HomeAuthor InterviewsInterview with Daniele Tonini

Interview with Daniele Tonini

Daniele is the co-author of Codeless Time series analysis with KNIME. We got the chance to sit down with him and find out more about his experience of writing with Packt.

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.

Codeless Time series analysis with KNIME on Amazon.com