Apart from visual demonstrations of data, verbal communication is just as important when presenting results. If you are not merely uploading results or publishing, you are usually presenting data to a room of data scientists, executives, or to a conference hall.
In any case, there are key areas to focus on when giving a verbal presentation, especially when the presentation is regarding findings of data.
There are generally two styles of oral presentation: one meant for more professional settings, including corporate offices where the problem at hand is usually tied directly to company performance or some other key performance indicator (KPI), and another meant more for a room of your peers where the key idea is to motivate the audience to care about your work.
Whether it is a formal or casual presentation, people like to hear stories. When you are presenting results, you are not just spitting out facts and metrics, you are attempting to frame the minds of your audience to believe in and care about what you have to say.
When giving a presentation, always be aware of your audience and try to gauge their reactions/interest in what you are saying. If they seem unengaged, try to relate the problem to them:
"Just think, when popular TV shows like Game of Thrones come back, your employees will all spend more time watching TV and therefore will have lower work performance."
Now you have their attention. It's about relating to your audience; whether it's your boss or your mom's friend, you have to find a way to make it relevant.
When presenting data findings to a more formal audience, I like to stick to the following six steps:
- Outline the state of the problem: In this step, we go over the current state of the problem, including what the problem is and how the problem came to the attention of the team of data scientists.
- Define the nature of the data: Here, we go into more depth about who this problem affects, how the solution would change the situation, and previous work done on the problem, if any.
- Divulge an initial hypothesis: Here, we state what we believed to be the solution before doing any work. This might seem like a more novice approach to presentations; however, this can be a good time to outline not just your initial hypothesis but, perhaps, the hypothesis of the entire company. For example, "we took a poll and 61% of the company believes there is no correlation between hours of TV watched and work performance."
- Describe the solution and, possibly, the tools that led to the solution: Get into how you solved the problem, any statistical tests used, and any assumptions that were made during the course of the problem.
- Share the impact that your solution will have on the problem: Talk about whether your solution was different from the initial hypothesis. What will this mean for the future? How can we take action on this solution to improve ourselves and our company?
- Future steps: Share what future steps can be taken with the problem, such as how to implement the solution and what further work this research sparked.
By following these steps, we can hit on all of the major areas of the data science method. The first thing you want to hit on during a formal presentation is action. You want your words and solutions to be actionable. There must be a clear path to take upon the completion of the project and the future steps should be defined.