Creating a Presentation for a Data Science Project

One of the most sought after skills in data analytics is to be able to tell a story with data. This is why you used the data visualizations, statistics, and machine learning, it was to influence business decisions. There should be plenty of domain knowledge put to use and you should convey your point in such a way that someone who has no knowledge of data analytics can understand your message. One classic way to give a presentation is with a slide show.

The first slide, after the title, should explain the problem statement. Why are you working on this project? What is the benefit? This gives an introduction and should convey the importance of the project. It is probably best to use two slides, one for the problem, and another for the benefits of the analytics.

Next up is methodology. Talk about how you broke the problem down and what tools you used. Explain any challenges you overcame. This is a good time to show how thorough of a job you did. No need to get too detailed though, unless you are presenting to a technical audience.

After methodology, talk about your findings. What new things did you discover? Were you able to support existing theories about the business using statistics? How did your machine learning model perform? This is the most important part of the presentation, as it is the results of your project.

Now that the findings have been discussed, make some recommendations. This helps put the analytics to work in the real world. You may not be the one making big business decisions, but your analytics could be what is driving them. Domain knowledge and knowledge of business operations is a real force multiplier here.

Finally and optionally, talk about future work. What are the next steps? Does the problem need to be revisited in the future? Can the project be improved with more work? If you were given more time and resources, is there anything that could be done better?

These talking points should help you organize your presentations. It is popular to talk about statistical methods and machine learning algorithms on the internet, but keep in mind that communication skills are just as important.