ds-cheatsheets

ds-cheatsheets is a curated collection of data-science cheatsheets — single-page reference PDFs and images that summarise the key commands, syntax, and concepts of a tool or topic in a way you can glance at while working. The repository itself is essentially a long, organised table of contents that links out to each cheatsheet, rather than software you install and run.

The cheatsheets are grouped by area so you can jump to the section that matches what you are learning or struggling with. Categories include Business Science workflows, Python (basics, pandas, numpy, Jupyter, regular expressions, importing data), R (the tidyverse, data.table, dplyr, lubridate, stringr, purrr, R Markdown, package development), math and calculus refreshers, probabilities and statistics, big data tools (PySpark RDDs and DataFrames, Dask, sparklyr), machine learning (scikit-learn, caret, H2O, mlr, supervised and unsupervised learning summaries, choosing the right model), deep learning (Keras, neural networks, convolutional and recurrent networks), SQL, and data visualisation (Matplotlib, Seaborn, Bokeh and others).

You would use this repository as a study companion or a quick lookup when you are working on a data-science task and need to remember the exact name of a function or the shape of a syntax. Beginners use it to map out what topics exist in the field; more experienced practitioners use individual sheets as desk references during day-to-day work.

There is no code or runtime here — the repository contains links and PDFs, and many of the cheatsheets are pulled from third parties such as DataCamp, RStudio and Dataquest. The full README is longer than what was provided.