The ability to effectively present complex topics to an organization is a skill that clearly sets data professionals apart in the working world. It’s vital to distill intricate information into clear explanations when working with convoluted topics, and the success of this effort hinges on the ability to bridge the gap between complexity and comprehension. This is particularly true when talking about the difficult topics found in data science, for example deep learning algorithms, Bayesian inference, and dimensionality reduction (to name a few).
This article is the first in a series on preparing material for presentations, in which I want to run through the strategies and techniques I use when creating presentations to transform high-level topics into simple summaries. This series will walk through the various methods I use when considering how to structure my presentations to be clear, concise, and effective.
The advice I give in this series can be broken down into 3 simple tenets, which I have laid out below:
Know your audienceGuide your audienceAnticipate and prepare for responses
All of these points are interconnected and interdependent — a successful presentation will incorporate all three, allowing the audience to comprehend your key message, take away information relevant to them, and have their queries and concerns answered in a satisfactory manner. With these 3 key guidelines, you can be assured of success in technical presentations.
In this article I will focus on the first guideline — how to gain a sufficient understanding of your audience to be able to gauge their key concerns, base level of understanding on the topic at hand, and expectations for the presentation you’re about to give. This level of preparation is essential when dealing with any large audience composed of different stakeholders with…
