A selection of my recorded talks on data science:

When data science projects fail

Recorded at PyData Ann Arbor

Everyone loves talking about successes, but data science projects fail all the time. Datasets don’t end up having signals, the work takes far longer than expected, and products end up missing the mark. In this recorded talk I examine the key themes that show up in projects that fail and how data scientists can spot them coming. To highlight these themes I use examples from the many failed data science projects that I have been personally responsible for.

Deep learning isn’t hard, I promise.

Recorded at New York R Conference

Deep learning sounds complicated and difficult, but it’s really not. Thanks to packages like Keras, you can get started with only a few lines of R code. Once you understand the basic concepts, you will able to use deep learning to make AI-generated humorous content! In this talk I give an introduction to deep learning by showing how you can use it to make a model that generates weird pet names like: Shurper, Tunkin Pike, and Jack Odins. If you understand how to make a linear regression in R, you can understand how to create fun deep learning projects.

You’re not paid to model

Recoded as part of the Metis Demystifying Data Science series

People are always willing to tell you about the fancy different modeling techniques in data science, and suggest they are the key to success. As a practicing data scientist, I am here to say that models are only a small part of the complexity of a corporate data science project. Almost always these complexities outside of the model are what cause projects to fail, not the fact that a model wasn’t using a cutting-edge approach. In this talk, I walk through the end-to-end lifecycle of a data science project in industry—and what you can do to maximize the chances of success.