Take Off with Stats in Python by Lorena Barba : Hands-on data analysis using a computational approach and real-life applications. Tour the dynamics of change and motion by Lorena Barba : Tour of the dynamics of change and motion using computational thinking with Python. HyperPython: a practical introduction to the solution of hyperbolic conservation laws , a course by David Ketcheson.

Colour science computations with colour , a Python package implementing a comprehensive number of colour theory transformations and algorithms supported by a dedicated collection of IPython Notebooks. More colour science related IPython Notebooks are available on colour-science. Learning Population Genetics in an RNA world is an interactive notebook that explains basic population genetics tools and techniques by building an in silico evolutionary model of RNA molecules.

An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study. This notebook fully reproduces the research published in this paper. The notebook uses mostly python but includes some bash and R as well and is relevant for researchers in bioinformatics and public health.

This Python notebook uses the Jupyter-widget Clustergrammer-Widget to visualize hierarchical clustering of gene expression and post-translational modification data from 37 lung cancer cell lines as an interactive heatmap. The notebook is part of the research project from this paper.

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Materials Science in Python using pymatgen. A series of python notebooks using the pymatgen package and materials project API for materials science. EarthPy , a collection of IPython notebooks with a focus on Earth Sciences, from whale tracks to the flow of the Amazon. Python for Geosciences , a tutorial series aimed at the Earth Sciences community, by Nikolay Koldunov. Find graffiti close to NY subway entrances , one of a rich collection of notebooks on large-scale data analysis , by Roy Hyunjin Han.

Logistic models of well switching in Bangladesh , part of the "Will it Python" blog series repo on Machine Learning and data analysis in Python. By Carl Vogel.

Machinalis has a public repo with material support for geospatial-data processing related blog posts. It includes notebooks about Object Based Image Analysis and irrigation circles detection. It includes a fairly large number of notebooks on how to solve the acoustic and elastic wave equation with various different numerical methods. Additionally it contains notebooks with an extensive introduction to data handling and signal processing in seismology, and notebooks tackling ambient seismic noise, rotational and glacial seismology, and more.

Geo-Python is an introduction to programming in Python for Bachelors and Masters students in geo-fields geology, geophysics, geography taught by members of the Department of Geosciences and Geography at University of Helsinki, Finland. Course lessons and exercises are based on Jupyter notebooks and open for use by any interested person. Workshop on text analysis by Neal Caren. Mining the Social Web 3rd Edition. Sound Analysis with the Fourier Transform. A set of IPython Notebooks by Caleb Madrigal to explain what the Fourier Transform is and how to use it for basic audio processing applications.

An introduction to Compressed Sensing , part of Python for Signal Processing : an entire book and blog on the subject by Jose Unpingco. Kalman and Bayesian Filters in Python. A textbook and accompanying filtering library on the topic of Kalman filtering and other related Bayesian filtering techniques. Digital Signal Processing A collection of notebooks that accompanies a masters course on the topic.

## An Introductory Course on Mathematical Game Theory

Algorithms in IPython notebooks , by Sebastian Raschka. Comparing the performance of Python compilers - Cython vs. Numba vs. Parakeet , by Sebastian Raschka.

- Global and Regional Mercury Cycles: Sources, Fluxes and Mass Balances.
- In Lincolns Hand: His Original Manuscripts with Commentary by Distinguished Americans?
- Advances in Photochemistry. Volume 15.

Repo by Chris Fonnesbeck. Introduction to numerical computing with numpy by Steve Phelps. Using Numba to speed up numerical codes. And another Numba example: self-organizing maps.

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Numpy performance tricks , and blog post , by Cyrille Rossant. Understanding the design of the R "formula" objects by Matthew Brett. Comparing different approaches to evolutionary simulations. Also available here to better visualization. The notebook was converted to a HTML presentation using an old nbconvert with the first developing implementation of reveal converter. By Yoav Ram. A git tutorial targeted at scientists by Fernando Perez. Interactive Curve-Fitting The lmfit package provides a widget-based interface to the curve-fitting algorithms in SciPy.

CodeCombat gridmancer solver by Arn-O. This notebook explains how to improve a recursive tree search with an heuristic function and to find the minimum solution to the gridmancer. Survival Analysis , an illustration of the lifelines library , by Cam Davidson Pilon. Data about the Sandy Hook massacre in Newtown, Conneticut , which accompanies a more detailed blog post on the subject. Here are the notebook and accompanying data. By Brian Keegan. More on gun violence analysis with Wikipedia data.

An analysis of the Gaza-Israel crisis. Ranking NFL Teams. The full repo also includes an explanatory slideshow.

By Sean Taylor. Automated processing of news media and generation of associated imagery. An analysis of national school standardized test data in Colombia using Pandas in Spanish. By Javier Moreno. GDELT is a dataset containing more than million geolocated events with global coverage for to the present. Titanic passengers, coal mining disasters, and vessel speed changes , by Christopher Fonnesbeck.

Sean Pue , C. Titus Brown and Tracy Teal. Using Python to see how the Times writes about men and women , by Neal Caren. Exploring graph properties of the Twitter stream with twython and NetworkX , by F. Perez complete gist repo with utilities here. By Andrew Conti. How clean are San Francisco's restaurants? NYT gender wage gap and US crime by state.

Part of a notebook collection on football aka soccer analysis. Geographic Data Science is an entire course by Dani Arribas-Bel to learn to access, munge, and analyse spatial data on social phenomena. Analysis and visualization of a public OKCupid profile dataset using Python and Pandas by Alessandro Giusti includes many colorful data visualizations.

Intuition and simulation for the theory Ma et al. Demonstrates that variance in cortical activity, rather than impairing sensory systems, is an adaptive mechanism to encode uncertainty in sensory measurements. Modeling psychophysical data with non-linear functions by Ariel Rokem. Visualizing mathematical models of brain cell connections. The effect of convolution of different receptive field functions and natural images is examined.

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Python for Vision Research. A tutorial introduction to machine learning with sklearn , an IPython-based slide deck by Andreas Mueller. Introduction to Bayesian Networks by Kui Tang. A collection of examples for solving pattern classification problems , by Sebastian Raschka. Machine learning in Python , a series based on Andrew Ng's Coursera class on machine learning.

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Part of a larger collection of data science notebooks by John Wittenauer. Complimentary slides. Geodemographic Segmentation Model , by Filipa Rodrigues.