Luca Zavarella is the author of Extending Power BI with Python and R, we got the chance to interview him and find out more about his experience of writing with Packt.
Q: What is/are your specialist tech area(s)?
Luca: Business Intelligence; Advanced Analytics with R and Python; Machine Learning.
Q: How did you become an author for Packt? Tell us about your journey. What was your motivation for writing this book?
Luca: I was contacted by Packt staff via LinkedIn in early December 2020. The initial proposal was to write a book on how to extend Power BI with Python. Initially, I was skeptical, I never thought I would be able to write a book. This is because I know myself very well: I often worry that I always know too little about a specific topic and I know that I am sometimes overly obsessive about details. After a week of second-guessing, I plucked up my courage and said, “Why not! Let’s try!”. Even, given my knowledge of the R language as well, I proposed to extend the topics by including it as an analytical language in addition to Python. What made me accept this opportunity was the realization that my experience in both Advanced Analytics and Business Intelligence could help both the more timid Power BI analysts to get a foot in the door of Python and R, and the analysts with experience in these languages to explore their potential in combination with Power BI. I also accepted this challenge in order to make a contribution to the community of users of analytical languages in Power BI, since there is very little learning material available to them.
Q: What kind of research did you do, and how long did you spend researching before beginning the book?
Luca: Almost all of the topics covered in the book answer the following basic question: “What is possible to do in Power BI with Python and R that is not already possible with the standard Power BI features?”. Therefore, looking for interesting, non-obvious topics that would stimulate the reader was not so easy. For all these reasons, the research of the topics to be covered lasted about 15 days.
Q: Did you face any challenges during the writing process? How did you overcome them?
Luca: Some of the most treacherous challenges I’ve faced have been writing advanced code to implement some innovative ideas never before introduced in Power BI. In one specific case, I even feared that the idea was not implementable. Eventually, though, with hard work and the help of some technical blogs made available by the community, I was able to come up with some winning ideas. As for the content, I started from the topics established in the outline and I enriched them, taking care to explain everything in a simple and straightforward way. I encountered some difficulty in explaining some topics of mathematics and data science while maintaining a simple and fluent language. I hope I succeeded.
Q: What’s your take on the technologies discussed in the book? Where do you see these technologies heading in the future?
Luca: Knowledge of analytical languages such as Python and R allows analysts to enrich their analyses with stunning insights. Now that these languages have also been implemented in Power BI, the analyst can harness their power to achieve truly unexpected results. Within a few years, the use of certain advanced techniques will become more and more commonplace, and therefore their knowledge will be mandatory for the professional analyst.
Q: Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?
Luca: The first thing I thought of was to not write a book of obviousness, as unfortunately often happens in the market. Therefore, I would have covered neither the basics of Python and R, nor the basics of Power BI, since there is already so much material available on the web to learn them. I envisioned myself in reading this book, and thus the reference reader I had while writing it is an analyst with some experience in both of the above languages. Therefore, I thought it was appropriate to set up the content of the book as if it were a sort of “Stack Overflow” on how to get the most out of Python and R with Power BI. In fact, the code you’ll find attached to the book is not trivial at all. Come on, fess up, you too have learned a new technology by starting with code suggested by the community on Stack Overflow and then moving on to methodically study the same technology. Don’t be ashamed, I am one of them! In addition, to address an audience with less experience in the field, in addition to giving functional solutions adaptable to production environments, I thought it appropriate to also provide some indications on the theory behind the implemented code and references to resources that allow the neophyte to deepen the technical issues.
Q. What are the key takeaways you want readers to come away from the book with?
Luca: I tried to organize the code attached to the book so that it can be easily reused in contexts other than those presented. Therefore, the reader will have at his disposal a new toolset of Python and R code chunks that he can inject into his Power BI code very easily. Moreover, each chapter introduces the concepts implemented in the code from a theoretical point of view in a very clear and comprehensive way. This thing gives the reader a much more cohesive overview of the topics covered.
Q. What advice would you give to readers learning tech? Do you have any top tips?
Luca: You should take the approach that best suits our intellectual abilities. But the advice I would certainly give is to follow closely the communities that are dedicated to the technology of our interest on social networks (LinkedIn, Twitter, YouTube). This is the only way to keep up with the latest news and new techniques of the field. And as always, I always encourage people to contribute to the community: just as you learn from the contributions of others, others can do the same from your contributions.
Q. Do you have a blog that readers can follow?
Luca: Yes, sure! You can find it here: https://medium.com/@lucazav.
Q. Can you share any blogs, websites, and forums to help readers gain a holistic view of the tech they are learning?
Luca: Unfortunately, there are few resources on the web that deal with mixing technologies such as Power BI, Python, and R. You have to dig into each one separately and then put together with a little imagination what you’ve learned. There are a few authors who are constantly sharing their experience with Power BI and analytic languages on Twitter. Some of them are Sandeep Pawar (@PawarBI) and Leila Etaati (@leila_etaati). If, on the other hand, you’d like to learn more about the use of the R and Python languages applied to Data Science, I suggest following Matt Dancho’s Business Science blog (https://www.business-science.io/blog/). After that, the advice I always give is to follow the various topics on socials to stay on track.
Q. What are your favorite tech journals? How do you keep yourself up to date on tech?
Luca: I don’t generally rely on a few sources of information to stay current on a specific topic. My strategy is always to search for good articles and blogs on the web and then aggregate their sources via my favorite feed reader (Feedly), or to follow the authors on the blogging platform Medium, on which I have a blog as well.
Q. How would you describe your author journey with Packt? Would you recommend Packt to aspiring authors?
Luca: The experience of writing a book together with Packt was really positive. I really enjoyed the initial approach, from writing the outline to the stylistic approach of the first 4 chapters. After that, the rest of the activities went very smoothly, also thanks to the patience of the team in the face of some small delays in delivery due to unforeseen events.
Q. What is the one writing tip that you found most crucial and would like to share with aspiring authors?
Luca: Writing the book mostly occupied my free time. Therefore, after work hours, I was committed daily to the various intermediate goals. When I have to start a new chapter, I always focus on the topics decided in the outline and from those I start setting the skeleton of the chapter by adding the titles of the sections and subsections. I immediately start writing the introductory part of the chapter and the first sections, which are always theoretical and aim to clarify all the conceptual aspects behind the technical topic to be developed. Finished this first part, I immediately dedicate myself to the implementation of the code by choosing a meaningful scenario. In front of the implementation of the code, I often add subsections that aim to explain the content. After that, I complete the writing of the chapter.
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