data science cheat sheets

Data science encompasses statistics, machine learning, and data analysis. Navigating this expansive field is easier with handy cheat sheets. They’re not only great for technical interviews but also serve as valuable resources, offering a quick overview for beginners and aiding in reviewing key concepts.

Explore the world of data science through comprehensive cheat sheets, condensing intricate concepts into quick references. From statistical foundations to advanced machine learning, these abstracts serve as invaluable resources for both beginners and seasoned professionals, offering a concise and effective means to reinforce understanding and stay updated on the latest methodologies.

Unlock essential knowledge with these five must-have cheat sheets for data science professionals and enthusiasts

1. Data Science Max Pro Cheat Sheet

Link: Data-Science-Cheatsheet/data-science-cheatsheet.pdf

ds max pro cheatsheet

This extensive 9-page guide provides a thorough overview of probability, statistics, statistical learning, machine learning, big data frameworks, and SQL. Tailored for individuals with a foundational understanding of statistics and linear algebra, it serves as an excellent entry point for those venturing into the realm of data science.

2. Probability and Statistics Cheat Sheet by Stanford

Link: CME 106 (stanford.edu)

cme-106

This cheat sheet offers a succinct overview of vital concepts in probability and statistics. Covering random samples, estimators, the Central Limit Theorem, confidence intervals, hypothesis testing, regression analysis, correlation coefficients, and more, it’s an ideal resource for grasping foundational statistical principles essential in the field of data science.

3. Data Science Cheat Sheet 2.0

Link: aaronwangy/Data-Science-Cheatsheet

ds cheatsheet 2.0

This condensed cheat sheet encapsulates a semester’s worth of introductory machine learning from MIT’s Machine Learning courses 6.867 and 15.072. Topics include linear and logistic regression, decision trees, SVM, K-Nearest Neighbors, and more. An invaluable resource for exam reviews, interview preparation, and a swift refresher on crucial machine learning concepts.”

4. Super Machine Learning Cheat Sheet

Link:  afshinea/stanford-cs-229-machine-learning

super machine learning

This cheat sheet distills the core concepts from Stanford’s CS 229 Machine Learning course. Featuring refreshers on key topics like Probabilities and Statistics, Algebra, and Calculus, as well as detailed cheat sheets for each machine learning field, it serves as an indispensable resource for those seeking a deeper understanding of machine learning. Tailored for both experts and beginners, it offers a quick reference for fundamental concepts.

5. Super Deep Learning Cheat Sheet

Link: afshinea/stanford-cs-230-deep-learning

deeplearning cheatsheet

Explore the world of deep learning with Stanford’s CS 230 course materials. Comprehensive coverage of convolutional neural networks, recurrent neural networks, and practical tips for training deep learning models make this resource invaluable for those concentrating on the deep learning dimension of data science — and the best part? It’s free!

Enhance your grasp of data science with these succinct cheat sheets, providing a quick and effective review across various disciplines. Whether revisiting the fundamentals of statistics or delving into the complexities of machine learning and deep learning, these resources are indispensable for students, professionals, and enthusiasts alike. Use them regularly to reinforce foundational concepts and stay abreast of the latest methodologies.