[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71901":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":20,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":27,"readmeContent":28,"aiSummary":29,"trendingCount":16,"starSnapshotCount":16,"syncStatus":30,"lastSyncTime":31,"discoverSource":32},71901,"Data-Science-Cheatsheet","aaronwangy\u002FData-Science-Cheatsheet","aaronwangy","A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.","",null,"TeX",5418,754,151,6,0,3,12,39.63,false,"main",true,[24,25,26],"cheatsheet","data-science","machine-learning","2026-06-12 02:02:55","# Data Science Cheatsheet 2.0\n\nA helpful 5-page data science cheatsheet to assist with exam reviews, interview prep, and anything in-between. It covers over a semester of introductory machine learning, and is based on MIT's Machine Learning courses 6.867 and 15.072. The reader should have at least a basic understanding of statistics and linear algebra, though beginners may find this resource helpful as well. \n\nInspired by Maverick's *Data Science Cheatsheet* (hence the 2.0 in the name), located [here](https:\u002F\u002Fgithub.com\u002Fml874\u002FData-Science-Cheatsheet).\n\nTopics covered:\n- Linear and Logistic Regression\n- Decision Trees and Random Forest\n- SVM\n- K-Nearest Neighbors\n- Clustering\n- Boosting\n- Dimension Reduction (PCA, LDA, Factor Analysis)\n- Natural Language Processing\n- Neural Networks\n- Recommender Systems\n- Reinforcement Learning\n- Anomaly Detection\n- Time Series\n- A\u002FB Testing\n\nThis cheatsheet will be occasionally updated with new\u002Fimproved info, so consider a follow or star to stay up to date.\n\nFuture additions (ideas welcome):\n- ~~Time Series~~ Added!\n- ~~Statistics and Probability~~ Added!\n- Data Imputation\n- Generative Adversarial Networks\n- Graph Neural Networks\n\n## Links\n* [Data Science Cheatsheet 2.0 PDF](https:\u002F\u002Fgithub.com\u002Faaronwangy\u002FData-Science-Cheatsheet\u002Fblob\u002Fmain\u002FData_Science_Cheatsheet.pdf)\n\n## Screenshots\n\nHere are screenshots of a couple pages - the link to the full cheatsheet is above!\n\n![](images\u002Fpage1-1.png?raw=true) \n![](images\u002Fpage2-1.png?raw=true)\n\n### Why is Python\u002FSQL not covered in this cheatsheet?\nI planned for this resource to cover mainly algorithms, models, and concepts, as these rarely change and are common throughout industries. Technical languages and data structures often vary by job function, and refreshing these skills may make more sense on keyboard than on paper.\n\n\n## License\n\nFeel free to share this resource in classes, review sessions, or to anyone who might find it helpful :)\n\nThis work is licensed under the \u003Ca rel=\"license\" href=\"http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc-sa\u002F4.0\u002F\">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u003C\u002Fa>\n\n\u003Ca rel=\"license\" href=\"http:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc-sa\u002F4.0\u002F\">\u003Cimg alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https:\u002F\u002Fi.creativecommons.org\u002Fl\u002Fby-nc-sa\u002F4.0\u002F88x31.png\" \u002F>\u003C\u002Fa>\u003Cbr\u002F>\n\nImages are used for educational purposes, created by me, or borrowed from my colleagues [here](https:\u002F\u002Fstanford.edu\u002F~shervine\u002Fteaching\u002Fcs-229\u002F)\n\n## Contact\nFeel free to suggest comments, updates, and potential improvements!\n\nAuthor - [Aaron Wang](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Faxw\u002F)\n\nIf you'd like to support this cheatsheet, you can buy me a coffee [here](https:\u002F\u002Fwww.paypal.me\u002Faaxw). I also do resume, application, and tech consulting - send me a message if interested.\n","该项目是一个5页的数据科学速查表，旨在帮助用户复习考试、准备面试以及快速回顾机器学习基础知识。它涵盖了线性回归、决策树、支持向量机等核心算法和模型，并基于MIT的机器学习课程6.867和15.072内容。该速查表适合需要快速查阅数据科学概念和技术细节的学生、求职者或从业者。由于其简洁明了的设计，对于具备一定统计学和线性代数基础的学习者尤其有用。此外，项目维护者会不定期更新资料，确保信息的时效性和准确性。",2,"2026-06-11 03:39:21","high_star"]