A few weeks ago I had the pleasure of chatting with Marco Santoni and Cesare Placanica from the Python Milano user group, as the first guest of their new Intervista Pythonista, a podcast about the Italian Python community. You can listen to the episode on Anchor/Spotify in Italian.
This blog post offers a very belated summary of the podcast episode for English readers. It’s not a full transcript but it sums up our chat.
Some of the points we discussed (questions from the hosts in bold):
- How did you get into Python? And how do you use it nowadays?
My first encounter with Python was random. At that time I was working as software engineer, mostly on web applications (PHP and JavaScript, long before Angular/React and friends). Someone, probably from a local Linux User Group, mentioned Python and I had a first look. When I got into Natural Language Processing, it felt like a good excuse to study this new (for me) language a bit more and to look into the ecosystem — only NLTK at the time, as far as I remember. Later I used it for my MSc dissertation and throughout my PhD work. In the meanwhile the Python ecosystem for Data Science became more and more robust, and it became my first weapon of choice. Nowadays I use it as my main tool for all my Data Science work, and I teach it at my corporate training courses. - How do you keep your knowledge up to date [with the latest Python developments]?
Through a variety of channels. Conferences and meetups are a great way to see what people are working on, and to be exposed to fresh ideas. These days in-person gatherings are on hold so that part is mostly missing, but different user groups are still producing a lot of good content published on YouTube. Twitter or other social media channels can also be useful to catch up with some Python news, bump into a new library or new articles. Of course it’s difficult to keep up with everything so one has to be a bit selective when it comes to spend time digging into the details. When I’m really interested in a particular topic, after a first look at blogs and tutorials, I’d probably seek something more structured like a book or a video course. - How (and why) did you start a career as solo consultant? Bonus: how do you find your clients?
I started my Data Science consultancy firm in 2015, mainly looking for independence. Taking the first step was simple (register your company online at Companies House and you’re good to go). Over time I’ve learned, and I’m learning, all the other facets of how to run a business, but taking that first step was the crucial moment. On the topic of finding clients, most of my work comes through my network, e.g. via word-of-mouth from people I’ve worked with in the past, or through a recommendation from a person I’ve helped somehow in the past. With this in mind, having a presence at conferences/events and curating your personal network is essential. - How did you get into NLP?
I was working on a search application so I had to learn more. I started learning about Information Retrieval methods like TF-IDF, using off-the-shelf tools. From there, things took off: I later studied Information Retrieval for my MSc and PhD, I developed an interest for Natural Language Processing at large and I’ve been involved in many NLP projects. These days, I work on a broad variety of Data Science projects but NLP remains my favourite topic. - Can you tell us about your experience in writing books?
I wrote one book on Data Mining for Social Media, and developed two video courses on data science so far. It’s a lot of work! The reward is usually not on the financial side, unless you write the next Harry Potter series. The process gave me a lot of insights on the publishing industry as a whole and put me in touch with a variety of professionals in that industry, which was great. I learned bits and pieces related to editing, marketing and all the other steps that one doesn’t think about when starting a book. More importantly, it gave me the opportunity to polish my writing skills, which I think are essential in our profession, even more so for a consultant. I think for every professional in our field, improving your writing is a good investment, that will always pay dividends in the long run. - Tell us about your experience as Python trainer
I’ve been teaching and training in tech, in different capacities, for more than twenty years now. In the early days of my career, it was a second job through a local non-profit organisation. Later it became more and more central and after a short stint in academia, a few years ago I started offering corporate training courses as part of my consulting services. The demand for Python and Data Science is high, with many companies looking into the PyData stack for their data analytics and business intelligence needs. Most of my trainings these days are 2 or 3-day sessions with small groups of circa 10-12 people. Python is easy to pick up so during those few days of training folks can learn a lot and feel productive, even people who are new to programming. - How has your training experience changed with remote work now being more prominent?
Training remotely is something I’ve been doing for a few years, so when Covid restrictions forced companies to work from home, I was ready. Reading the room in a Zoom call is obviously more difficult compared to in-person sessions, but there are tricks one can implement to keep the engagement high, improving the overall enjoyment for the delegates. For example, short demos with frequent “try it yourself” moments bring up questions that help me perceive the students’ understanding of the subject — Jupyter notebooks are great for this because of their interactive nature. Splitting the class in groups of 2-3 people using breakout rooms is also very useful for interactive exercises that people can solve in a “pair programming” fashion. - Tell us about your community engagement (meet-ups, conferences, etc)
Since 2014 I started attending local meet-ups, and in particular I was regularly at PyData London, a new (at the time) meet-up around Python and Data Science. I was enjoying the atmosphere and the quality of the presentations, and I found myself coming back every month. Like many Python events, PyData London is community-driven: everything is run by volunteers. Shortly after the first few events, I got closer with the regulars and the organisers and started helping out with the monthly meet-ups and even with the annual conference, reviewing proposals, chairing sessions, etc. Since 2018 I’ve been the chair of our annual conference, and I’ve helped growing the conference to over 700 attendees. Meanwhile, I’ve also attended many other Python conferences giving talks and tutorials, in particular PyCon UK and PyCon Italy, but also EuroPython, various local PyData chapters in the UK, PyMunich and PyParis. The common line is always the great community: you get the opportunity to meet interesting people in your field, talking shop while enjoying a relaxing atmosphere. I certainly recommend everybody to look for local events to connect to your peers, and if possible to present your work to boost your CV!
Many thanks to Marco and Cesare for having me as first guest of their podcast, which now has already a few episodes under its belt, do check them out here!
Marco Bonzanini 