Scientific Computing with Python
Austin, Texas • July 10-16, 2017
 

Tutorial Participant Instructions

SciPy2017 is on Slack and each tutorial has a channel.

Join the channels for the tutorials for which you are registered. Please post any questions or discussion topics on the channel.

Contact scipy@enthought.com if you need an invitation to Slack.



Monday July 10, 2017 8:00 AM - 12:00 PM

Software Carpentry Scientific Python Course Part 1 (Beginner)
Room 105

Maxim Belkin, Software Carpentry
Software Carpentry tutorial is an introduction to Python. As such, you are not required to know anything about it (though it will not hurt if you do). However, to follow the instructions presented in the session and because learning happens when you try things out for yourself, we encourage you to install Python (ver. 3), and a few of its packages on your laptop computer before the tutorial. There are many ways in which you can obtain and install Python. However, the most reliable and convenient way is to install a "distribution," which is a "plain vanilla" Python plus some extra packages that extend its capabilities. To find a list of popular distributions, navigate to: https://scipy.org/install.html.

Software Carpentry workshop participants frequently find it convenient to use Anaconda distribution that supports Windows, Linux, and macOS. For instructions specific to your operating system, please go to https://maxim-belkin.github.io/2017-07-10-scipy/ and scroll down to the "Setup" section.

Cython for Data, Scientists, and Data Scientists (Intermediate/Advanced)
Room 203
Kurt Smith, HomeAway
Tutorial materials including an outline can be viewed here

Numba: Tell Those C++ Bullies to Get Lost (Intermediate)
Room 101
Gil Forsyth, George Washington University
Lorena Barba, George Washington University
Tutorial materials including an outline can be viewed here.

Automatic Code Generation with SymPy (Advanced)
Room 103
Jason Moore, PyDy and SymPy
Aaron Meurer, University of South Carolina, SymPy
Björn Dahlgren, KTH Royal Institute of Technology, SymPy
Kenneth Lyons, University of California, Davis
Tutorial materials including an outline can be viewed
here

Modern Optimization Methods in Python (Intermediate/Advanced)
Room 106
Michael McKerns, UQ Foundation
Tutorial materials including an outline can be viewed here.


Monday July 10, 2017 1:30 PM - 5:30 PM

Software Carpentry Scientific Python Course Part 2 (Beginner)
Room 101
Maxim Belkin, Software Carpentry
Tutorial materials including an outline please see above in Part 1

Computational Statistics (Beginner)
Room 105
Allen Downey, Olin College
Tutorial materials including an outline can be viewed here

The Jupyter Interactive Widget Ecosystem (Intermediate/Advanced)
Room 203
Matt Craig, Minnesota State University Moorhead
Sylvain Corlay
Jason Grout, Bloomberg LP
Tutorial materials including an outline can be viewed here

HDF5 take 2: h5py & PyTables (Intermediate/Advanced)
Room 103
Tom Kooij
Tutorial materials including an outline can be viewed
here

Interactive Data Visualization with HoloViews & Bokeh (Advanced)
Room 106
Philipp Rudiger, Continuum Analytics
Jean-Luc Stevens, Continuum Analytics
Bryan Van de Ven, Continuum Analytics
Tutorial materials in
cluding an outline can be viewed here


Tuesday July 11, 2017 8:00 AM - 12:00 PM

Introduction to Numerical Computing with NumPy (Beginner)
Room 103
Dillon Niederhut, Enthought
Tutorial materials including an outline can be viewed
here

Pandas for Data Analysis (Beginner)
Room 106
Daniel Chen, Virginia Tech
Tutorial materials including an outline can be viewed
here

Machine Learning with scikit-learn Part One (Intermediate)
Room 203
Andreas Mueller, Columbia University
Alexandre Gramfort, INRIA, Université Paris-Saclay
The requirements and setup instructions for the scikit-learn tutorials are documented in-depth in the Readme in the tutorial repository here

Parallelizing Scientific Python with Dask (Intermediate)
Room 101
James Crist, Continuum Analytics
Martin Durant, Continuum Analytics
Skipper Seabold, Civis Analytics

Attendees will only need a laptop with an internet connection and browser, we will be providing a remote execution environment where the tutorial will take place. If you want to look at the materials beforehand or have them locally as well, they can be found at https://github.com/dask/dask-tutorial/tree/scipy-2017. Note that these may be updated until the day of the tutorial.

Parallel Data Analysis in Python (Intermediate)
Room 105
Matthew Rocklin, Continuum Analytics
Ben Zaitlen, Continuum Analytics
Aron Ahmadia, Capital One
Tutorial materials including an outline can be viewed here


Tuesday July 11, 2017 1:30 PM - 5:30 PM

Anatomy of Matplotlib (Beginner)
Room 105
Ben Root, Atmospheric and Environmental Research, Inc./Matplotlib
Tutorial materials including an outline can be viewed
here

scikit-image: Image Processing for Python (Intermediate)
Room 101
Stéfan van der Walt, University of California, Berkeley
Emmanuelle Gouillart
Claire McQuin, B
road Institute
Tutorial materials including an outline can be viewed
here

Network Science and Statistics: Fundamentals and Applications (Intermediate)
Room 106
Eric Ma, MIT
Tutorial materials including an outline can be viewed
here

Machine Learning with scikit-learn Part Two (Intermediate)
Room 203
Andreas Mueller, Columbia University
Alexandre Gramfort, I
NRIA, Université Paris-Saclay
The requirements and setup instructions for the scikit-learn tutorials are documented in-depth in the Readme in the tutorial repository here

Signal Processing and Communications Hands-On Using scikit-dsp-comm (Intermediate)
Room 103
Mark Wickert, University of Colorado
Tutorial materials including an outline can be viewed
here