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add jupyter notebook and postgresql resources
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content/pages/02-development-environments/06-jupyter-notebook.markdown

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data science notebook projects such as MATLAB and Mathematica to set the stage
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for IPython and Jupyter's creation.
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* [How to use Jupyter Notebooks in 2020 (Part 1: The data science landscape)](https://ljvmiranda921.github.io/notebook/2020/03/06/jupyter-notebooks-in-2020/)
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is a high-level overview post that starts a series on Jupyter Notebooks.
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This first post covers why a tool like Jupyter Notebook is needed in
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the broader landscape of data science.
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* [How to Version Control Jupyter Notebooks](https://nextjournal.com/schmudde/how-to-version-control-jupyter)
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explains how Jupyter Notebooks are stored in JSON, the issues with that
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format for [source control](/source-control.html) and how to get

content/pages/03-data/02-postgresql.markdown

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* [How to speed up tests in Django and PostgreSQL](http://nemesisdesign.net/blog/coding/how-to-speed-up-tests-django-postgresql/)
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explains some hacks for making your schema migration-backed run quicker.
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* [Thinking psycopg3](https://www.varrazzo.com/blog/2020/03/06/thinking-psycopg3/)
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is written by a developer who has worked on this critical Python library
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for interacting with PostgreSQL since 2005. The author writes up thoughts
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on what should change if backwards-incompatible changes are ever introduced
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in a new hypothetical future version.
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* [Records](https://pypi.org/project/records/) is a wrapper around the psycopg2
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driver that allows easy access to direct SQL access. It's worth a look if
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you prefer writing SQL over using an

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