Data Smart, 2nd Edition

12Conclusion

Where Am I? What Just Happened?

You may have started this book with a rather ordinary set of skills in math and spreadsheet modeling. But if you made it this far, that means you made it out alive. I imagine you’re now a spreadsheet modeling connoisseur with a good grasp in a variety of data science techniques.

This book has covered topics ranging from classic operations research fodder (optimization, Monte Carlo, and forecasting) to unsupervised learning (outlier detection, clustering, and graphs) to supervised AI (regression, decision stumps, and naïve Bayes). You should feel confident working with data at this higher level.

And if there’s a particular topic that really grabbed you in this book, dive deeper! Want more R, more optimization, more machine learning? The world is your oyster! There are so many ways to learn from books to online courses to just creating your own project. I’ve only scraped the surface of analytics practice in this book.

But wait…

Before You Go-Go

I want to use this conclusion to offer up some thoughts about what it means to practice data science in the real world, because merely knowing the math isn’t enough.

In the book’s first edition, John Foreman wrote this:

• Anyone who knows me well knows that I’m not the sharpest knife in the drawer. My quantitative skills are middling, but I’ve seen folks much smarter than I fail mightily at working as analytics professionals. The problem is that while they’re brilliant, they don’t know the ...