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

Roy Hyunjin Han (CrossCompute)
Data Science, Sutton Center / Sutton South (NY Hilton)
Tutorial Please note: to attend, your registration must include Tutorials.
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(3.62, 8 ratings)



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)
Photo of Roy Hyunjin Han

Roy Hyunjin Han

CrossCompute

Roy Hyunjin Han is a Computational Engineer at CrossCompute, working on mathematical models to help cities run effectively. Previously, he worked with the Modi Research Group at the Earth Institute of Columbia University to develop an electricity infrastructure planning system currently used by countries in Africa and a remote sensing system that finds buildings in satellite images using convolutional neural networks. He holds a M.S. in machine learning and a B.S. in mathematics and neuroscience.

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