A collection of (mostly) technical things every data scientist should know
This is a
s/programmer/data-scientist/g of every-programmer-should-know by @mtdvio. That meas two things: 1. I take no credit for the idea of creating this page, I just wanted one for data-science and 2. things that are purely related to software development will not be the focus of this page, though there may be limited duplication.
What do YOU think every data scientist should know?
This list is far from complete/correct. Add/remove as you wish...
As in the original, all of the following applies:
Comes in no particular order
P.S. You don't need to know all of that by heart to be a data scientist.
But knowing the stuff will help you become better!
P.P.S. Contributions are welcome!*
📜 📜Theme issue ‘The ethical impact of data science’ Phil. Trans. R. Soc. 📖Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Base Math for Data Science
Open Source / Open Data
📇Machine Learning Flashcards 📖Gaussian Processes for Machine Learning 🔗10 Machine Learning Algorithms You Should Know to Become a Data Scientist 🔗 🏫Cheatsheets for Machine/Deep Learning for Stanford's CS 229
Artificial Intelligence / Neural Networks / Deep Learning
🎥But what is a Neural Network? | Chapter 1, deep learning 🔗Awesome Tensorflow 🏫Stanford Course on Deep Learning for NLP
"Data Constructs" - Data Structures & Relational Databases
"Big Data" Processing/Managment Technologies & Operalization
📜Machine Learning: The High Interest Credit Card of Technical Debt 🔗Hadoop HDFS Architecture Explained 🔗Awesome Big Data
Programming Languages/Software Developement
📖You Don't Know JS 🔗Hyperpolyglot - Programming Languages - commonly used features in a side-by-side format
- Trello Data Science
- Hadley Wickhams - Stats 337: Readings in Applied Data Science
- Open Source Data Science Masters
Blogs/Tweeps you should follow
- the morning paper / @adriancolyer
This work is licensed under a Creative Commons Attribution 4.0 International License.