What I learnt from giving 120+ Data Science Presentations
By: Das Wijesundera
Tags: Storytelling, Data Science, Presentations, Office Hours, Data Visualization

What I learnt from giving 120+ Data Science Presentations

6 points to help navigate the world and daily technical practices to become better data communicators

Photo by Anggi Nurjaman on Unsplash

Reflecting back on the various roles I have had in a Data Science team, it has occurred to me that I am one of the few people who is often asked to present.

From a whole host of meeting invites, to slides in various Teams channels and countless versions of presentations on my laptop, I realised that I had presented more than 120 versions of 18 projects over the last six years — that’s roughly one presentation every three weeks! Through this, I can safely say that I have sat through and given presentations that did not land well with various audiences.

One would argue that this many presentations were unnecessary for technically apt stakeholders in a data literate organisation, but these individuals are few and far between where I work.

Giphy — Come through, Karamo 👏🏽👏🏽👏🏽

Many of my presentations have been time-consuming and cumbersome to prepare, but they have been invaluable in my attempt to become a better Data Science communicator. Regardless, I am thankful of my introspective moments which have led to confidence in my content and communication skills.

To date, being a keynote speaker and attending a panel at Financial Times Energy Summit on ML in the energy industry has been a highlight. Whilst I have so much more to learn, I now know that I can drive an engaging conversation with those who are listening.

So, here are six points that I think are valuable and some resources I have used when considering presenting my projects.

1. Create a narrative

Regardless of the project you are presenting, whether it is explorative, a production ready solution or research, a narrative is necessary to coherently articulate the progress made. Whilst there are many approaches to this, there are two crucial components which are a must.

Context

Most of the time, our projects won’t make sense when they land in front of us. We have to painstakingly construct models, insights and outcomes from mediocre data. To make sense we dissect and explore parts of data over and over to find meaning within the bigger picture. Essentially, we become scientists.

Fortunately for our audience, we are in a position to frame our data within a bigger picture and to become the story-teller. We can include history, similar trends and approaches others may have taken. We can use visualisations from our explorative stages to describe potential challenges, silver linings and our inspirations.

Whether you present to very specific teams or bigger, diverse groups, a tailored presentation with more relevant facts and background is beneficial. I tend to structure my slides following a fairly high-level narrative and draw attention to the detailed slides when necessary. Doing this allows the audience to remain focused on the challenge and the solution, whilst I help them appreciate the progress I have made.

Timeline

It is important to stick to a clear and organised timeline (preferably linear) — Data Science stories shouldn’t feel like a Christopher Nolan film.

I have often seen presentations start instantly with an outcome and solution. Whilst these are impactful, they also lead to a lot of “what does that mean?” moments, which can potentially alienate your audience.

A strong start is essentially your background and the context you provide. A valuable middle section would include a concise explanation of your method, findings, insights and necessary information to help the audience understand what was done. At the end, you should wrap up with model outcomes, potential improvements, use and challenges with data, strategies to production and wider impact if the project was to continue.

If you are able to, make room for questions. This gives the audience the opportunity to ‘dive deeper’ into the content and anything they have become particularly interested with.

2. Don’t make the audience listen and read.

Put yourself in the shoes of a stakeholder sitting through a ML slide deck, trying to listen to an expert, read their blurb on the slide and understand the value their work has on a business domain. This is a lot to juggle.

Instead, limit the amount of text to few bullet points and statements per slide to avoid cramming text. There are number of steps you can take to make concise and clear points, articulated well by Guy Kawasaki — 10/20/30 rule which provides an excellent template for pitching. Though it doesn’t translate directly for ML presentations, much of it is applicable.

3. Visualisations are important. If you are making charts, use the right visual tools.

This is probably the most important point for me, however, it is also the hardest to achieve. These principles may be easy to discuss but are much harder to execute, consistently.