When I wrote about the Jackalope’s return and the second edition of Advanced Data Science and Analytics with Python, I hinted that this wasn’t just a light refresh. It’s a proper evolution. New chapters, new tools, and, perhaps most importantly, a stronger emphasis on how we trust the models we build.
One of the chapters I’ve been spending time with recently dives head-first into forecasting. Not the hand-wavy, crystal-ball-gazing sort (sadly no actual precogs were harmed in the process), but practical, defensible forecasting that you can deploy without fear of your future self cursing your name.
Enter the Prophet
Yes, that Prophet.
Facebook’s (now Meta’s) Prophet framework gets its own dedicated treatment. Not because it’s fashionable, but because it occupies a genuinely interesting space: expressive enough to handle real-world seasonality, trends, and holiday effects, yet accessible enough that you don’t need to disappear into a cave with nothing but state-space equations and a beard.
The chapter walks through:
- How Prophet decomposes time series into trend, seasonality, and effects you can actually explain to stakeholders
- When it works beautifully; and when it really, really doesn’t
- Why it’s often a strong baseline, even if you later graduate to more exotic architectures
Think of Prophet as the Millennium Falcon of forecasting: not the newest ship in the galaxy, occasionally held together with duct tape, but astonishingly reliable in the right hands.
The Bit Everyone Skips (and Shouldn’t)
Forecasting models are easy to build. Evaluating them properly is where things usually fall apart. So this chapter leans hard into time series cross-validation and forecast evaluation. No random shuffling. No accidental peeking into the future. No Schrödinger’s test set.
We cover:
- Rolling and expanding windows (and why they matter)
- Forecast horizons and why “one-step ahead” tells only half the story
- Metrics that actually align with decision-making, not just leaderboard vanity
If you’ve ever had a model that looked flawless in development and then collapsed in production like a soufflé near a subwoofer, this section is for you.
In applied data science, forecasting sits at an awkward crossroads. It’s everywhere — demand planning, operations, finance, healthcare, energy — and yet it’s often treated as a dark art or an afterthought.
This chapter is about demystifying that space. About treating time seriously (literally), respecting causality, and building forecasts you can defend in a meeting without resorting to interpretive dance or “the model felt confident”.
This is just one chapter. Over the coming weeks, I’ll be writing about other additions and revisions in the second edition — from modern modelling techniques to deployment considerations, and a few opinionated takes on where data science education often goes wrong.
If this chapter is about seeing the future, the rest of the book is about making sure you survive it — preferably with clean code, reproducible results, and fewer existential crises.
More soon. 🛸📈