Failure. It happens. It happens a lot. And we can't avoid it. But we can learn to anticipate some of it, and to better deal with it as it happens to us in our data science projects. In this episode, I discuss how failure happens in projects, how to avoid some of it, how do deal with some of it, examples of where I've really failed, and how to cope …

Seeing how to solve a problem is one thing. Solving that problem in a way that doesn't hamstring future you takes a lot of work. In this episode, we talk about technical debt: what it is, what it looks like to a data scientist, and some resources to help avoid it. People I reference to follow on Twitter: Jenny Bryan (@JennyBryan) Mara Averick (@dat…

In the first two episodes, I talked about knowing your data, then knowing your tools. In this episode, I talk about knowing your business. I give a story about where I didn't know my business and it burned me. How can knowing your business help you up even your data science skills? Listen and find out!…

The common complaint about data science is that 90% of your time is spent data wrangling. In this episode, I talk about some history that leads to this current state of data science work, and why you should embrace this. I also give some resources that will help you with your data wrangling at the raw level. R Packages and Tools mentioned in this e…

In this introductory episode, I introduce myself (Matt Pettis), what I want to do with the podcast, and some examples and stories regarding knowing your data. I talk about example of where not knowing my data burned me, how knowing it saved me, and some simple tactics you can use to familiarize yourself with the data of your project. I originally w…