Cheat Sheets for Data Science Learning

This repository is a comprehensive resource for Data Science Cheat Sheets, specifically designed for Python and R.

These Cheat Sheets offer a multidimensional approach to learning, available in three distinct formats:

PDF	Streamlit	Google Colab

Sections

Python

Python, an easily readable high-level programming language, was created by Guido van Rossum in 1991.

Numpy

NumPy is Python's essential library for scientific computing, offering high-performance multidimensional arrays.

Pandas

Pandas, built on NumPy, offers user-friendly data structures and analysis tools for Python.

Matplotlib

Matplotlib is a Python library for creating high-quality 2D plots in various formats and interactive platforms.

Scikit-Learn

Scikit-learn is an open-source Python library that offers a unified interface for machine learning algorithms.

Polars

Polars is an efficient DataFrame library for working with structured data (written in Rust).

Dplyr

dplyr is a data manipulation tool that offers a consistent set of actions for solving common data manipulation tasks.

Ggplot2

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics.

Forcats

forcats package is to provide a suite of tools that solve common problems with factors (in R).

🔑 Note: The PDF cheat sheets in this repository are created by various contributors and have inspired the content presented here.