Q. What is/are your specialist tech area(s)?
Daniele: Advanced Business Analytics, Machine Learning.
Q: How did you become an author for Packt? Tell us about your journey. What was your motivation for writing this book?
Daniele: In my experience as an educator at universities and business schools, I have often seen difficulties in working with Time Series (as opposed to the more standard cross-sectional data), especially in properly addressing the entire analysis process from preparing/describing the data to developing a reliable forecasting model. When I was approached by a colleague about the possibility of writing a book on Time Series with Packt, I thought it would be a good opportunity to share my knowledge on the subject and provide readers with a set of practical guidelines to follow in order to perform a proper Time Series Analysis.
Q: What kind of research did you do, and how long did you spend researching before beginning the book?
Daniele: In recent years I have worked extensively in the field of time series analysis, both as an educator and as a consultant in structured demand planning and business forecasting projects. During this time I have therefore collected a lot of material on the subject: books, use cases, papers, articles, scripts, etc. So I took the best I’ve seen on the subject and tried to condense it into the chapters of the book I wrote for Packt.
Q: Did you face any challenges during the writing process? How did you overcome them?
D: I am very slow to write because I always think there is a better way to communicate a concept than how I wrote it… so I tend to rewrite each sentence a thousand times. Thankfully at Packt, they are very patient!
Q. What’s your take on the technologies discussed in the book? Where do you see these technologies heading in the future?
D: I can say that I use the technologies presented in the book every day. Codeless machine learning is really a powerful tool to speed up the development of complex projects, to have an easy-to-view overview of the whole process, and to share methods and results with colleagues. I often turn to scripting for specific applications, I won’t deny it, but a visual tool like KNIME has great advantages when it comes to orchestrating, speeding up, and sharing data science projects.
Q: Why should readers choose this book over others already on the market? How would you differentiate your book from its competition?
D: I think this book is very pragmatic and oriented to real-world applications. In some ways, it succeeds in being a handbook of the process to follow in developing an end-to-end analysis. Many of the books I own dedicated to time series analysis focus a lot on methodologies and theory, which is clearly important. But I must say that there are not many books that deal with time series analysis from a practical point of view.
Q. What are the key takeaways you want readers to come away from the book with?
D: I think the main take way might be the following: Regardless of the industry where you work and the type of data you routinely manage, it is crucial for a business analyst/data scientist to have an understanding of how to approach time series analysis in a practical way, from visualization to forecasting, because this will impact a lot on many applications that you can develop for your company.
Q. What advice would you give to readers learning tech? Do you have any top tips?
D: Even if you’re a habitual user of Python or R, give codeless data science a chance…it might surprise you!
Q. How would you describe your author journey with Packt? Would you recommend Packt to aspiring authors?
D: Nice people, very accurate in their job.. and very patient!
You can find Daniele’s book on Amazon by following this link: Please click here.