> For the complete documentation index, see [llms.txt](https://irosyadi.gitbook.io/irosyadi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://irosyadi.gitbook.io/irosyadi/data-engineering/data-science-resource.md).

# Data Science Resources

## Awesome Data Science

* [Awesome Data Science](https://github.com/academic/awesome-datascience)

## Data Science Learning

* [Data Science Learning Roadmap](https://www.freecodecamp.org/news/data-science-learning-roadmap/)

## Data Science

* [Lessons Learned from two years as a Data Scientist](https://dawndrain.github.io/braindrain/two_years.html)

## Data Science

* [r0f1/datascience: Curated list of Python resources for data science.](https://github.com/r0f1/datascience)

## Data Science

* [Essential Training Data Guide](https://lionbridge.ai/training-data-guide/)

## AI Blog

* [Data Science Weekly Newsletter Archive Data Science Weekly](https://www.datascienceweekly.org/newsletters)

## Data Science Course Notes

* [**MIT Deep Learning**](https://deeplearning.mit.edu/)–Lecture notes, slides and guest talks about deep learning and self driving cars
* [**Introduction to Artificial Intelligence from UC Berkeley**](https://inst.eecs.berkeley.edu/~cs188/fa18/)–lecture notes, slides, homework from the Fall 2018 course
* [**Deep Learning Course from University of Paris-Saclay**](https://m2dsupsdlclass.github.io/lectures-labs/)–lecture notes and Python code
* [**Stanford Machine Learning**](https://github.com/afshinea/stanford-cs-229-machine-learning)–cheatsheets from Stanford’s CS 229 machine learning course, translated in multiple languages