I have published the slide deck from my talk at the NDR conference in Iași, Romania.
I have published the slide deck from my talk at the NDR conference in Iași, Romania.
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.
And if you are afraid of that awkward silence when you ask “are there any questions” and nobody reacts, you should read my post “Any Questions? How to fight the awkward silence at the end of the presentation“.
If you ever gave or attended a presentation, you are familiar with this situation: the presenter asks whether there are any questions and … nobody asks anything. This is an awkward situation. Why aren’t there any questions? Is it because everything is clear? Not likely. Everything is never clear. Is it because nobody cares? Well, maybe. There are certainly many people that don’t care. It’s a fact of life. Study your audience, work hard to make the presentation relevant and exciting but still, some people won’t care. Deal with it.
However, the bigger reasons for lack of the questions are human laziness and the fear of being stupid. Nobody likes asking a question that someone will perceive as a stupid one. Sometimes, some people don’t mind asking a question but are embarrassed and prefer not being the first one to break the silence.
What can you do? Usually, I prepare one or two questions by myself. In this case, if nobody asks anything, I say something like “Some people, when they see these results ask me whether it is possible to scale this method to larger sets.”. Then, depending on how confident you are, you may provide the answer or ask “What do you think?”.
You can even prepare a slide that answers your question. In the screenshot below, you may see the slide deck of the presentation I gave in Trento. The blue slide at the end of the deck is the final slide, where I thank the audience for the attention and ask whether there are any questions.
My plan was that if nobody asks me anything, I would say “Thank you again. If you want to learn more practical advises about data visualization, watch the recording of my tutorial, where I present this method <SLIDE TRANSFER, show the mockup of the “book”>. Also, many people ask me about reading suggestions, this is what I suggest you read: <SLIDE TRANSFER, show the reading pointers>
Luckily for me, there were questions after my talk. Luckily, one of these questions was about practical advice so I had a perfect excuse to show the next, pre-prepared, slide. Watch this moment on YouTube here.
Today, during the EuroSciPy conference, I gave a presentation titled “Three most common mistakes in data visualization and how to avoid them”. The title of this presentation is identical to the title of the presentation that I gave in Barcelona earlier this year. The original presentation was approximately one and a half hours long. I knew that EuroSciPy presentations were expected to be shorter, so I was prepared to shorten my talk to half an hour. At some point, a couple of days before departing to Trento, I realized that I was only allocated 15 minutes. Fifteen minutes! Instead of ninety.
Frankly speaking, I was in a panic. I even considered contacting EuroSciPy organizers and asking them to remove my talk from the program. But I was too embarrassed, so I decided to take the risk and started throwing slides away. Overall, I think that I spent eight to ten working hours shortening my presentation. Today, I finally presented it. Based on the result, and on the feedback that I got from the conference audience, I now know that the 15-minutes version is better than the original, longer one. Video recording of my talk is available on Youtube and is embedded below. Below is my slide deck
Illustration image credit: Photo by Jo Szczepanska on Unsplash
Today, I hosted a data visualization workshop, as a part of the workshop day adjacent to the fourth Israeli Data Science Summit. I really enjoyed this workshop, especially the follow-up questions. These questions are the reason I volunteer talking about data visualization every time I can. It may sound strange, but I learn a lot from the questions people ask me.
Yesterday, I talked in front of the Barcelona Data Science and Machine Learning Meetup about the most common mistakes in data visualization. I enjoyed talking with the local community very much. Judging by the feedback I received during and after the talk, they too, enjoyed my presentation. I uploaded my slides to Slideshare.
On Thursday, March 20, I will give a talk titled “Three most common mistakes in data visualization and how to avoid them.” I will be a guest of the Barcelona Data Science and Machine Learning Meetup Group. Right now, less than twenty-four hours after the lecture announcement, there are already seventeen people on the waiting list. I feel a lot of responsibility and am very excited.
Today, I made a presentation to the faculty of the Chisinau
Institute of Mathematics and Computer Science. The audience gathered in a conference room in Chisinau, and I was in my home office in Israel.
Following is a list of useful tips for this kind of presentations.
I consider teaching and presenting an integral part of my job as a data scientist. One way to become better at teaching is to collect feedback from the learners. I tried different ways of collecting feedback: passing a questionnaire, Polldaddy surveys or Google forms, or simply asking (no, begging) the learners to send me an e-mail with the feedback. Nothing really worked. The response rate was pretty low. Moreover, most of the feedback was a useless set of responses such as “it was OK”, “thank you for your time”, “really enjoyed”. You can’t translate this kind of feedback to any action.
Recently, I figured out how to collect the feedback correctly. My recipe consists of three simple ingredients.
working time: 5 minutes
Our goal is to collect constructive feedback. We want to improve and thus, are mainly interested in aspects that didn’t work well. In other words, we want the learners to provide constructive criticism. Sometimes, we may learn from things that worked well. You should decide whether you have enough time to ask for positive feedback. If your time is limited, skip it. Criticism is more valuable than praises.
Pass post-it notes to your learners.
Next, start with preventive amnesty, followed by mandatory questions, followed by another portion of preventive amnesty. This is what I say to my learners.
[Preventive amnesty] Criticising isn’t easy. We all tend to see criticism as an attack and to react accordingly. Nobody likes to be attacked, and nobody likes to attack. I know that you mean well. I know that you don’t want to attack me. However, I need to improve.
[Mandatory question] Please, write at least two things you would improve about this lecture/class. You cannot pass on this task. You are not allowed to say “everything is OK”. You will not leave this room unless you handle me a post-it with two things you liked the less about this class/lecture.
[Preventive amnesty] I promise that I know that you mean good. You are not attacking me, you are giving me a chance to improve.
When I teach using the Data Carpentry methods, each of my learners already has two post-it notes that they use to signal whether they are done with an assignment (green) or are stuck with it (red). In these cases, I ask them to use these notes to fill in their responses — one post-it note for the positive feedback, and another one for the criticism. It always works like a charm.
Запись моего доклада на WordCamp Moscow (август 2017г.) доступна онлайн.
The recording of my presentation at WordCamp Moscow (Aug 2017) is finally available online: Two Heads are Better Than One – on blogging persistence (Russian)