SQL 

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A note on learning SQL; SQL is easy to pick up but deceptively complex. You can pick up the basic syntax of SQL in an afternoon by reading one of the SQL style guides below and working through the Mode SQL tutorial. As a hiring manager, the mark of someone who has progressed beyond the basics and on to intermediate and more advanced skill is someone who can wrangle messy data in SQL, write in clean, legible CTEs, and has an intuition for modeling complex data. You can only gain better SQL skills by writing more SQL and encountering harder and harder challenges, soI recommend opening a free BigQuery instance and playing around. 

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Mazur’s Style Guide (the one that most people use)

https://github.com/mattm/sql-style-guide

 

Brooklyn Data Co Style Guide (my preferred style guide - it's so opinionated and good)

https://github.com/brooklyn-data/co/blob/main/sql_style_guide.md

 

Mode SQL Tutorials

If you are just starting, begin here. It is the best beginner SQL tutorial on the Internet. 

https://mode.com/sql-tutorial/

 

Mystery SQL Tutorial 

https://mystery.knightlab.com/walkthrough.html

 

Aaron’s SQL Tutorials

https://github.com/ABZ-Aaron/SQL-Tutorials

 

Learn CTEs

https://learnsql.com/blog/cte-with-examples/

https://www.sqlservertutorial.net/sql-server-basics/sql-server-cte/

https://learnsql.com/blog/sql-subquery-cte-difference/

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Terminal/Command Line

I expect everyone on my team to be comfortable with the command line. Not only is it a prerequisite for using git and dbt, but it will make you a better data analyst. Your more senior comrades and coworkers will expect you to be able to navigate your computer from a terminal and to be able to perform basic operations, such as making a director or opening a file. If you think the terminal is something out of a 1990s hacker movie, take a moment to work through these exercises. 

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Learn Enough Command Line to Be Dangerous

https://www.learnenough.com/command-line-tutorial/basics

 

Introduction to the Bash Command Line

https://programminghistorian.org/en/lessons/intro-to-bash

 

Launch School Command Line Book 

https://launchschool.com/books/command_line/read/introduction

 

Terminal Cheat Sheet

https://gist.github.com/cferdinandi/ef665330286fd5d7127d

 

Viking Code School Command Line Crash Course 

https://www.vikingcodeschool.com/web-development-basics/a-command-line-crash-course

 

Crash course

https://cglab.ca/~morin/teaching/1405/clcc/book/cli-crash-course.html

 

CI/CD

Continuous integration (CI) and continuous deployment (CD) are two software engineering best practices that I think data analysts should take advantage of more often. They often go hand in hand, but I have found CI to be more common than CD on data teams. 

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CI/CD Pipeline: A Gentle Introduction 

https://semaphoreci.com/blog/cicd-pipeline

 

dbt

It is no secret that I am a dbt fan. I started using dbt when I was the Data Director of Sunrise and never went back. I refuse to work without it. I think more organizations and campaigns on the left should use this tool. 

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What is dbt 

https://www.getdbt.com/blog/what-exactly-is-dbt/

 

Style Guide

https://github.com/dbt-labs/corp/blob/main/dbt_style_guide.md

 

From worst to first, revamping your dbt project to be world-class

https://www.youtube.com/watch?v=v26R0EgNK44&ab_channel=dbt

 

dbt Discourse 

https://discourse.getdbt.com/

 

dbt Guide

https://about.gitlab.com/handbook/business-technology/data-team/platform/dbt-guide/

 

* How we structure our dbt Projects

https://docs.getdbt.com/guides/best-practices/how-we-structure/1-guide-overview

 

Don’t nest your curlies 

https://docs.getdbt.com/docs/building-a-dbt-project/dont-nest-your-curlies

 

Jinja docs 

https://jinja.palletsprojects.com/en/2.11.x/

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Python 

I believe that every data analyst should know SQL and one scripting language. In my opinion, Python is the best language to learn for its ease and versatility. With Python, you can move data between tools and databases, automate tedious tasks, and interact with APIs. See this lovely article by the GOAT Michael Kaminsky.  

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RealPython

https://realpython.com/

Use your professional development budget to buy a license. They have great Python tutorials 

 

What does Pythonic code look like?

https://docs.python-guide.org/writing/style/

 

Object Oriented Programming Tutorial 

https://realpython.com/learning-paths/object-oriented-programming-oop-python/

 

Time complexity 

https://wiki.python.org/moin/TimeComplexity?

 

Codewars

https://www.codewars.com/collections/python-test-1

 

Think Like a Computer Scientist

https://openbookproject.net/thinkcs/python/english3e/

 

Learn Python the Hard Way

https://learnpythonthehardway.org/book/

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Data engineering

I adamantly believe that most organizers over index on data analysis and under index on data engineer. Data engineering is what allows you to move data through your tech stack and into the hands of organizers. It's what allows you to bypass the limitations of your CRM to deliver quality, accurate data to your key stakeholders. Data engineering is one of the most on-demand skills in progressive politics. We don't need more dashboards, we need more robust, well-tested pipelines. 

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Austin Weisgrau's data engineering resources for the left

https://austinweisgrau.github.io/data-engineering-resources.html

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Parsons (open source engineering library for lefty/progressive tools)

https://github.com/move-coop/parsons

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Intro to REST APIs

https://www.restapitutorial.com/

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Requests package documentation

https://docs.python-requests.org/en/master/

 

HTTP tutorial

https://www.freecodecamp.org/news/http-and-everything-you-need-to-know-about-it/

 

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Warehouses 

Civis is not a data warehouse. Civis is a wrapper around a Redshift data warehouse that facilitates ease of use. Good engineers know about their warehouses: how data are stored in them and how to extract better performance from them. If you use Redshift, you should read the Redshift documentation front to back. Same advice goes for BigQuery. 

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Michael Kaminsky's series on data warehouses

https://www.fivetran.com/people/michael-kaminsky

Michael is my favorite expert on data warehouses and I find their teaching style to be really intuitive and easy to grok. Michael wrote a couple of articles for FiveTran on data warehouses that I highly recommend.

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Modeling data

This section is a blend of my SQL and dbt sections. Analytics engineers are expected to have strong data modeling skills - that is, the ability to take raw data and transform it into business-facing logic. For example, the raw tables that are loaded into your warehouse have little meaning to your Political Director, but with some data modeling you can turn those raw tables into models that have valuable insight (and may be used for a report or dashboard). Below are a few of my favorite reads on data modeling. 

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What is an OLAP cube?

https://analyticsengineers.club/whats-an-olap-cube/

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Dimensional modeling

https://docs.getdbt.com/terms/dimensional-modeling

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Building a Kimball dimensional model with dbt

https://docs.getdbt.com/blog/kimball-dimensional-model

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Kimball in the context of the modern data warehouse: what's worth keeping, and what's not

https://www.getdbt.com/coalesce-2020/kimball-in-the-context-of-the-modern-data-warehouse-whats-worth-keeping-and-whats-not/

 

Slowly changing dimension

This is a fun data concept that I think every data analyst should know!

https://en.wikipedia.org/wiki/Slowly_changing_dimension#Type_2:_add_new_row

 

Git/GitHub

Version control is a must for advanced data teams. As a data professional, you may be expected to collaborate with your team via git and GitHub. If you don't have one already, I highly suggest making a GitHub account and showcasing some of your work in a portfolio repo. Here are some resources to help you master git. 

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git for the rest of us 

https://www.youtube.com/watch?v=mGSecJDvtUQ&ab_channel=dbt

Claire Carroll is the GOAT and is the single best teacher of git that I've ever come across. 

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Git guide 

https://github.com/dbt-labs/corp/blob/main/git-guide.md

 

How I build a Feature

https://simonwillison.net/2022/Jan/12/how-i-build-a-feature/

 

How to create a Pull Request 

https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request

 

Git hand book  

https://guides.github.com/introduction/git-handbook/

 

Introduction to git flow

https://guides.github.com/introduction/flow/

 

GitHub Hello World Tutorial

 https://guides.github.com/activities/hello-world/