Crunching the Data

We help data scientists increase the impact of their work by ensuring that they are working on the right projects using the right tools and processes. We write about a variety of topics ranging from machine learning and engineering fundamentals to project management and communication skills

Data science best practices

Are you looking for best practices you can implement to increase the impact that your team has? Check out this piece of data science best practices!

Data science project lifecycle

Are you wondering how to structure the lifecycle of a data science project to facilitate alignment and collaboration with stakeholders? Then this is the article for you!

How to choose a ML model

Are you trying to decide which machine learning model to use for your data science project? Check out this comprehensive guide to choosing a machine learning model.

Do you have a specific topic that you would like to learn more about? Use this search bar to see whether we have any articles on that topic!

Gentle introductions to engineering foundations

We provide gentle introductions to engineering foundations that help data teams build analyses that are more reproducible and easier to iterate on.

Version control for data teams
Unit tests for data teams
Duplication in data codebases

Best practices & processes

We help teams introduce the right processes and best practices to ensure they are working on the right projects in a way that makes sense for them.

Scaling the impact of data teams
Knowledge silos in data teams
Data science project proposals
Getting feedback on data projects

Comprehensive guides on machine learning

It is difficult to piece together scattered information from different sources. We create comprehensive guides that help you get all the answers you need in one place.

How to choose a ML model
Baseline models for ML
Evaluating ML models

“It’s nice to have a place to visit for advice on soft skills that matter more in senior positions.”

Kelly Williams

“It is not easy to find websites that help me improve team processes. Much appreciated.”

Tom Rodgers

“I love how comprehensive the info is. The page on choosing the right machine learning model is a great reference.”

Jason Wu