Better Data Science Presentations
TL;DR You can read the guide here.
If a data scientist does great work but there’s no stakeholder to champion it, did they really have impact?
Despite the inclusion of “scientist” in our job titles, we are not academics in ivory towers. We are not paid to explore data endlessly, but instead to use our statistical, mathematical, and programming knowledge to solve problems facing our businesses. This means we must remain laser-focused on applications in order to be successful in our roles.
Despite the inclusion of “scientist” in our job titles, we are not academics in ivory towers. We are not paid to explore data endlessly, but instead to use our statistical, mathematical, and programming knowledge to solve problems facing our businesses. This means we must remain laser-focused on applications in order to be successful in our roles.
DALL-E prompt: data scientist presenting to executives in the style of magritte
Working in industry over the years, we have noticed that the most common hurdle to data scientists having impact (and getting promoted!) was not the lack of technical knowledge, but rather the organization they work in not understanding the data scientists’ work. Presentations are an extremely common way that data science work is shared, but many data science presentations are lackluster, failing to communicate with the audience in a way that resonates with them.
We wrote this guide to help you with that. In it, we'll give you a framework for tailoring your presentation to three types of groups–your data scientist peers, your cross-functional teammates, and the sponsors who ultimately commissioned your work in the first place. We’ll give examples based around a hypothetical scenario, where we are assuming that you were hired as a data scientist by the New York City’s business improvement district (BID) program to figure out how to better deploy resources to increase the cleanliness of the streets. You are given the historical data that contains a unique ID of the business improvement district, the area that needs to be serviced and how much staff was employed (both part- and full-time).
You can read the guide online here.
About us
Luca Belli is the founder of Sator Labs, a Visiting NIST AI Fellow and a UC Berkeley Tech Policy Fellow. Previously he was the co-founder and Research Lead for Twitter's Machine learning Ethics, Transparency and Accountability (META) team where he guided industry leading approaches for responsible ML practices and product changes. Before that he operated as a Data Science and Machine Learning Engineer at Conversant and WolframAlpha. His research interests lie at the intersection of feedback loops, algorithmic amplification (with a special eye on politics), and algorithmic audits. He holds a Ph.D. in Math from Tor Vergata University in Rome.
Katie Bauer is the Head of Data at GlossGenius, an all-in-one app that enables solo beauty entrepreneurs to run independent businesses. She previously built and lead the Infrastructure Data Science and Analytics organization at Twitter, as well as the Consumer Product Data Science team at Reddit. She is sought after for her expertise on leading teams, and speaks and writes publicly about data work, leadership, and career paths.
If you find this guide helpful, let us know. And if you are so inclined, you can buy us a coffee!
We wrote this guide to help you with that. In it, we'll give you a framework for tailoring your presentation to three types of groups–your data scientist peers, your cross-functional teammates, and the sponsors who ultimately commissioned your work in the first place. We’ll give examples based around a hypothetical scenario, where we are assuming that you were hired as a data scientist by the New York City’s business improvement district (BID) program to figure out how to better deploy resources to increase the cleanliness of the streets. You are given the historical data that contains a unique ID of the business improvement district, the area that needs to be serviced and how much staff was employed (both part- and full-time).
You can read the guide online here.
About us
Luca Belli is the founder of Sator Labs, a Visiting NIST AI Fellow and a UC Berkeley Tech Policy Fellow. Previously he was the co-founder and Research Lead for Twitter's Machine learning Ethics, Transparency and Accountability (META) team where he guided industry leading approaches for responsible ML practices and product changes. Before that he operated as a Data Science and Machine Learning Engineer at Conversant and WolframAlpha. His research interests lie at the intersection of feedback loops, algorithmic amplification (with a special eye on politics), and algorithmic audits. He holds a Ph.D. in Math from Tor Vergata University in Rome.
Katie Bauer is the Head of Data at GlossGenius, an all-in-one app that enables solo beauty entrepreneurs to run independent businesses. She previously built and lead the Infrastructure Data Science and Analytics organization at Twitter, as well as the Consumer Product Data Science team at Reddit. She is sought after for her expertise on leading teams, and speaks and writes publicly about data work, leadership, and career paths.
If you find this guide helpful, let us know. And if you are so inclined, you can buy us a coffee!