• Tue, Jun 5, 2018
• Wed, Jun 6, 2018
• Mon, Jun 11, 2018
• Wed, Jun 13, 2018

The course is intended for engineers with MATLAB^® experience that are interested in extending their knowledge and skills in the Python programming language. The course will combine an introduction to programming in Python with a practical overview of the Scientific Python stack.

• Day 1. Introduction to Python: types, operators, flow, lists, functions, memory model.
• Day 2. Advanced Python: I/O, iteration, object oriented programming.
• Day 3. Numerical Python: arrays, plotting, linear algebra, calculus, ODE, image processing.
• Day 4. High performace computing, concurrency, MATLAB and C#, command line interface, packaging, testing, documenting.

• IPython/Jupyter
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• NumPy
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• SciPy
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• Matplotlib
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• Pandas
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• Seaborn
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• Scikit-image
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• Cython
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• SymPy
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• Python for .NET
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• Python-MATLAB bridge
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• Conda
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• Click
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Download Anaconda with Python 3 which already includes most the popular scientific and numerical packages. Check that everything is installed by running python --version and jupyter notebook in the terminal\command line.

• Introduction to Python for Scientific Computing: main features, history, who uses it, and why.
• myzcat : example Python project, demonstrating project structure, unit testing, and packaging.
• NumPyCharm : Some examples on using PyCharm with Scientific Python.

• Think Python by Allen B. Downey has basic, short, and clear explanations. Free ebook.
• Dive Into Python by Mark Pilgrim is a free Python book for experienced programmers. Free ebook.
• The IPython for Interactive Computing and Data Visualization by Cyrille Rossant targets those who already know Python, and want to learn IPython for the extended console, the notebook, and more advanced scientific applications.
• Think Stats by Allen B. Downey is an introduction to Probability and Statistics for Python programmers. Free ebook.
• Think DSP by Allen B. Downey is an introduction to Digital Signal Processing in Python.
• Fluent Python by Luciano Ramalho reads fluently and makes you fluent in Python's features and details.
• Flask Web Development by Miguel Grinberg is a terrific book on developing web apps and APIs with Flask.

• Detailed video lectures by topics - first steps.
• Google's video lectures - more advanced.
• Enthought's online Scientific Python courses.
• Python related video indexed so you can find it.

• Programming: Pick up Python by Jeffrey M. Perkel in Nature Toolbox. 2015.
• Interactive notebooks: Sharing the code by Helen Shen in Nature Toolbox. 2015.

• NumPy for MATLAB users cheatsheet.
• The Hitchhiker’s Guide to Python - a best practices handbook.
• What to do when my code isn't working?.
• A gallery of interesting IPython Notebooks
• Introduction to Data Processing with Python by OpenTechSchool.
• Programming with Python by Software Carpentry.
• Introduction to Data Processing with Python by Eric Matthes.
• A simple guide for getting started with git.
• The ŷhat blog - filter on Python topics.
• 14 Best Python Pandas Features
• Allen Downey's blog at DSPRELATED.com has many IPython notebook related to digital signal processing.
• Scipy Lecture Notes - Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques.
• Practical Numerical Methods with Python: a MOOC centered around Jupyter notebooks and focusing on ODE, PDE, and diffusion.
• Python Packaging User Guide

• Azure: Run the sessions on the cloud using Microsoft Azure by cloning the Py4Eng library. A valid Microsoft login is required, but the service is free.
• Binder: Turn a GitHub repo into a collection of interactive notebooks in the cloud.
• Python tutor: visualize the memory of your Python code.