##### Tutorial Information and Participant Instructions

Tutorial set up instructions will be added in June. There will be a slack channel set for each tutorial if you have trouble with set up or have additional questions.

###### Introductory Tutorials

#### Intro to Python Programmingâ€‹

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Matt Davis

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This tutorial is a gentle introduction to Python for folks who are completely new to it and may not have much experience programming. We’ll work in a Jupyter Notebook, one of the most popular tools in scientific Python. You’ll learn how to write beautiful Python while practicing loops, if’s, functions, and usage of Python’s built-in features in a series of fun, interactive exercises. By the end of the tutorial we think you’ll be ready to write your own basic Python -- but most importantly, we want you to learn the form and vocabulary of Python so that you can understand Python documentation, interpret code written by others, and get the most out of other SciPy tutorials.

Tutorial Prerequisites: None

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Set up instructions:

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#### Introduction to Numerical Computing With NumPy

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Eric Olsen

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NumPy provides Python with a powerful array processing library and an elegant syntax that is well suited to expressing computational algorithms clearly and efficiently. We'll introduce basic array syntax and array indexing, review some of the available mathematical functions in NumPy, and discuss how to write your own routines. Along the way, we'll learn just enough about matplotlib to display results from our examples. Tutorial Prerequisites: The tutorial is intended for people new to the scientific Python ecosystem.

Tutorial Prerequisites: The tutorial is intended for people new to the scientific Python ecosystem. Previous experience in Python or another programming language is useful but not required.

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Set up instructions:

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#### Learn Python through Data Processing in Pandas

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Daniel Chen

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The goal of this tutorial is to guide new learners into Python, especially those who are first-time attendees of SciPy. We'll introduce how to program in Python using data cleaning in pandas as the teaching example. This will help transition new learners who work with data in spreadsheets but want to utilize the power of the scientific python stack.

Tutorial Prerequisites: The tutorial is intended for people new to the scientific Python ecosystem. Previous experience in Python or another programming language is useful but not required.

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Set up instructions:

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#### The Jupyter Interactive Widget Ecosystem

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Matthew Craig, Maarten Breddels, Martin Renou, Itay Dafna, Mehmet Bektas

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Jupyter widgets are powerful tools for building user interfaces with graphical controls such as sliders and text boxes inside a Jupyter notebook. Interactive widgets can also be rendered in Sphinx documentation, nbviewer, and static web pages. Jupyter widgets are more than a collection of controls, they also are a framework that makes it easy to build custom GUI controls. Examples of custom widget packages include libraries for interactive 2-D charting (bqplot), 3-D graphics (pythreejs, ipyvolume), mapping (ipyleaflet), and more.

Tutorial Prerequisites: Basically familiarity with jupyter notebook and/or jupyter lab. Participant should be able to open a notebook in the classic interface or in lab and execute cells without assistance.

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Set up instructions:

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#### Introduction to Conda for (Data) Scientists

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David Pugh

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This tutorial is a Software Carpentry-style introduction to Conda for (data) scientists. This tutorial motivates the use of Conda as a development tool for building and sharing project specific software environments that facilitate reproducible (data) science workflows. Particular attention is given to using Conda to create reproducible environments with NVIDIA GPU dependencies (including environments for Horovod, TensorFlow, PyTorch, and NVIDIA RAPIDS) as well as a discussion of best practices for using Conda in HPC environments.

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Tutorial Prerequisites: Basic familiarity with Python programming and Bash shell concepts (i.e., basic commands, environment variables, etc). Familiarity installing NVIDIA CUDA Toolkit would be beneficial for NVIDIA GPU focused episodes.

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###### Intermediate/Advanced Tutorials

#### Xarray for Scalable Scientific Data Analysis

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Joseph Hamman, Ryan Abernathy, Deepak Cherian, Stephan Hoyerâ€‹

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Xarray provides data structures for multi-dimensional labeled arrays and a toolkit for scalable data analysis on large, complex datasets with many related variables. Xarray combines the convenience of labeled data structures inspired by Pandas with the multi-dimensional arrays of NumPy and parallel out-of-core computation from Dask to provide an intuitive, powerful and scalable platform for scientific analysis. This tutorial will introduce data scientists already familiar with Numpy and Pandas to the Xarray package and will guide participants through the process of using Xarray from small to big data applications. The tutorial also highlights how Xarray interacts with the greater scientific Python ecosystem and a wide range of common array storage formats.

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Tutorial Prerequisites: Students are expected to have some familiarity with Jupyter, Numpy, and Pandas. No specific domain knowledge is required to effectively participate in this tutorial.

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Set up instructions:

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#### Parallel and Distributed Computing in Python with Dask

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James Bourbeau, Mike McCarty, Dharhas Pothinaâ€‹

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Dask is a library for scaling and parallelizing Python code on a single machine or across a cluster. Dask provides familiar, high-level interfaces to extend the SciPy ecosystem (e.g. NumPy, Pandas, Scikit-Learn) to larger-than-memory or distributed environments, as well as lower-level interfaces for parallelizing custom algorithms and workflows. This tutorial will cover the ins and outs of Dask for new users, including the Dask Array and Dask DataFrame collections, low-level Dask Delayed and Futures interfaces, pros and cons of Dask's task schedulers, and interactive diagnostic tools to help users better understand their computational performance.

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Tutorial Prerequisites: No prior experience with Dask is required. Familiarity with Python, NumPy, and Pandas is preferred.

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#### Deep Learning from Scratch with PyTorch

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Hugo Bowne-Anderson, Dhavide Aruliah

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This tutorial introduces deep learning (also called neural networks) to intermediate-level Pythonistas. The goal is for participants to develop a sound conceptual foundation for deep learning and to obtain some hands-on experience using an industry-ready toolkit. They will do this in two parts: (1) implementing a neural network classifier from scratch (following a quick review of NumPy array-based computing & supervised learning with Scikit-Learn); and (2) a tour of the PyTorch library building more sophisticated, industry-grade neural networks of varying depth & complexity.

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Tutorial Prerequisites: Attendees should be comfortable with basic Python programming (e.g., data structures, functions, etc.) Some prior exposure to Python data science libraries (e.g., NumPy, Pandas, Scikit-Learn)is helpful.

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#### Bayesian Data Science by Simulation

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This tutorial is an Introduction to Bayesian data science through the lens of simulation or hacker statistics. We will become familiar with many common probability distributions through i) matching them to real-world stories & ii) simulating them. We will work with joint/conditional probabilities, Bayes Theorem, prior/posterior distributions and likelihoods, while seeing their applications in real-world data analyses. We’ll see the utility of Bayesian inference in parameter estimation and comparing groups and we’ll wrap up with a dive into the wonderful world of probabilistic programming.

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Tutorial Prerequisites: Knowledge of `numpy`, `matplotlib`, and Python are prerequisites for this tutorial, in addition to curiosity and an excitement to learn new things!

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#### Spatial Data Analysis with PySAL

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This tutorial is an introduction to geospatial data analysis in Python, with a focus on the Python Spatial Analysis Library (PySAL). It introduces participants to the different libraries to work with vector geospatial data, and will cover munging geo-data and exploring relations over space. This includes importing data in different formats (e.g. shapefile, GeoJSON), visualizing, combining, and tidying them up for analysis. The second part of the workshop focuses on applications of spatial anaytical methods to geodemographics and segregation.

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Tutorial Prerequisites: No previous experience with those geospatial python libraries is needed, but basic familiarity with geospatial data and concepts (shapefiles, vector vs raster data) and pandas will be helpful.

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Set up instructions:

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