John Foreman, Data Scientist
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The Jackass Who Slapped His Face on His Technical Book

11/17/2013

6 Comments

 
Now that my book is in the wild, I've been getting a lot of questions:
Why does Excel 2007's nonlinear solver crap out on k-means unlike the newer 3 versions of Excel? 
Why bother calculating t stats manually in the book?
If I want to learn more about data science after finishing this book, should I go back to school?

But my favorite question: 
Why in the world is your face on the cover of your book? and upside down to boot?
Picture
I am that jackass.
I acknowledge that slapping my face on Data Smart was absurd. Data Smart is a technical book, and I am no one in particular. I'm no Davy Jones.

But this wasn't done arbitrarily. No, the decision behind it was intentionally made by myself, the cover designer, and the publisher.

The upside-down face is meant to set expectations for the reader. This book has more than a point-of-view -- it has a strong, yet somewhat kooky, narrative presence. 

Strong narrative presence ==> my face.
Some fairly strange content (for example, a playlist of mid-90s club hits) ==> turn it upside down.

The cover is a reflection of the content.

Data Smart is an experiment in narratively heavy-handed, self-conscious technical writing. And so this question about the cover is actually part of a larger discussion on technical books: 
Should you write the kind of technical book that would lead to such a cover?

Point of View versus Narrative Presence

Most, dare I say all, technical books have a point of view. They're written by humans, so the mere decision of what to put in and what to leave out of a text is pointed. Hell, even if the author leaves nothing out of a book then that act of non-editing is pointed.

Take for instance Shifrin's 1995 book: Abstract Algebra: A Geometric Approach. I love this book. It's tough, it's dry as a bone, but the fact that the author chooses to explain things like group theory using geometric language is none-the-less a point of view.

Similarly, in Data Smart, I teach optimization early on and then build on optimization to teach other techniques. A lot of other data science books ignore optimization entirely, simply letting optimization techniques embedded deep in packages silently do the heavy liftying. The fact that I think optimization is important is my point of view.

That said, having a strong narrative presence is about more than just having a point of view. It's about the reader never being able to forget that the author is talking to them. Instead of letting dry words speak plainly for themselves, I am, as the author, constantly jumping in with silly similes (how a Big M constraint is like a dead squirrel), odd data (empire waisted dress sales are a good pregnancy predictor unless we're in Sense and Sensibility), and dogmatic statements about minor aesthetics (Excel's 3d pie charts are the spawn of Satan).

You see this type of authorial presence in movies all the time. Take for instance, the concept of a "Spielberg visual gag," such as the T-rex in the rearview mirror or the librarian stamp scene in Last Crusade:
Picture
These scenes remind you, "Oh yeah, this is a Spielberg film."  The viewer is reminded that they're not alone -- that there's someone who's behind the scenes and has constructed this tale just for the viewer. Some folks hate that kind of thing, because it's distracting. Reminding the viewer of who the author is doesn't advance the plot -- and in my case, it sure doesn't convey technical material more quickly. 

So why do it?

100% Clarity is Overrated

A lot of people feel that a technical book should minimize the author's voice and lay out information in a straight-forward, tight way. I get it.

But I thought about the way I learned math back in college. I read, I worked, and I smoked. I smoked a lot when I studied.

The smoke break was ritual for me when studying tough concepts. It was a Selah that I inserted between reading proofs in my topology textbook. Would it have been more efficient to not take smoke breaks? If I weren't human, sure. But as a person, I could only read about the hairy ball theorem for so long before I needed a break.

And that's what all the stupidity in my book is about. Sure, it inherently slows down the pace and inserts jokes where a clearer sentence would have sufficed. But it's a welcome pause for many readers. A chance to remind the reader that I'm still there with them working through the material. Take a breath, have a laugh, and let's keep moving. 

And who is that reader?

The reader here is important. The audience for this technical book was non-technical. I specifically wanted to teach data science to those folks I felt like were largely ignored by the manifold R and Python based O'Reilly texts floating around. I wanted folks with more of an MBA background to find a foothold in data science.

And those people probably aren't used to dense technical material. At the same time, I didn't want to dumb the text down. There are plenty of "about books" on data science already (Moneyball books as my tech editor calls them). So the only way to talk to these folks without dumbing down subject matter is to make it very clear, use tools they're familiar with, pace the content well, and make it approachable. 

Should more technical books have faces on the cover?

It depends on the audience, but generally, I think yes. 

Recently, we've heard plenty from the media about how the best persuaders, the best educators, the best communicators are good story tellers. Stories activate more of the brain.

Should technical books capitalize on this? I don't see why not. Storytelling, even if it's in the form of approachable, real-world datasets, referential humor, or anecdotes from the writer's own practical use of the techniques, can go a long way to keeping the reader engaged and tracking with you.

I hope I'm proved right on this point. Because putting your face on a book is going a bit further than just plopping your name on front. If people don't like Data Smart, I will forever be the jackass with his face on the front of a 400 page turd. It's a risk, but one that I hope will change the way people think about teaching and learning complex technical concepts.
6 Comments
Anna link
3/23/2014 23:24:22

Hello John,

Thanks a lot for an absolutely amazing book! I
I am not sure if it's ok to ask about some analysis in the book, but if you could comment or hint what i am missing, it would be really great.
I am struggling with the example of k-means analysis on WineKMC dataset and have very simple question: why do we cluster customers in 32 dimensional offer space, but not in 6-7 dimensional space of actual variables that make this 32 space (e.g. wine type, quantity, percentage off, etc)? at the end of the day, isn't it want we are looking at when interpreting kmeans results?

Thanks a lot again for a wonderful book!
Anna

Reply
John Foreman link
3/24/2014 04:15:28

Hey, thanks for reading the book, and great question. You could do it that way. K means would work. Doing the cosine similarity calculation would change slightly if you did actual counts on the descriptive columns (you could have a 3 next to Pinot Noir for example).

Now, because some of these descriptors are categorical (wine type), you'd end up with more than 7 columns probably, because you'd need to split that column out into multiple wine type dummy columns. You'd have for instance a Pinot column AND and Cabernet column.

The other thing (which I don't say in my book) is that if you cluster the actual transactions then when you get a funky cluster you can contemplate whether those items have something in common not captured by the descriptors you have.

Reply
Anna link
3/24/2014 08:08:39

Thanks a lot, John!
yes, also was thinking that categories should be turned into binary format, but your idea of seeing something not in the list of descriptors is really great one - didn't think about it:).

Would it be also correct to think that usage of offers vs descriptors is to some extent driven by the eventual goal of the the analysis (e.g. what we want to learn and do)? For instance, if a client wants to know to whom to send existing offers - it's one thing, but, let's say, if a client wants to construct new offers - that's a different thing and then we could potentially use the descriptors.

thanks a lot again! btw, don't stress about your picture on the front cover - as to me (a complete novice to the field and an ordinary reader who was searching what to read about data science at amazon), the cover looked super cool and reminded of a doctor House (in fact, i thought it was him just upside down:). Thus, the conclusion was "it ought to be great" and bought it. Never regretted;)

TV
3/29/2014 07:14:29

So, why does K-means crap out in Excel 2007? Is something like the Knitro solver a suitable alternative?

Reply
John Foreman link
3/31/2014 05:23:57

Excel 2007 only has the GRG solver, not the evolutionary solver, and unfortunately, the way the GRG solver works it generally hits a local optimum that just shoves every customer into a single cluster from which no one can escape. The evolutionary solver bounces around enough to prevent that. I think something like Knitro would work great if you could get it hooked in, maybe via SolverStudio?

Reply
Arun
5/23/2014 22:04:41

Hi John,

Really enjoying your book.Had to wait for sometime to get the Indian edition.I work a lot on feedback data .Specifically on Chapter 3 is there any better approach to handle comments having N words in it.Since the example in that chpt covers twitter.I work on open ended comments...

Thanks for your time
Arun

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    Hey, I'm John, the data scientist at MailChimp.com.

    This blog is where I put thoughts about doing data science as a profession and the state of the "analytics industry" in general.

    Want to get even dirtier with data? Check out my blog "Analytics Made Skeezy", where math meets meth as fictional drug dealers get schooled in data science.

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