- 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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R
install.packages("plyr", repos="http://cran.us.r-project.org")
The downloaded binary packages are in /var/folders/bh/x038t1s943qftp7jzrnkg1vm0000gn/T//Rtmpa5E0hb/downloaded_packages
d <- data.frame(year = round(runif(10, 2000, 2005)), count = round(runif(10, 0, 10))) library(plyr) ddply(d, 'year', mutate, mu=mean(count), sigma=sd(count), cv=mu/sigma)
year count mu sigma cv 1 2001 4 2.333333 1.527525 1.527525 2 2001 1 2.333333 1.527525 1.527525 3 2001 2 2.333333 1.527525 1.527525 4 2003 6 4.250000 3.947573 1.076611 5 2003 9 4.250000 3.947573 1.076611 6 2003 1 4.250000 3.947573 1.076611 7 2003 1 4.250000 3.947573 1.076611 8 2004 3 5.000000 2.828427 1.767767 9 2004 7 5.000000 2.828427 1.767767 10 2005 2 2.000000 NA NA
par(mfrow=c(2,(1+length(unique(d$year)))/2), mar = c(3,3,1,1), oma=c(3,3,0,0)) d_ply(d, 'year', transform, plot(count, main=unique(year), type='o'))
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