If you’re looking for an email signup form: we abolished our email list in favor of a public Trello board, which contains all of our analysis recipes, tutorials and discount codes to CIFL courses. View it + subscribe here.
Coding is for Losers is an elite group of analysts, executives, marketers and nonprofiteers advancing the idea that working with data need not be a drag.
We believe in recipes: that any data analysis should be repeatable by anyone, regardless of their technical skill.
We work almost exclusively in the Google stack: Google Sheets, BigQuery and Data Studio.
With this stack, we’ve evolved a framework to build data pipelines without much effort - learn more about that here.
We’re grateful to our collaborators in data-land for making CIFL possible:
- The team over at DBT, whose framework for SQL modeling we use on a daily basis.
- The spreadsheet wizards at Supermetrics, whose Sheets Add-on is indenspensible for marketers everywhere
There are five ways (5!) to collaborate with CIFL:
Via the aforementioned public Trello board - which houses all CIFL analysis templates, courses and tutorials. Subscribe here to the board to follow along - feel free to comment or ask questions anywhere on there.
We publish tutorials to our YouTube channel - subscribe to get pinged when we publish new videos.
If you’re interested in diving deeper into learning what we call the ‘Lazy Way,’ there’s a handful of courses covering Google Sheets, Data Studio + building data pipelines available. Our Build your Agency Data Pipeline course includes a 1-on-1 office hours session with yours truly, which are always a great time.
We build data pipelines with our ADP framework, mostly for digital agencies and eCommerce businesses. Holler to firstname.lastname@example.org if you’re interested in implementing one for your business.
Lastly, there exists a super-secret Slack group of followers of the Lazy Way. I can’t say much about it (due to NDAs and such), but you can find the invite link on our Trello board.
We wish you continued serenity in your work with data of all shapes and sizes.