# SciPy - for Scientific Computing in Python
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## Data Science tools in Python
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* [SciPy](https://www.scipy.org/) ecosystem of open-source software for mathematics, science, and engineering.
* [BioPython](https://biopython.org/) tools for biological computation.
* [NumPy](http://www.numpy.org/) to handle N-dimensional arrays.
* [Pandas](https://pandas.pydata.org/) Python Data Analysis Library. (Data Frames)
* [Matplotlib](http://matplotlib.org/) a 2D plotting library.
* [Seaborn](https://seaborn.pydata.org/) data visualization library based on matplotlib.
* [Bokeh](https://bokeh.pydata.org/) interactive visualization library.
* [SciKit-Learn](http://scikit-learn.org/) Machine Learning in Python. (name from: SciPy Toolkit)
* [TensorFlow](https://www.tensorflow.org/) Machine learning framework. (primarily for deep-learning) (developed by Google engineer)
* [Keras](https://keras.io/) Machine learning framework (primarily for deep-learning, neural-network). (On top of Tensorflow and now part of it.)
* [PyTorch](https://pytorch.org/) Machine learnng framework. (primarily for deep-learning) (from Facebook)
* [Orange](https://orange.biolab.si/) Machine learning and data visualization tool. Written partially in Python.

* [Airflow](https://airflow.apache.org/) Workflow management platform (from AirBnB)
* [Luigi](https://github.com/spotify/luigi) Data pipelines (from Spotify)

* [Showing speed improvement using a GPU with CUDA and Python with numpy on Nvidia Quadro 2000D](https://code-maven.com/showing-speed-improvement-with-gpu-cuda-numpy)

* [Octave](https://www.gnu.org/software/octave/) (Open Source Matlab replacement - not related to Python)

* There are a lot more libraries for each one of these tasks. Shop around!


## Data Analysis resources
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* [Exploratory data analysis](https://en.wikipedia.org/wiki/Exploratory_data_analysis) by John Tukey
* [Think Bayes - Bayesian Statistics Made Simple](http://www.greenteapress.com/thinkbayes/thinkbayes.pdf)
* [Statistical Signal Extraction and Filtering: Structual Time Series Models](https://www.le.ac.uk/users/dsgp1/ERCSTUFF/ercimstruct.pdf)
* [Panel Data](https://en.wikipedia.org/wiki/Panel_data )

For Econometrics

* [Econometric Analysis](http://pages.stern.nyu.edu/~wgreene/Text/econometricanalysis.htm)
* [Microeconometric Modeling and Discrete Choice Analysis with Cross Section and Panel Data](http://people.stern.nyu.edu/wgreene/Microeconometrics.htm)

For Intro Stats

* [Applied Statistics with R](https://daviddalpiaz.github.io/appliedstats/)
* [Statistics: A Fresh Approach](https://www.amazon.com/Statistics-Approach-Donald-H-Sanders/dp/0070546789)

Datasets

* [Climate](http://www.worldclim.org/)
* [Open Weather map](https://openweathermap.org/bulk)
* [PRB](https://www.prb.org/)

