momer

There are a number of good introductory SQL resources available for free and online. There are also some paid resources which I recommend for beginners, that are very effective, and well worth expensing in my opinion.

A couple of notes:

• I haven’t used all of these resources, but they come with strong recommendations around the web or myself/my peers.
• You absolutely don’t need to use every single resource. Find a couple that work for you, and go to town.
• You can always reach out to me if you have questions. I always paste this online when people are new to asking very technical questions – it’s not meant to be snarky – it's a gentle guide on how to compose your questions and gather necessary resources in order to best give technical people the information needed to get a quick/effective response: http://www.mikeash.com/getting_answers.html

Video/Class/Mini-course based:

1. Stanford Self-paced ‘Database’ course

• The original Coursera

ericmjl

UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli

How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!