As some of you may know, last year I took a sabbatical and focused on my authoring efforts, in order to produce a book on Julia (Julia for Data Science book, by Technics Publications). Actually it wasn’t a full sabbatical as I had to do some odd jobs here and there to pay for the overpriced rent and other expenses of the city of Seattle! That’s not to say that I didn’t enjoy the whole process though. I particularly loved working as a data scientist for the 3-month contract job at G2 Web Services, even more than I enjoyed working in other data analytics positions over the years. However, I found that it was particularly challenging to do a good job in the book, while having a full-time job. That’s because, unlike other publishers, Technics Publications focused (and still focuses) on quality rather than quantity, so I had to make sure that whatever I wrote was worth the ink and paper it was going to use when it was going to be published.
Writing a book on an evolving technology like Julia wasn’t an easy feat, which is probably why Technics Publications was the first non-trivial publisher to finish such a project (even though there were other more well-known publishers that were attempting the same thing while I was writing this book, such as O’Reily’s with Leah Hansen, one of the Julia gurus, whom I followed over the years through her blog). However, writing yet another book on Julia wasn’t appealing to me for two reasons. First of all, I wasn’t a developer so there were some more esoteric aspects of the language that I wouldn’t be able to explain properly. Secondly, people in my field are more interested in the usefulness of a technology, particularly how it can be used to crunch data effectively and efficiently. Since I had some expertise on data science I decided to write yet another data science book, geared towards the Julia language.
This whole endeavor was tough but educational for me. I had an opportunity to go deep into the technology, always being up-to-date with the latest trends, and interacting with some of the more experienced users (who were very happy to help out, by the way, something I hadn’t encountered while learning other technologies, for example). Also, I got to write several Julia scripts and get acquainted with the IDEs in a way that would have been impossilbe otherwise. Because, it’s only when you try to explain something to someone who’s never heard it before, that you really get to know it yourself.
“Was it worth it?” you may ask. Well, for me it was. I wouldn’t do the same thing again though for yet another book on R or Python though, since I would have a hard time being motivated to do so. After all, Julia was a bit of a gamble back then (even though I had no doubt that it would become better and more popular as time went by). Things could have gone awry and the book would have been all for nothing. If I would do my best though it might just help this technology become more well-known. It was a risk but a calculated one at that.
So, if you are thinking about writing a book yourself in some data science technology or methodology, here is my advice: don’t talk to too many people but do talk to the ones who know. Form a concrete idea of what the world needs and aim to fulfill that need through your book. It may not be a best seller but it definitely looks good on both your resume and on your bookshelf!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.