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Data Engineering Cheat Sheets

Git lets you manage changes made to files and directories in a project. It allows you to keep track of what you did, undo any changes you decide you don't want, and collaborate at scale with others. This cheat sheet will give you the download on all things Git.

Git Cheat Sheet

<h2 id="about-spacy">About spaCy</h2>
<a href="https://spacy.io">spaCy</a> is a free, open-source library for advanced Natural Language Processing (NLP) in Python. It's designed specifically for production use and helps you build applications that process and "understand" large volumes of text.
<a href="https://res.cloudinary.com/dyd911kmh/image/upload/v1653829192/Marketing/Blog/spaCy_Cheat_Sheet_final.pdf"><img style="display: block; margin-left: auto; margin-right: auto;" src="https://res.cloudinary.com/dyd911kmh/image/upload/v1653828756/Screenshot_2022_05_29_at_14_52_15_5f150fbdb6.png" alt="spacy_cheat_sheet" width="900" /></a>
To learn more about spaCy, take my DataCamp course <a href="https://www.datacamp.com/courses/advanced-nlp-with-spacy">"Advanced NLP with spaCy"</a>. You can download the Cheat Sheet <a href="https://res.cloudinary.com/dyd911kmh/image/upload/v1653829192/Marketing/Blog/spaCy_Cheat_Sheet_final.pdf">here</a>!
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Check out the first official spaCy cheat sheet! A handy two-page reference to the most important concepts and features.

spaCy Cheat Sheet: Advanced NLP in Python

You most probably will know by now that data storytelling, accomplished by data visualization, amongst other things, is an essential skill for every data scientist: after you have turned the raw data into understanding, insights and knowledge, you also need to communicate these findings effectively to your audience.
For most beginners, the first Python data visualization library that they use is, naturally, Matplotlib. It is a Python 2D plotting library that enables users to make publication-quality figures. It is quite an extensive library where a <a href="https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet">cheat sheet</a> will definitely come in handy when you're learning, but when you manage to use this library effectively, you'll also be able to get insights and work better with other packages, such as Pandas, that intend to build more plotting integration with Matplotlib as time goes on.
Another package that you'll be able to tackle easily is Seaborn, the statistical data visualization library of Python.
DataCamp has created a Seaborn cheat sheet for those who are ready to get started with this data visualization library with the help of a handy one-page reference.
You'll see that this cheat sheet presents you with the five basic steps that you can go through to make beautiful statistical graphs in Python.
Check out the infographic by clicking on the button below:
<a href="http://datacamp-community-prod.s3.amazonaws.com/263130e2-2c92-4348-a356-9ed9b5034247"><img style="width: 800px; height: 419px;" src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1625839419/Python_Seaborn_Cheat_Sheet_q074wv.png" alt="Seaborn Python cheat sheet" /></a>

This Python Seaborn cheat sheet with code samples guides you through the data visualization library that is based on Matplotlib.

Data Engineering Cheat Sheets

Git Cheat Sheet

spaCy Cheat Sheet: Advanced NLP in Python

Python Seaborn Cheat Sheet