1:4,2,2); b = [36; 88]; A\b [L,U,P] = lu(A) [Q,R] = qr(A) [V,D] = eig(A)
ans = 60 -8 L = 1.00000 0.00000 0.50000 1.00000 U = 2 4 0 1 P = Permutation Matrix 0 1 1 0 Q = -0.44721 -0.89443 -0.89443 0.44721 R = -2.23607 -4.91935 0.00000 -0.89443 V = -0.90938 -0.56577 0.41597 -0.82456 D = Diagonal Matrix -0.37228 0 0 5.37228
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