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Cheat Sheets for Data Science Learning

This repository serves as a hub for various Cheat Sheets related to the field of Data Science (with Python and R). What sets these Cheat Sheets apart is their multidimensional approach to enhancing the learning experience. Each Cheat Sheet is made available in three distinct formats:

  • PDF:
  • Streamlit:
  • Google Colab: Open In Colab

Content

Python

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).

R

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 format cheat sheets included here are authored by other contributors and have been used as sources of inspiration for the content presented.

References