A collection of learning resources

Most of my career in technology has been about self-teaching - SQL, Python, data modeling, data engineering, product management, etc. This is a collection of resources that i've found particularly helpful, organized by topic. This is mostly focused on SQL, Python and data related topics since that has been my focus for the past few years.

Learning SQL

• Mode's free SQL course

  • When I was first learning SQL this course from Mode was my jumping off point. I cannot stress how much I love this 🤑 FREE 🤑 set of online learning resources from Mode. They also have a Python course and some follow up problems to their SQL course that are more analytical / business focused

• SQL murder mystery

  • A really fun way to practice your SQL skills that makes a game out of learning. You do need a little bit of SQL experience for this one

• CoRise beginner, intermediate, or advanced SQL (depending on your level)

  • If you have funds for learning the classes at CoRise are hyper specific and you will learn with a cohort group. I have found them really helpful for advanced SQL and data modeling specifically

• 8 week SQL challenge

• Windows functions

  • This is specifically for postgres, but it's good to understand the concepts of windows functions as you become more experience. they can really improve your queries. I cannot imagine my life in SQL without windows functions

• Count's ultimate SQL guide

Learning Python

• DataCamp or Dataquest

  • two online paid resources for doing project based learning specifically for data science, data engineering, data analytics, etc

• Treehouse

  • this is my personal fave in terms of the more "traditional" online coding platforms (like codeacademy)

• Python for data analysis

• Any of the Python books from No Starch Press

Really, for Python I started with a few courses here and there but it really hit for me when I started doing my own projects. I started with picking apart and analyzing some public data sets from WPRDC and then moved on to more "complex" things like building teeny apps that utilize APIs and things like that.

Learning data & analytics engineering

• Learn Analytics Engineering

  • Here's a good article to start with

• Analytics Engineers Club 

  • If you have $$ or learning & development funds to spend this is a good async/at-your-own-pace resource

• Analytics Engineering Roundup 

  • I think this has gone kind of stale, but the articles from the past are good

• Data pipelines pocket reference 

  • This is a great reference for data engineering basics

• https://awesomedataengineering.com/ 

Reading about data related things

• Data analysis journal

  • I absolutely love this substack by Olga Berezovsky. It's a great resource for folks in all stages of their data journey.

• https://benn.substack.com/

  • spicy, moderately spicy, and interesting takes about the data space

• Counting stuff

• Data patterns

• Choosing the best data visualization to communicate your data (an interactive site)

• Explaining joins

Stats

• https://www.openintro.org/book/stat/

Since I wasn't trending too much in the data scientist direction I didn't focus a lot on stats/math and just picked a few topics that I thought would be useful to my job. Things like:

• a/b testing

• linear regression

• forecasting with Python

• mean/median and other "basic" math related topics

Data careers

• Brooklyn Data data team roles

  • I love this breakdown of data team roles by Brooklyn Data. When I was first trying to figure out what was interesting to me in the realm of data careers this would have been really helpful to understand the nuances between and analyst, and engineer & a scientist

Slack Communities

• Data angels

• Locally optimistic

• dbt