Why you should speak at conferences?

Me speaking on a stage

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“.

“Any questions?” How to fight the awkward silence at the end of a presentation?

Questions?

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>

Screen Shot 2018-09-17 at 10.10.21

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.

Live in Barcelona. Three most common mistakes in data visualization.

Me in front of a whiteboard, pointing at a graph

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.

 

Tips on making remote presentations

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.

Me presenting in front of the computer

Following is a list of useful tips for this kind of presentations.

  • When presenting, it is very important to see your audience. Thus, use two monitors. Use one monitor for screen sharing, and the other one to see the audience
  • Put the (Skype) window that shows your audience under the camera. This way you’ll look most natural on the other side of the teleconference.
  • Starting a presentation in Powerpoint or Keynote “kidnaps” all the displays. You will not be able to see the audience when that happens. I export the presentation to a PDF file and use Acrobat Reader in full-screen mode. The up- and down- buttons in my presentation remote control work with the Reader. The “make screen black” button doesn’t.
  • I open a “lightable view” of my presentation and put it next to the audience screen. It’s not as useful as seeing the presenter’s notes using a “real” presentation program, but it is good enough.
  • Stand up! Usually, we stand up when we present in front of live audience. For some reason, when presenting remotely, people tend to sit. A sitting person is less dynamic and looks less engaging. I have a standing desk which allows me to stand up and to raise the camera to my face level. If you can’t raise the camera, stay sitting. You don’t want your audience staring at your groin.

Auditorium in Chisinau showing me on their screen

 

What is the best way to collect feedback after a lecture or a presentation?

A pile of green and red post-it notes with feedback on them

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.

Collecting feedback. The recipe.

working time: 5 minutes

Ingredients

  • Open-ended mandatory questions: 1 or 2
  • Post-it notes: 1 – 2 per a learner
  • Preventive amnesty: to taste

Procedure

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.

That’s it.

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.

A pile of green and red post-it notes with feedback on them

 

Can the order in which graphs are shown change people’s conclusions?

Example of how priming can affect the perceived separability of two data sets

When I teach data visualization, I love showing my students how simple changes in the way one visualizes his or her data may drive the potential audience to different conclusions. When done correctly, such changes can help the presenters making their point. They also can be used to mislead the audience. I keep reminding the students that it is up to them to keep their visualizations honest and fair.  In his recent post, Robert Kosara, the owner of https://eagereyes.org/, mentioned another possible way that may change the perceived conclusion. This time, not by changing a graph but by changing the order of graphs exposed to a person. Citing Robert Kosara:

Priming is when what you see first influences how you perceive what comes next. In a series of studies, [André Calero Valdez, Martina Ziefle, and Michael Sedlmair] showed that these effects also exist in the particular case of scatterplots that show separable or non-separable clusters. Seeing one kind of plot first changes the likelihood of you judging a subsequent plot as the same or another type.

via IEEE VIS 2017: Perception, Evaluation, Vision Science — eagereyes

As any tool, priming can be used for good or bad causes. Priming abuse can be a deliberate exposure to non-relevant information in order to manipulate the audience. A good way to use priming is to educate the listeners of its effect, and repeatedly exposing them to alternate contexts. Alternatively, reminding the audience of the “before” graph, before showing them the similar “after” situation will also create a plausible effect of context setting.

P.S. The paper mentioned by Kosara is noticeable not only by its results (they are not as astonishing as I expected from the featured image) but also by how the authors report their research, including the failures.

 

Featured image is Figure 1 from Calero Valdez et al. Priming and Anchoring Effects in Visualization