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# numpy_euroscipy2015
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Material for the training on Numpy presented at EuroScipy 2015
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+
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+## Abstract
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+
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+The `numpy` package takes a central role in
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+Python data science code. This is mainly
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+because numpy code has been designed with
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+high performance in mind. This tutorial
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+will provide the most essential concepts
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+to become confident with `numpy` and `ndarray`s.
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+Then some concrete examples of applications
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+where numpy takes a central role, will
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+be presented as well.
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+
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+## Description
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+
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+It is very hard to be a scientist without knowing how to write code,
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+and nowadays **Python** is probably the language of choice in many research fields.
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+This is mainly because the Python ecosystem includes a lot of tools and libraries
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+for many research tasks: `pandas` for *data analysis* ,
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+`networkx` for *social network analysis*,
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+`scikit-learn` for *machine learning*, and so on.
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+
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+Most of these libraries relies (or are built on top of) `numpy`.
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+Therefore, `numpy` is a crucial component of the common Python
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+stack used for numerical analysis and data science.
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+
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+On the one hand, NumPy code tends to be much cleaner (and faster) than
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+"straight" Python code that tries to accomplish the same task.
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+Moreover, the underlying algorithms have
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+been designed with high performance in mind.
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+
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+This training is be organised in two parts: the first part is
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+intended to provide most of the essential concepts
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+needed to become confident with NumPy data structures and functions.
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+
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+In the second part, some examples of data analysis libraries and code
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+will be presented, where NumPy takes a central role.
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+
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+Here is a list of software used to develop and test the code examples presented
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+during the training:
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+
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+* Python 3.x (2.x would work as well)
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+* iPython 2.3+ (with **notebook support**) or Jupyter:
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+ * `pip install ipython[notebook]` (OR)
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+ * `pip install jupyter`
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+* numpy 1.9+
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+* scipy 0.14+
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+* scikit-learn 0.15+
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+* pandas 0.8+
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+
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+
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+## Target Audience
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+
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+The training is meant to be mostly introductory, thus it is perfectly suited
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+for **beginners**. However, a good proficiency in Python programming is (at least)
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+required.
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+
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+## License and Sharing Material
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+
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+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/80x15.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
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Valerio Maggio