# High Performance Data Analytics in Python

Scientists, engineers and professionals from many sectors are seeing an enormous growth in the size and number of datasets relevant to their domains. Professional titles have emerged to describe specialists working with data, such as data scientists and data engineers, but also other experts are finding it necessary to learn tools and techniques to work with big data. Typical tasks include preprocessing, analysing, modeling and visualising data.

Python is an industry-standard programming language for working with data on all levels of the data analytics pipeline. This is in large part because of the rich ecosystem of libraries ranging from generic numerical libraries to special-purpose and/or domain-specific packages, often supported by large developer communities and stable funding sources.

This module will give an overview of working with research data in Python using general libraries for storing, processing, analysing and sharing data. The focus is on high performance. After covering tools for performant processing on single workstations the focus shifts to profiling and optimising, parallel and distributed computing.

:::{prereq}

- Basic experience with Python
- Basic experience in working in a Linux-like terminal
- Some prior experience in working with large or small datasets
:::

:::{toctree}
:caption: Software setup
:maxdepth: 1

episodes/Python-HPDA-0-SoftwareSetup
:::

:::{toctree}
:caption: Episodes
:maxdepth: 1

episodes/Python-HPDA-1-Motivation
episodes/Python-HPDA-2-EfficientArrayComputing
episodes/Python-HPDA-2-EfficientArrayComputing-markdown
episodes/Python-HPDA-2-EfficientArrayComputing-pdf
episodes/quiz/quiz_in_episodes
episodes/ModuleName-1-EpisodeName
:::

:::{toctree}
:caption: Reference
:maxdepth: 1

instructor-guide
reference-for-learners
:::

:::{toctree}
:caption: Walkthrough for module authors
:maxdepth: 1

reference/structure-evita-module
:::


## Learning outcomes

This material is for all researchers and engineers who work with large or small datasets and who want to learn powerful tools and best practices for writing more performant, parallelised, robust and reproducible data analysis pipelines.

By the end of this module, learners should:

- Have a good overview of available tools and libraries for improving performance in Python (**link to leaves in skill tree**)
- Knowing libraries for efficiently storing, reading and writing large data  (**link to leaves in skill tree**)
- Be comfortable working with NumPy arrays and Pandas dataframes for data analysis using Python (**link to leaves in skill tree**)
- ...

:::{admonition} Credit
:class: warning

Don't forget to check out additional [**course materials**](https://www.evitahpc.eu/) from XXX. Please [**contact us**](https://www.evitahpc.eu/) if you want to reuse these course materials in your teaching. You can also join the [**XXX channel**](https://www.evitahpc.eu/) to share your experience and get more help from the community.

:::

::::{admonition} License
:class: attention

:::{admonition} CC BY-SA for media and pedagogical material
:class: attention dropdown

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[See the legal code](https://creativecommons.org/licenses/by-sa/4.0/legalcode.en)

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This deed highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. You should carefully review all of the terms and conditions of the actual license before using the licensed material.

:::

:::{admonition} MIT for source code and code snippets
:class: attention dropdown

MIT License

Copyright (c) {{ copyright }}

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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The above copyright notice and this permission notice shall be included in all
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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SOFTWARE.

:::

:::{note}
*To module authors*: For code you may use any OSI-approved license as mentioned in <https://spdx.org/licenses/>, such as Apache License 2.0, GNU GPLv3, MIT. Please make sure to update the deed above and
`LICENSE.code` file accordingly.
:::

::::