scipy_2016_notes

Tutorials

• Software Carpentry Instructor Training

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- [Software Carpentry Scientific Python Course (Beginner)](https://github.com/katyhuff/2016-07-11-scipy)

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- [Network Science and Statistics: Fundamentals and Applications (Intermediate)](https://github.com/ericmjl/Network-Analysis-Made-Simple) - [Deep Learning for Image Recognition (Beginner)](https://github.com/rouseguy/scipyUS2016_dl-image) - [NumPy (Beginner)](https://github.com/enthought/Numpy-Tutorial-SciPyConf-2016) - [Symbolic Compution with Python using SymPy (Beginner)](https://github.com/sympy/scipy-2016-tutorial) - [Bokeh for Data Applications and Visualization (Intermediate)](https://github.com/bokeh/bokeh-notebooks) - [Simulating Robot, Vehicle, Spacecraft, and Animal Motion with Python (Advanced)](https://github.com/pydy/pydy-tutorial-human-standing) - [Data Science is Software: Developer #lifehacks for the Python Data Scientist (Intermediate)](https://github.com/drivendata/data-science-is-software) - [Numba: Tell those C++ bullies to get lost (Intermediate)](https://github.com/barbagroup/numba_tutorial_scipy2016)

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- [Time Series Analysis with Python (Intermediate)](https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython) - [Machine Learning with scikit-learn (Intermediate)](https://github.com/amueller/scipy-2016-sklearn) - [Matplotlib Tutorial (Beginner)](https://github.com/rougier/matplotlib-tutorial) - [Parallel Python: Analyzing Large Datasets (Intermediate)](https://github.com/mrocklin/scipy-2016-parallel) - The Google compute clusters used during the workshop may not work in the future. - [Scikit-image: Image analysis in Python (Intermediate)](https://github.com/scikit-image/skimage-tutorials/blob/master/2016-scipy/000_index.ipynb) - [Analyzing and Manipulating Data with Pandas (Beginner)](https://github.com/jonathanrocher/pandas_tutorial) ([Video](https://www.youtube.com/watch?v=6ohWS7J1hVA&index=8&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6)) - [Scalable Hierarchical Parallel Computing (Intermediate)](https://github.com/mmckerns/tuthpc) - [Geographic Data Science with PySAL and the pydata stack (Beginner)](https://github.com/darribas/gds_scipy16)

Talks

Day 1

• JupyterHub as an Interactive Supercomputing Gateway
  • https://github.com/cwaldbieser/jhub_remote_user_authenticator
  • https://github.com/mbmilligan/batchspawner
• Experiments as Iterators: asyncio
  • https://github.com/danielballan/bluesky-talk
• Reproducible, One-Button Workflows with the Jupyter Notebook and Scons
  • https://github.com/jhamrick/nbflow
  • https://github.com/jhamrick/nbflow-example

Day 2

• JupyterLab: Building Blocks for Interactive Computing
  • slides: http://archive.ipython.org/media/SciPy2016JupyterLab.pdf
  • video: https://www.youtube.com/watch?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6&v=Ejh0ftSjk6g
  • blog post: http://blog.jupyter.org/2016/07/14/jupyter-lab-alpha/
  • installation typo: conda install -c conda-forge jupyterlab

Day 3

• Tell me Something I Don't Know: Analyzing OkCupid Profiles
  • https://github.com/juanshishido/okcupid
  • https://github.com/juanshishido/scipy_proceedings

Keynotes

Brian Granger

• https://github.com/ellisonbg/altair

Hanna Wallach @hannawallach http://dirichlet.net/

• https://github.com/hannawallach/python-lda
• https://github.com/hannawallach/cmpsci691bm
• http://mallet.cs.umass.edu/topics.php
• https://radimrehurek.com/gensim/
• http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html
• https://github.com/aschein/bptf
• http://www.fatml.org/