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Editing 101 DATA SCIENCE with Cheat Sheets (ML, DL, Scraping, Python, R, SQL, Maths & Statistics)
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https://medium.com/@anushka.datascoop/101-data-science-cheat-sheets-ml-dl-scraping-python-r-sql-maths-statistics-ef30b4d786eb # 101 DATA SCIENCE with Cheat Sheets (ML, DL, Scraping, Python, R, SQL, Maths & Statistics) | by Anushka Bajpai | Medium  [Anushka Bajpai](https://medium.com/@anushka.datascoop) “_Perhaps the best test of a man’s intelligence is his capacity for making a summary”_ [](https://www.wisefamousquotes.com/lytton-strachey-quotes/perhaps-the-best-test-of-a-man039s-intelligence-1859399/)— **Lytton Strachey** **_Couldn’t find a better way to begin my ‘Medium’ journey with . . ._** Photo by [Annie Spratt](https://unsplash.com/@anniespratt?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com/?utm_source=medium&utm_medium=referral) > Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all these functions, operations and formulas for each of concept. **That’s why we have cheat sheets and summaries**. They help us access the most commonly needed reminders for making our Data Science journey fast and easy. > There are thousands of cheat sheets available out there. However, how many of them do you really find useful ? > > It’s a treasure to have good quality resources compiled at one place. Hence, I decided to go ahead and put in this end to end compilation of Cheat Sheets for an instant and thorough reference. **_For my readers’ convenience, I have segregated the best cheat sheets (comprehensive yet concise) separately for each of the below topics. . ._** 1. **_Python_** (_Basic python as well as ML libraries : Numpy /Pandas/Matplotlib/Seaborn/Plotly/Spacy_) 2. **_R_** 3. **_SQL_** 4. **_Web scraping Tools_** (_Beautifulsoup, Scrapy and Selenium)_ 5. **_Data Visualization Tools_** (_Tableau, Power BI_) 6. **_Maths and Statistics_** 7. **_Machine Learning Algorithms_** _(Supervised and Unsupervised)_ 8. **_Deep Learning and NLP_** _(Neural Networks, Keras, Tensorflow, Pytorch, NLTK, Spacy)_ 9. **_IDE/OS_** _(GitHub, Linux, SkLearn, Jupyter Notebook)_  [Source](https://www.linkedin.com/groups/3990648/?q=highlightedFeedForGroups&highlightedUpdateUrn=urn%3Ali%3AugcPost%3A7187824630445805568) PYTHON ------  Source :[https://www.reddit.com/](https://www.reddit.com/) **_Basic Python_** 1. **_gto76 —_** [_extensive Python cheat sheet_](https://github.com/gto76/python-cheatsheet) _offered by GitHub_ 2. [memento](https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf) 3. [_Pythoncheatsheet.org_](https://www.pythoncheatsheet.org/) 4. [_Cheatography_](https://cheatography.com/davechild/cheat-sheets/python/) 5. [_Python crash course_](https://ehmatthes.github.io/pcc/cheatsheets/README.html) 6. _DataQuest (_ [_basic_](https://s3.amazonaws.com/dq-blog-files/python-cheat-sheet-basic.pdf) _and_ [_intermediate_](https://s3.amazonaws.com/dq-blog-files/python-cheat-sheet-intermediate.pdf) _)_ 7. [_MementoPython3_](https://perso.limsi.fr/pointal/_media/python:cours:mementopython3-english.pdf) **_Numpy_** 1. [_DataCamp_](http://datacamp-community-prod.s3.amazonaws.com/ba1fe95a-8b70-4d2f-95b0-bc954e9071b0) 2. [_Intellipat_](https://intellipaat.com/mediaFiles/2018/12/Python-NumPy-Cheat-Sheet-1.png) 3. [_A Little Bit of Everything_](http://datasciencefree.com/numpy.pdf) 4. [_Data Quest_](https://s3.amazonaws.com/dq-blog-files/numpy-cheat-sheet.pdf) 5. [_NumPy for R (and S-plus) Users_](http://mathesaurus.sourceforge.net/r-numpy.html) 6. [_Ipgp\_github_](https://ipgp.github.io/scientific_python_cheat_sheet/?utm_content=buffer7d821&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer#numpy-import-numpy-as-np) **_Pandas_** 1. [_The Most Comprehensive Cheat Sheet_](https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf) 2. BecomingHuman.AI 3. [_The Beginner’s Cheat Sheet_](https://www.dataquest.io/blog/pandas-cheat-sheet/) 4. [_DATACAMP_](http://datacamp-community-prod.s3.amazonaws.com/f04456d7-8e61-482f-9cc9-da6f7f25fc9b) 5. [_Data Science Central (compact)_](http://datacamp-community-prod.s3.amazonaws.com/dbed353d-2757-4617-8206-8767ab379ab3) 6. [_INTELLIPAT_](https://intellipaat.com/mediaFiles/2018/12/Python-Pandas-Cheat-Sheet.png)  **_Matplotlib_** 1. [Datacamp](http://datacamp-community-prod.s3.amazonaws.com/e1a8f39d-71ad-4d13-9a6b-618fe1b8c9e9) 2. [GitHub](https://camo.githubusercontent.com/bc3b143766ed68eb6a851900c317c5d9222eb1471888942afb35137aa5141557/68747470733a2f2f6d6174706c6f746c69622e6f72672f63686561747368656574732f63686561747368656574732d312e706e67) 3. [Cheatography](https://cheatography.com/aggialavura/cheat-sheets/python-matplotlib/pdf_bw/) 4. [Becominghuman.ai](https://miro.medium.com/max/1890/1*uZOP1Jv2P3uyngWnTfO9Kg.png) 5. [Travis_Blog](https://www.travistang.com/blog/-matplotlib-cheat-sheet-for-quick-data-visualization) **_Seaborn_** 1. [Datacamp](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf) 2. [Cheatography](https://cheatography.com/justin1209/cheat-sheets/seaborn/) 3. [MartinNormark](https://martinnormark.com/a-simple-cheat-sheet-for-seaborn-data-visualization-2/) 4. [kaggle](https://www.kaggle.com/themlphdstudent/cheat-sheet-seaborn-charts) **_Plotly_** 1. [Datacamp](https://www.datacamp.com/cheat-sheet/plotly-express-cheat-sheet) 2. [Plotly official](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) 3. [KDnuggets](https://www.kdnuggets.com/publications/sheets/Plotly_Express_for_Data_Visualization_Cheat_Sheet_KDnuggets.pdf)        R ------ 1. [github](https://iqss.github.io/dss-workshops/R/Rintro/base-r-cheat-sheet.pdf) 2. [datacamp](http://datacamp-community-prod.s3.amazonaws.com/008e41e8-7526-4bbc-806b-8c828dfb81d4) 3. [rstudio](https://www.rstudio.com/resources/cheatsheets/) 4. [intellipat](https://intellipaat.com/mediaFiles/2019/02/Data-structure-in-R-Cheat-Sheet-.png)  SQL ------ 1. [SQL_Detailed](https://websitesetup.org/wp-content/uploads/2020/08/SQL-Cheat-Sheet-websitesetup.pdf) 2. [LearnSQL](https://learnsql.com/blog/standard-sql-functions-cheat-sheet/) 3. [SQL_Tutorial](https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf) 4. [SQL_Joins](https://storage.googleapis.com/kaggle-forum-message-attachments/671063/14425/SQL.pdf) 5. [Intellipat](https://intellipaat.com/mediaFiles/2019/02/SQL-Commands-Cheat-Sheet.png) 6. [BuggyProgrammer](https://buggyprogrammer.com/sql-cheatsheet/)   Web Scraping Tools ------ 1. [Data Camp (Python webscraping)](https://www.datacamp.com/community/tutorials/web-scraping-using-python) 2. [Beautifulsoup -Tutorialspoint](https://www.tutorialspoint.com/beautiful_soup/beautiful_soup_tutorial.pdf) 3. [Web Scraping with Scrapy and MongoDB](https://realpython.com/web-scraping-with-scrapy-and-mongodb/) 4. [Beautiful Soup — Akul’s Blog](http://akul.me/blog/2016/beautifulsoup-cheatsheet/) 5. [Beautiful Soup — Cheatography](https://cheatography.com/justin1209/cheat-sheets/beautiful-soup/pdf/) 6. [Selenium — Intellipaat](https://intellipaat.com/mediaFiles/2019/03/Selenium-Cheat-Sheet.pdf) 7. [Selenium — Automatetheplanet](https://www.automatetheplanet.com/applications/most-complete-selenium-webdriver-java-cheat-sheet/) _Some blogs and GitHub repositories with more details:_ [Medium Blog by Frank](https://medium.com/geekculture/web-scraping-cheat-sheet-2021-python-for-web-scraping-cad1540ce21c) [Blog by Hartley Brody](https://blog.hartleybrody.com/web-scraping-cheat-sheet/) [GitHub Repo — YUSUZECH](https://github.com/yusuzech/r-web-scraping-cheat-sheet) [Making Web Crawlers Using Scrapy for Python — DataCamp](https://www.datacamp.com/community/tutorials/making-web-crawlers-scrapy-python) [Use web scraping to become more competitive](https://www.newgenapps.com/blogs/using-web-scraping-to-become-more-competitive-in-your-industry-3/) [Here](https://roboticsandautomationnews.com/2020/04/06/essential-legal-issues-associated-with-web-scraping/31501/) is am article on Legal issues associated with Web scraping. Data Visualization Tools ------   Maths and Statistics ------ 1. [Probability](https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf) 2. [Statistics](http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf) 3. [Linear Algebra](https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf) 4. [Matrices](https://www.scribd.com/doc/7877744/Matrices-Cheat-Sheet) 5. [Calculus/Trignometry_etc](https://tutorial.math.lamar.edu/Extras/CheatSheets_Tables.aspx#CalcSheet) 6. [Stats_for_interviews](https://www.stratascratch.com/blog/a-comprehensive-statistics-cheat-sheet-for-data-science-interviews/) Machine Learning Algorithms ------   [Machine Learning Algorithms](https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/) [Stanford_ML](https://stanford.edu/~shervine/teaching/cs-229/) [Machinelearningmastery](https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/) [Sas_ml](https://blogs.sas.com/content/subconsciousmusings/2020/12/09/machine-learning-algorithm-use/) [R_bloggers](https://www.r-bloggers.com/2019/07/101-machine-learning-algorithms-for-data-science-with-cheat-sheets/)        **_Reinforcement Learning_**  1. [Reinforcement Learning Cheatsheet](https://roboticsbiz.com/wp-content/uploads/2021/11/Reinforcement-Learning-Cheatsheet.pdf) 2. [RL Cheatsheet Github](https://github.com/FrancescoSaverioZuppichini/Reinforcement-Learning-Cheat-Sheet/blob/master/rl_cheatsheet.pdf)  **_Evaluation Criteria_**  Deep Learning and NLP ------ 1. [AndrewNG_DeepLearning](https://storage.googleapis.com/kaggle-forum-message-attachments/730211/14820/DeepLearning_AndrewNG.pdf) 2. [Stanford(deep learning)](https://stanford.edu/~shervine/teaching/cs-230/) 3. [Stanford(neural_nets)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks) 4. [Datacamp (Keras)](http://datacamp-community-prod.s3.amazonaws.com/af9bb467-170d-41c9-a0bd-26e675384c4e) 5. [Aimov_institute](https://www.asimovinstitute.org/neural-network-zoo/)   _Tensorflow_ 1. [BecomingHuman.AI](https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-science-pdf-f22dc900d2d7) 2. [Altoros](https://cdn-images-1.medium.com/max/2000/1*dtOZSuYDonyyBvEULpJALw.png) 3. [Github](https://github.com/patrickphat/Tensorflow-2-cheatsheet/blob/master/tf-cheatsheet.md) 4. [Stanford.edu](https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf) 5. [Cheatography](https://cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf) 6. [HackerNoon](https://github.com/kailashahirwar/cheatsheets-ai/blob/master/Deep%20Learning%20Cheat%20Sheet-Hacker%20Noon.pdf) 7. [TensorFlow](https://www.tensorflow.org/federated)  _Keras_ 1. [BuggyProgrammer](https://buggyprogrammer.com/keras-cheatsheet/) 2. [DataCamp](https://www.datacamp.com/community/blog/keras-cheat-sheet) 3. [Quick_tutorial](https://www.crcv.ucf.edu/wp-content/uploads/2019/03/CAP6412_Spring2018_KerasTutorial.pdf)   **_NLTK_** 1. [Cheatography](https://cheatography.com/murenei/cheat-sheets/natural-language-processing-with-python-and-nltk/pdf/) 2. [Computingeverywhere](https://computingeverywhere.soc.northwestern.edu/wp-content/uploads/2017/07/Text-Analysis-with-NLTK-Cheatsheet.pdf) 3. [nltk.org](https://www.nltk.org/) **_Spacy_** 1. [Spacy.io](https://spacy.io/usage/spacy-101) 2. [Datacamp](http://datacamp-community-prod.s3.amazonaws.com/b5685b85-c4de-4987-926d-b999d0f5a8b6) 3. [kaggle](https://storage.googleapis.com/kaggle-forum-message-attachments/922965/16283/spaCY.pdf) 4. [Cheatography](https://cheatography.com/nuozhi/cheat-sheets/spacy/)  IDE and OS ------ _Linux_ 1. [Linux_OS](https://www.linuxtrainingacademy.com/linux-commands-cheat-sheet/) 2. [Cheatography](https://cheatography.com/davechild/cheat-sheets/linux-command-line/) 3. [Guru99](https://www.guru99.com/linux-commands-cheat-sheet.html) _Jupyter Notebook_ 1. [Datacamp](http://datacamp-community-prod.s3.amazonaws.com/21fdc814-3f08-4aa9-90fa-247eedefd655) 2. [Cheatography](https://cheatography.com/weidadeyue/cheat-sheets/jupyter-notebook/pdf_bw/) 3. [Edureka](https://www.edureka.co/blog/wp-content/uploads/2018/10/Jupyter_Notebook_CheatSheet_Edureka.pdf) 4. [Anaconda](https://www.anaconda.com/wp-content/uploads/2019/03/11-2018-JupyterLab-Notebook-Cheatsheet.pdf) _ScikitLearn_ 1. [DataCamp](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf) 2. [Edureka.co](https://d1jnx9ba8s6j9r.cloudfront.net/blog/wp-content/uploads/2018/09/Python-Cheat-Sheet-for-Scikit-learn-Edureka.pdf) 3. [Intellipaat](https://intellipaat.com/mediaFiles/2018/12/Sklearn-cheat-sheet.pdf) 4. [Cheatography](https://cheatography.com/sati/cheat-sheets/scikit-learn-cyber-security/pdf/)    _GitHub_ 1. [BitBucket](https://www.atlassian.com/git/tutorials/atlassian-git-cheatsheet) 2. [Freecodecamp](https://www.freecodecamp.org/news/git-cheat-sheet/) 3. [Gitlab](https://about.gitlab.com/images/press/git-cheat-sheet.pdf) 4. [GitHub](https://education.github.com/git-cheat-sheet-education.pdf) 5. [Git_Training](https://training.github.com/downloads/github-git-cheat-sheet/) 6. [Intellipat](https://intellipaat.com/blog/tutorial/devops-tutorial/git-cheat-sheet/)  _Pytorch_ 1. [Pytorch.org](https://www.sznajdman.com/wp-content/uploads/2018/02/pytorch-cheat.jpg) 2. [Pytorch.org (for beginners)](https://pytorch.org/tutorials/beginner/ptcheat.html) 3. [KDNuggets](https://www.kdnuggets.com/2019/08/pytorch-cheat-sheet-beginners.html) 4. [Cheatography](https://cheatography.com/deleted-101890/cheat-sheets/monk-ai-cheatsheet) 5. [Github](https://github.com/bfortuner/pytorch-kaggle-starter) 6. [ProgrammerSought](https://www.programmersought.com/article/54932176906) 7. [TechRepublic](https://www.techrepublic.com/article/how-to-learn-pytorch-a-resources-guide-for-developers)  **Final Thoughts** So that was my effort in segregating and collecting the best cheat sheets on various Data Science facets. I hope it helps and will keep updating this blog with newer and better cheat sheets. Please feel free to add in your views and suggestions for the same and I promise to keep an eye on each of them and add them to the list above! Will be soon back with another useful blog on Data Science! **Till then..** Happy Learning!! Cheers :)
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