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Data science is a vast field that combines elements of statistics, machine learning, and data analytics. To navigate this complex domain, having a set of useful cheat sheets can be of great help.
Cheat sheets can also serve as a valuable resource to prepare for technical interviews, review key concepts, and provide an overview for beginners beginning their careers in data science.
Here are five super cheat sheets every data science enthusiast and professional should have:
Link: Data Science Cheat Sheet/Data Science Cheat Sheet.pdf
This comprehensive 9-page reference covers the basics of probability, statistics, statistical learning, machine learning, big data frameworks, and SQL. Ideal for those with a basic understanding of statistics and linear algebra, it is a great starting point for anyone diving into data science.
Link: CME 106 (stanford.edu)
This cheat sheet is a concise summary of key concepts in probability and statistics. Includes topics such as random samples, estimators, the central limit theorem, confidence intervals, hypothesis testing, regression analysis, correlation coefficients, and more. It is perfect for understanding fundamental statistical concepts that are crucial in data science.
Link: aaronwangy/Data-Science-Cheat Sheet
This cheat sheet is a condensed version of data science knowledge, covering more than a semester of introductory machine learning based on MIT Machine Learning Courses 6.867 and 15.072. It covers topics such as linear and logistic regression, decision trees, SVM, K-nearest neighbors, and more. The cheat sheet is a valuable resource for exam review, interview preparation, and a quick refresher on key machine learning concepts.
Link: afshinea/stanford-cs-229-machine-learning
This cheat sheet summarizes the key concepts covered in Stanford's CS 229 machine learning course. Includes updates on related topics (probability and statistics, algebra and calculus), detailed cheat sheets for each field of machine learning, and a definitive compilation of important concepts. It is an essential resource for anyone interested in delving deeper into machine learning. It is designed for experts and provides a quick reference of basic concepts.
Link: afshinea/stanford-cs-230-deep-learning
If you are interested in deep learning, Stanford's CS 230 course has an excellent collection of materials that cover everything you need to know about convolutional neural networks and recurrent neural networks and offers tips for training deep learning models. This resource is invaluable for anyone focusing on the deep learning aspect of data science and it's FREE.
These cheat sheets offer a concise and effective way to review and strengthen your understanding of the data science disciplines. From the basics of statistics to the complexities of machine learning and deep learning, these resources are invaluable for students, professionals, and enthusiasts alike. Refer to them frequently to solidify fundamental concepts or review the latest methodologies.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master's degree in technology Management and a Bachelor's degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
