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scipy_2016_notes

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# scipy_2016_notes

# Tutorials

- [Software Carpentry Instructor Training](https://swcarpentry.github.io/instructor-training/)
<hr>
- [Software Carpentry Scientific Python Course (Beginner)](https://github.com/katyhuff/2016-07-11-scipy)
<hr>
- [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)
<hr>
- [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/

scipy_2016_notes

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