A collection of facts is not a story. If you have the chance to give a presentation, why would you talk about only facts? What is the story?
As scientists, we like to focus on facts—facts are safe, they aren’t up for debate. Facts (i.e., data) are what we agree to before we start the discussion; the discussion that follows can focus on the interpretation of the data, but the facts—the data—is not up for debate. (At least by the time you get to the point where you are giving a presentation.)
When we think of a story, we probably assume three acts: a beginning, a middle, and the end. But framing a presentation in this framework can cause worry to a business audience. It’s often helpful in this setting to provide the bottom-line-up-font (BLUF). Yes, it gives away the ending, but this technique creates a useful top-down narrative that anyone with too many meetings on their calendar will appreciate.
If you want to engage the audience—whether one, five, twenty or a hundred—tell us the story behind the data. It doesn’t have to be a long, drawn-out tale, but it needs to be enough to provide context and justify the time commitment you are asking us to give.
There is no better gig than getting paid to learn.
In August of last year, I decided to take on the challenge of teaching an applied statistical techniques course—one small problem—I’m not a statistician. Like most scientist and engineers, I’ve used (and abused) statistics all my entire career, so I do have experience with the concepts. Also, my math skills are not too shabby as I have incorporated calculus, differential equations, and other advanced topics into my work over the years. (Yes, some people do use algebra.) The good news? I had about four months before the first day of class and enough control over my schedule where I could commit 10+ hours per week to develop the course content. The real challenge was to create the course using R, a language, and environment for statistical computing and graphics. Being open about my own abilities, I was not proficient with this language.
With this in mind, I set out to create the course—with emphasis on the “applied” and “techniques.” Starting in the fall, I developed a module that students would work through each week. I estimated my 10–12 hours of work each week would translate into the three hours of material for each class and this worked out surprisingly well.
The course is coming to an end this week, and the best part of the experience was how much I learned (or maybe relearned). But most surprising, it was how much I learned the SECOND time I worked through the materials during the weeks I taught the course.
I classify skill sets at three levels:
- novice: apply basic principles to solve structured problems,
- hack: gather external resources to solve moderately complex problems,
- expert: apply advanced principles to solve complex problems with the minimal use of external resources.
I still consider myself a hack when it comes to performing statistical analysis using R, but having the opportunity to expand my own skill set and providing a framework for others to learn something new—that was a great gig.
It is an unfortunate truth that as you get older, you start paying more attention to the obituaries than when you were young.
I recently read about the death of a someone I have fond memories. I hadn’t seen this person in probably 25 or 30 years, but as a young man, they had a significant impact on how I should view the world—to be kind.
Every once in a while we need to be reminded of the events and people who shaped our lives for the better. An obituary is a final remembrance. As I was recently reminded, obituaries are about life. In this case a reminder to embody the kindness that was shown to me as a young man and how it was shown to others.