Resources
Python
Books
- Python for Data Analysis 3E by Wes McKinney
- Think Python 3E by Allen B. Downey
- How to Think Like a Computer Scientist: Learning With Python 3 by Peter Wentworth, Jeffrey Elkner, Allen B. Downey, & Chris Meyers
Python / NumPy / pandas
Software Carpentry
Statistics
- Learning Statistics with Python
- Data 8—Computational and Inferential Thinking: The Foundations of Data Science (Berkeley)
Visualization
- matplotlib plot types (with code examples)
- matplotlib gallery (with code examples)
- Scientific Visualization: Python + Matplotlib by Nicolas P. Rougier
Online Course
- CS50P: Introduction to Programming with Python (Harvard)
Paul’s notes
- Digital representation of data
- Control flow & Complex data types
- Functions, File input & output
- NumPy & Matplotlib
- Sampling & Frequency Representation of Data
- Optimization
- Parametric Statistical Tests
- Resampling, Bootstrapping, & Cross-Validation
- Maximum likelihood estimation (MLE)
- Simulating dynamical systems I
- Simulating dynamical systems II
MATLAB
- Digital representation of data
- Basic data types, operators & expressions
- Getting user input & printing output
- Control flow
- Functions
- Complex data types
- File Input & Output
- Graphical Displays of Data
- Debugging, Profiling & Speedy Code
- Signals, Sampling, & Filtering
- Optimization & Gradient Descent
- Integrating ODEs & Simulating Dynamical Systems
- Modelling Action Potentials
some R Resources
- A Beginner’s Guide to R (free pdf download if you’re on the Western computer network)
- R for MATLAB users
- R for Data Science (2e)
- the Program section of R for Data Science