In this post, I will try to convince you that speaking at a conference is an essential tool for professional development.
Many people are afraid of public speaking, they avoid the need to speak in front of an audience and only do that when someone forces them to. This fear has deep evolutional origins (thousands of years ago, if dozens of people were staring at you that would probably mean that you were about to become their meal). However, if you work in a knowledge-based industry, your professional career can gain a lot if you force yourself to speak.
Two days ago, I spoke at NDR, a machine learning/AI conference in Iași, Romania. That was a very interesting conference, with a diverse panel of speakers from different branches of the data-related industry. However, the talk that I enjoyed the most was mine. Not because I’m a narcist self-loving egoist. What I enjoyed the most were the questions that the attendees asked me during the talk, and in the coffee breaks after it. First of all, these questions were a clear signal that my message resonated with the audience, and they cared about what I had to say. This is a nice touch to one’s ego. But more importantly, these questions pointed out that there are several topics that I need to learn to become more professional in what I’m doing. Since most of the time, we don’t know what we don’t know, such an insight is almost priceless.
That is why even (and especially) if you are afraid of public speaking, you should jump into the cold water and do it. Find a call for presentations and submit a proposal TODAY.
I am excited to run a data visualization tutorial, and to give a data visualization talk during the 2018 EuroSciPy meeting in Trento, Italy.
My tutorial “Data visualization — from default and suboptimal to efficient and awesome”will take place on Sep 29 at 14:00. This is a two-hours tutorial during which I will cover between two to three examples. I will start with the default Matplotlib graph, and modify it step by step, to make a beautiful aid in technical communication. I will publish the tutorial notebooks immediately after the conference.
What: Data Visualization from default to outstanding. Test cases of tough data visualization
Why: You would never settle for default settings of a machine learning algorithm. Instead, you would tweak them to obtain optimal results. Similarly, you should never stop with the default results you receive from a data visualization framework. Sadly, most of you do.
When: May 27, 2018 (a day before the DataScience summit)/ 13:00 – 16:00
Where: Interdisciplinary Center (IDC) at Herzliya.
1. Theoretical introduction: three most common mistakes in data visualization (45 minutes)
2. Test case (LAB): Plotting several radically different time series on a single graph (45 minutes)
3. Test case (LAB): Bar chart as an effective alternative to a pie chart (45 minutes)
4. Test case (LAB): Pie chart as an effective alternative to a bar chart (45 minutes)
According to the conference organizers, the yearly Data Science Summit is the biggest data science event in Israel. This year, the conference will take place in Tel Aviv on Monday, May 28. One day before the main conference, there will be a workshop day, hosted at the Herzliya Interdisciplinary Center. I’m super excited to host one of the workshops, during the afternoon session. During this workshop, we will talk about the mistakes data scientist make while visualizing their data and the way to avoid them. We will also have some fun creating various charts, comparing the results, and trying to learn from each others’ mistakes.
Two months ago, on the PyCon-IL conference, I gave a lecture called “Time Series Analysis: When “Good Enough” is Good Enough”. You may find the written version of this talk here. Today, the conference organizers published all the conference talks on YouTube. Here’s mine:
Being highly professional, many data scientists strive toward the best results possible from a practical perspective. However, let’s face it, in many cases, nobody cares about the neat and elegant models you’ve built. In these cases, fast deployment is pivotal for the adoption of your work — especially if you’re the only one who’s aware of the problem you’re trying to solve.
This is exactly the situation in which I recently found myself. I had the opportunity to touch an unutilized source of complex data, but I knew that I only had a limited time to demonstrate the utility of this data source. While working, I realized it’s not enough that people KNOW about the solution, I had to make sure that people would NEED it. That is why I sacrificed modeling accuracy to create the simplest solution possible. I also had to create a RESTful API server, a visualization…
On June 12th, I’ll be talking about anomaly detection and future forecasting when “good enough” is good enough. This lecture is a part of PyCon Israel that takes place between June 11 and 14 in the Bar Ilan University. The conference agenda is very impressive. If “python” or “data” are parts of your professional life, come to this conference!