| Chapter | Topics |
|---|---|
| Intro | Python, iPython, Python notebooks, Spyder |
| Intro to NumPy | Arrays: Create, Index, Slice, Reshape, Resize, Vectorized ops, Matrix & Vector ops |
| Intro to SymPy | Symbols, Expressions, Manipulations, Calculus, Equations, Linear Algebra |
| Plotting & Visualization Intro to Matplotlib |
Plots, Steps, Bargraphs, Histograms, ErrorBars, ScatterPlots, Fill_Between, QuiverPlots, ColorMaps, 3D Plots |
| Equation Solvers with SciPy | Linear Equations (square, rectangular), Eigenvalues, NonLinear Equations |
| Optimization with SciPy | Univariate, Multivariate (unconstrained), Nonlinear Least Squares, Constrained |
| Interpolation with SciPy | Polynomials, Splines, Multivariate |
| Integration with SciPy & Scikit-monaco | Numerical Methods, Multiple integration, Symbolic & arbitrary precision, Integral transforms |
| Ordinary Differential Equations (ODEs) | Direction fields, Laplace transforms, Numerical methods |
| Sparse Matrices & Graphs | Matrix ops, Linear equation systems incl. Eigenvalues, Graphs/Networks |
| Partial Differential Equations (PDEs) | Finite-difference methods, Finite element methods & libraries, PDE solvers with FEniCS |
| Data Analysis with Pandas & Seaborn | Series, DataFrames, TimeSeries |
| Statistics with SciPy & NumPy | Probability, Random numbers, Distributions, Hypothesis testing, Nonparametric methods |
| Statistical Modeling with Stasmodels & Patsy | Model definitions, Linear regression, Logistic regression, Poisson models, Time series |
| Intro to Machine Learning with scikit-learn | Concepts, Regression, Classification, Clustering |
| Bayesian Statistics with pyMC | Concepts, Sampling posterior distributions, Linear regression |
| Signal Processing with SciPy, fftpack, signal, wavfile & io | Spectral analysis (Fourier transforms, windows, spectrograms), Signal filters (Convolution, FIR/IIR filters) |
| Data I/O | CSV, HDF5 (h5py files, groups, datasets, attributes, PyTables, HDFStore), JSON, Serialization |
| Code optimization | Numba, Cython |
