Philosophies

This book is centered around four core philosophies, which we will cover in this chapter. Internalizing these philosophies will help you understand where I'm coming from.

• Data scientists should strive to know every last detail about their compute stack
• There should be one, and preferably only one, obvious source of truth for things
• Eliminate drudgery by investing in automation
• Organize your projects by leveraging categories

These core ideas were born out of my prior experiences, my wins and losses at work. There, I saw the fruits of my labour flourish because of the adoption of the ideas in this book. But I also witnessed work that I poured time into languishing into oblivion because these foundational practices were absent - when projects lacked clear organization, single sources of truth, automation, and deep stack knowledge.

How these philosophies work together

These four principles aren't isolated ideas - they reinforce each other in powerful ways. When you know your compute stack deeply, you can make informed choices about what to automate. When you establish single sources of truth, categorization becomes natural and maintainable. When you automate effectively, you can focus energy on the problems that truly matter.

Here's what I've found works best: start with one philosophy that resonates with your current pain point, then let the others follow naturally. The beauty of this approach is that each philosophy amplifies the others, creating a compound effect on your productivity and project quality.