Subject archive for "best-practices," page 2
Best Practices for Managing Data Science at Scale
We recently published a practical guide for data science management intended to help current and aspiring managers learn from the challenges and successes of industry leaders. This blog post provides a distilled summary of the guide.
By Mac Steele3 min read
Emotional Intelligence for Data Science Teams
We surveyed and interviewed some of our most successful customers to learn how they align their data science team with their business. The challenges involved are not technical, which is why the solutions may not come easy to data science practitioners and managers.
By Kimberly Shenk7 min read
Principles of Collaboration in Data Science
Data science is no longer a specialization of a single person or small group. It is now a key source of competitive advantage, and as a result, the scale of projects continues to grow. Collaboration is critical because it enables teams to take on larger problems than any individual. It also allows for specialization and a shared context that reduces dependency on "unicorn" employees who don't scale and are a major source of key-man risk. The problem is that collaboration is a vague term that blurs multiple concepts and best practices. In this post, we clarify the differences between repeatability, reproducibility, and whenever possible the golden standard of replicability. By establishing best practices of frictionless in-team and cross-team collaboration, you can dramatically improve the efficiency and impact of your data science efforts.
By Eduardo Ariño de la Rubia17 min read
Introducing the Data Science Maturity Model
Many organizations have been underwhelmed by the return on their investment in data science. This is due to a narrow focus on tools, rather than a broader consideration of how data science teams work and how they fit within the larger organization. To help data science practitioners and leaders identify their existing gaps and direct future investment, Domino has developed a framework called the Data Science Maturity Model (DSMM).
By Mac Steele2 min read
Join Us: An Introduction to Using k-NN in Production
Join us next Wednesday, October 5 for a webinar hosted by our Chief Data Scientist covering best practices for using k-NN in production.
By Sheila Doshi1 min read
The "Joel Test" for Data Science
It's the sixteenth anniversary of Joel Spolsky's "Joel Test," which he described as a "highly irresponsible, sloppy test to rate the quality of a software team."
By Nick Elprin7 min read
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