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Your model is solid. Your statistics are sound. But your environment just broke, your project is a folder of untitled notebooks, and the code that ran fine yesterday won't run today.

Most data science programs teach the math, the models, and the domain. Almost none teach how to set up the machine underneath it all, structure a project so it survives six months, or build a workflow that runs identically on your laptop, your teammate's machine, and in production. That missing half is where data scientists lose days, miss deadlines, and quietly wonder whether everyone else has this figured out.

They don't. They just hit the same walls earlier, or they had a mentor.

Data Science Bootstrap is that mentor. It distills a decade of hard-won infrastructure lessons, battle-tested since I left bench science for computational work in 2013, into a short guide you can read in a weekend. You get the four philosophies that turn chaos into compounding productivity, the exact tools and configs to set up a real machine, a project structure that scales, the core skills that make your work hold up, and the modern AI-augmented workflow that lets you move at the speed of thought.

What's inside:

The four philosophies that turn chaos into compounding productivity, so each new project starts further ahead than the last.
How to configure a Mac or Linux machine for data science in an afternoon, with a dotfiles repo you can version and replicate anywhere, which means a new machine or a new teammate takes hours, not days.
Reproducible environments with pixi that end \"it works on my machine,\" so your code runs identically on your laptop, your colleague's machine, and in CI.
A project structure that scales from a throwaway script to production, which means future-you isn't lost in a swamp of untitled notebooks.
Shell mastery that turns twenty keystrokes into two, so the boring stuff stops eating your day.
How to write tests for data science code and models, so you catch breaks before they reach production and change your code without fear.
The modern AI-augmented workflow: repository memory with AGENTS.md, reusable skills, and agentic data science with marimo, so your coding agents compound in leverage instead of starting from zero.
CI/CD automation that builds, tests, and ships while you sleep, which means the drudgery disappears and you focus on the work that matters.

Why the eBook, when the knowledge base is free?

The online version will always be free; that's the point of the project. The eBook gives you the same content linearized into a focused read, portable offline in PDF and EPUB, and updated free for as long as I maintain it. It's also the most direct way to support the hundreds of hours behind it.

Who wrote this:

I'm Eric Ma, Senior Principal Data Scientist at Moderna, where I lead the Data Science and AI Research team. MIT ScD, formerly biomedical data science at Novartis, a past core developer on NetworkX and PyMC, and creator of pyjanitor and nxviz. Every practice here was forged in real projects, real collaborations, and real deadlines, not in a classroom.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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