A Hands-on Introduction to Cross-disciplinary Analytics With Python

Python is the language of choice when it comes to integrating analytical components. We will present a series of concepts and walkthroughs that illustrate how easy scientific computing is in Python, from machine learning and time series to spatial relationships and network analysis.

1. Explore interactively with IPython (5 minutes)
2. Work with matrices in NumPy (5 minutes)
3. Apply optimized algorithms from SciPy (10 minutes)
4. Visualize numerical data with Matplotlib (10 minutes)
5. Dive into machine learning with scikit-learn (30 minutes)
6. Model time series with pandas (30 minutes)
7. Compute statistics with statsmodels (15 minutes)
8. Interpret spatial relationships with shapely and pysal (30 minutes)
9. Analyze networks with NetworkX (10 minutes)
10. Save huge datasets with h5py (5 minutes)