Tutorial Participant Instructions
Still have questions? Visit the individual tutorial channel on scipy2019.slack.com. (Contact SciPy@enthought.com if you need an invitation to Slack.)
Monday, July 8 8:00 am-Noon
Intro to Python Programming​
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Matt Davis
Room 101
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These are the setup instructions for the Introduction to Python tutorial at SciPy 2019. Please visit https://github.com/jiffyclub/scipy-2019-intro-to-python for up-to-date information on the tutorial.
If you don't already have Anaconda installed, download and install Anaconda for Python 3 (not Python 2): https://www.anaconda.com/distribution/.
If you're prompted to install VS Code we recommend you do install it unless you already have a code editor you prefer.
After installing Anaconda you can test your installation using these instructions: http://docs.anaconda.com/anaconda/user-guide/getting-started/#write-a-python-program-using-anaconda-prompt-or-terminal
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Land on Vector Spaces: Practical Linear Algebra with Python​
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Lorena Barba and Tingyu Wang
Room 201
Tutorial instructions may be viewed at https://github.com/engineersCode/EngComp4_landlinear
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Xonsh - Bringing Python Data Science to your Shell
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Gil Forsyth and Anthony Scopatz
Room 104
Tutorial materials may be viewed at https://github.com/xonsh/scipy2019_tutorial
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Bayesian Statistics Made Simple
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Allen Downey
Room 105
Tutorial materials may be viewed at https://allendowney.github.io/BayesMadeSimple/
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Modern Time Series Analysis
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Aileen Nielsen
Room 203
Python 3.x is required
the following packages should be installed and updated to the most recent version per the sample script:
import tensorflow as tf
import mxnet as mx
import xgboost as xgb
import sklearn
import statsmodels
import numpy as npy
import pandas as pd
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Using SatPy to Process Earth-observing Satellite Data
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David Hoese
Room 202
Tutorial materials may be viewed at https://github.com/pytroll/tutorial-satpy-half-day
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Testing your Python Code with PyTest
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John Leeman and Ryan May
Room 106
Tutorial materials may be viewed at https://leemangeophysicalllc.github.io/testing-with-python/
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Monday, July 8 1:30 pm-5:30 pm
Bayesian Data Science: Simulation
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Hugo Bowne-Anderson and Eric Ma
Room 203
Tutorial materials may be viewed at https://github.com/ericmjl/bayesian-stats-modelling-tutorial.
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Complexity Science
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Allen Downey
Room 105
Tutorial materials may be viewed at https://allendowney.github.io/ComplexityScience/
Intermediate Methods for Geospatial Data Analysis
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Levi Wolf and Serge Rey
Room 202
Tutorial materials may be viewed at https://github.com/pysal/scipy2019-intermediate-gds
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Introduction to Numerical Computing with NumPy
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Alexandre Chabot-Leclerc
Room 201
Tutorial materials may be viewed at https://github.com/enthought/Numpy-Tutorial-SciPyConf-2019
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Lights Camera Action! Scrape, Explore, and Model to Predict Oscar Winners & Box Office Hits
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Sebastian Hanus, Deborah Hanus, Patricia Hanus, and Veronica Hanus
Room 104
Tutorial materials may be viewed at https://github.com/oscarpredictor/oscar-predictor
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Reproducible Data Science in Python
Chandrasekhar Ramakrishnan and Xu Fei
Room 101
Tutorial materials may be viewed at https://github.com/SwissDataScienceCenter/r10e-ds-py
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Visualize any Data Easily, from Notebooks to Dashboards
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James Bednar, Jean-Luc Stevens, and Julia Signell
Room 106
Tutorial materials may be viewed at http://holoviz.org
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Tuesday, July 9 8:00 am-Noon
Deep Learning Fundamentals: Forward Model, Differentiable Loss Function, and Optimization Routine
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Eric Ma
Room 203
Tutorial materials will be available soon
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Hands-on Satellite Imagery Analysis
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Sara Safavi and Samapriya Roy
Room 101
This tutorial will be using a hosted Jupyter environment. Only a laptop with internet access and a web browser is needed.
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Introduction to Bayesian Model Evaluation, Visualization, and Comparison Using Arviz
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Ravin Kumar and Colin Carroll
Room 202
Tutorial materials may be viewed at https://github.com/canyon289/bayesian-model-evaluation/
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Introduction to Matplotlib
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Hannah Aizenman and Thomas Caswell
Room 201
Tutorial materials may be viewed here
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RAPIDS: Open GPU Data Science
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Anthony Scopatz, Nick Becker, Keith Kraus, Dante Gama Dessavre
Room 106
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Pre-Tutorial Instructions for RAPIDS: Open GPU Data Science
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This tutorial will be conducted via Jupyter Notebooks hosted on cloud service provider machines. Exact URLs for the Jupyter Notebooks will be provided on the day of the tutorial, as they are generated when the cloud machines are spun up.
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Attendees should make sure to use a modern browser, such as a Google Chrome or Mozilla Firefox. Some laptops have security settings that block ports/websockets necessary for accessing cloud-hosted Jupyter Notebooks. To determine whether your laptop has a compatible configuration, please visit https://websocketstest.com . If your results say "WebSockets seem to work for you", you will be able to access the tutorial materials. If WebSockets do not work for you, please consider bringing an alternate laptop or temporarily changing your settings to allow for WebSockets, taking care to comply with any of your IT restrictions if you are using a corporate laptop.
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Attendees will be required to conduct the tutorial on the cloud Notebooks, even if they have CUDA-compatible GPUs in their laptops.
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Simulate and Generate: An Overview to Simulations and Generating Synthetic Data Sets in Python
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Aileen Nielsen
Room 105
Python 3.x is required
the following packages should be installed and updated to the most recent version per the sample script:
import numpy as np
import pandas as pd
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The Jupyter Interactive Widget Ecosystem
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Matt Craig, Jason Grout, Martin Renou, Maarten Breddels, and Sylvain Corlay
Room 104
Tutorial materials may be viewed at https://github.com/jupyter-widgets/tutorial
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Tuesday, July 9 1:30 pm-5:30 pm
Escape from Auto-manual Testing with Hypothesis!
Zac Hatfield-Dodds
Room 104
Tutorial materials may be viewed at https://github.com/Zac-HD/escape-from-automanual-testing
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Getting Started with JupyterLab
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Jason Grout, Matthias Bussonnier, and Stephanie Stattel
Room 202
Tutorial materials may be viewed at https://github.com/jupyterlab/scipy2019-jupyterlab-tutorial#installation
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Getting Started with Tensorflow
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Josh Gordon and Yufeng Guo
Room 203
Attendees will need a laptop and an internet connection. We will use https://colab.research.google.com for all examples so there is nothing to install in advance.
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Image Analysis in Python with SciPy and Scikit-image
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Joshua Warner, Juan Nunez-Iglesias, and Stéfan van der Walt
Room 106
Tutorial materials may be viewed at https://github.com/scikit-image/skimage-tutorials/blob/master/preparation.md
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Introduction to Data Processing in Python with Pandas
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Daniel Chen
Room 201
Tutorial materials may be viewed at https://github.com/chendaniely/scipy-2019-pandas
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Multi-dimensional Linked Data Exploration with Glue
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Thomas Robitaille
Room 101
Tutorial materials may be viewed at https://github.com/glue-viz/glue/wiki/SciPy-2019-Tutorial-on-Multi-dimensional-Linked-Data-Exploration-with-Glue
Network Analysis Made Simple
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Mridul Seth and Eric Ma
Room 105
Tutorial materials may be viewed at https://github.com/ericmjl/Network-Analysis-Made-Simple
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