This repository contains resources and cheatsheets that should be helpful for anyone learning or practicing data science. Vast majority of the resources is geared towards Python users, but there's a page for R resources (and I'm planning on adding Scala at some point).
- Cheat Sheets - contains a lot of useful cheat sheets for Python, data analysis, machine learning, Git and more.
- Python - contains different Python guides, tips and tricks.
- R - R resources, tutorials, guides, etc., starting with R for beginners through Data Analysis and Visualization to Machine Learning and Deep Learning with R.
- SQL - SQL tutorials, cheat sheets, exercises, videos and courses.
- Data Analysis - Exploratory Data Analysis guides, mostly with Pandas and NumPy.
- Data Visualization - contains various data visualization guides - Pandas plotting, Matplotlib, Seaborn, Bokeh.
- Machine Learning - contains different machine learning guides: supervised learning (regression, classification, tree-based models etc), unsupervised learning (clustering), feature selection, model evaluation, etc.
- Deep Learning - Deep Learning guides, including general guides and tutorials and resources organized by the type of network (CNN, RNN, etc.) and the library (TensorFlow, Keras, PyTorch, etc.).
- Natural Language Processing - contains Natural Language Processing resources: NLTK, SKLearn NLP and more.
- Statistics - contains mostly theoretical reading to deepen your understanding of statistics.
- Mathematics - contains resources for math topics that are relevant for data scientists.
- Datasets - links to interesting datasets.
- Git and GitHub - contains Git and GitHub/GitHub Enterprise resources.
- Command Line Interface - contains Command Line resources.
- Development Environment Resources - contains development environment resources: Jupyter Notebook/Lab, text editors (like VS Code), etc.