Tools
Notes for Tools of Data Science: Git, Command Line, Notebook, AWS etc
21 Data Processing - (Py)Spark
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- Introduction to PySpark on DataCamp
- Cluster configurations - Cleaning Data with PySpark
Summary: Processing Data using (Py)Spark
Pages: 9
8 Documentation
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- sphinx-doc. sphinx-doc/sphinx: Main repository for the Sphinx documentation builder. In: GitHub [Internet]. [cited 28 Aug 2021]. Available: https://github.com/sphinx-doc/sphinx
- Read the Docs
- squidfunk. squidfunk/mkdocs-material: Technical documentation that just works. In: GitHub [Internet]. [cited 28 Aug 2021]. Available: https://github.com/squidfunk/mkdocs-material
Summary: Documenting my data science project using sphinx or mkdocs-material
Pages: 9
7 Cookiecutter
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- drivendata. drivendata/cookiecutter-data-science: A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. In: GitHub [Internet]. [cited 27 Aug 2021]. Available: https://github.com/drivendata/cookiecutter-data-science
- cookiecutter. cookiecutter/cookiecutter: A command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, VueJS projects. In: GitHub [Internet]. [cited 27 Aug 2021]. Available: https://github.com/cookiecutter/cookiecutter
Summary: Use cookiecutter to initialize a project
Pages: 9
6 Some ML Workflow Frameworks
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Summary: Managing workflows in machine learning projects is not trivial.
Pages: 9
5 Git
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Summary: git the tool you need for your everyday work
Pages: 9
3 GNUPlot
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- GNUPLOT
Summary: quickly make a graph in your command line
Pages: 9
2 Amazon CloudWatch Logs
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Summary: CloudWatch logs as a tool for pipeline logs
Pages: 9
1 Jupyter Notebook
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- Built-in magic command @ ipython
Summary: Jupyter Notebook is a useful tool for data scientists
Pages: 9