Essential Data Science Reference Guides for Quick Recall

As a data scientist, having quick access to helpful reference materials can be an invaluable asset. Reference guides provide concise summaries of key concepts, formulas, code snippets, and more - allowing you to refresh your memory or find information quickly without having to delve into lengthy textbooks or manuals.

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In this post, we've compiled some of the most essential data science reference guides that can serve as handy resources for professionals at all levels.

The Python Data Science Cheat Sheet

This comprehensive cheat sheet from DataCamp covers a wide range of Python essentials for data science, including:

- Pandas data structures and functions

- Matplotlib visualization tips

- NumPy arrays and functions

- Scikit-learn machine learning algorithms

- And more

The cheat sheet is available in both one-page and two-page versions, making it easy to print out and keep nearby.

The SQL Quick Reference

Mastering SQL is a crucial skill for many data roles. This SQL cheat sheet from KDnuggets provides a handy reference for common SQL statements, clauses, functions, and data types. It also includes syntax examples for popular SQL dialects like MySQL, PostgreSQL, and Oracle.

The Probability and Statistics Primer

Data science heavily relies on probability and statistics concepts. This reference guide from Stanford covers key formulas, distributions, and statistical tests that data scientists should know, like:

- Bayes' Theorem

- Central Limit Theorem 

- Confidence intervals

- Hypothesis testing

The Machine Learning Algorithm Comparison

Understanding the different machine learning algorithms and when to apply them is critical. This reference guide from Stanford ML Group summarizes the characteristics, use cases, and pros/cons of popular ML algorithms like linear regression, decision trees, k-nearest neighbors, and more.

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The Data Cleaning Handbook

Data cleaning and preparation is a major part of any data scientist's work. This reference guide from Springboard covers common data cleaning techniques and best practices, including handling missing values, removing duplicates, handling outliers, and transforming data.

The Tableau Function Reference

For data visualization practitioners, this Tableau reference guide from The Data School provides a handy summary of Tableau functions, chart types, keyboard shortcuts, and other useful tips.

Having these essential data science reference guides on hand can help you work more efficiently, refresh your knowledge quickly, and become a more well-rounded data professional. Bookmark them, print them out, or keep them easily accessible as you tackle your next data science project.