- 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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Getting started with Python and the IPython notebook
The IPython notebook is an interactive, web-based environment that allows one to combine code, text and graphics into one unified document. All of the lectures in this course have been developed using this tool. In this lecture, we will introduce the notebook interface and demonstrate some of its features.
New : A new version of the IPython notebook knowan as Jupyter supports multiple kernels (differnet languages) and other enhancements. For a tour of its features, see this notebook .
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