How is an outlier in the data like obscenity? A case could be made that they're both the sort of thing where we know it when we see it, but that can be awfully tricky to perfectly define and detect. Visualize many data sets, and some of the data points are obvious outliers, but just as many (or more) fall in a gray area—especially if they're sneaky inliers. z-score, MAD, modified z-score, interquartile range (IQR), time-series decomposition, smoothing, forecasting, and many other techniques are available to the analyst for detecting outliers. Depending on the data, though, the most appropriate method (or combination of methods) for identifying outliers can change! We sat down with Brett Kennedy, author of Outlier Detection in Python, to dig into the topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. 

So, you finally took that recruiter's call, and then you made it through the initial phone screen. You weren't really expecting that to happen, but now you're facing an actual interview! It sounds intense and, yet, you're not sure what to expect or how to prepare for it. Flash cards with statistical concepts? A crash course in Python? LinkedIn stalking of current employees of the company? Maybe. We asked Jay Feng from Interview Query to join us to discuss strategies and tactics for data scientists and analyst interviews, and we definitely wanted to hire him by the time we were done! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

WHERE were you the first time you listened to this podcast? Did you feel like you were JOINing a SELECT GROUP BY doing so? Can you COUNT the times you've thought to yourself, "Wow. These guys are sometimes really unFILTERed?" On this episode, Pawel Kapuscinski from Analytics Pros (and the Burnley Football Club) sits down with the group to shout at them in all caps. Or, at least, to talk about SQL: where it fits in the analyst's toolbox, how it is a powerful and necessary complement to Python and R, and who's to blame for the existence of so many different flavors of the language. Give it a listen. That's an ORDER (BY?)! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Are you deeply knowledgable in JavaScript, R, the DOM, Python, AWS, jQuery, Google Cloud Platform, and SQL? Good for you! If you're not, should you be? What does "technical" mean, anyway? And, is it even possible for an analyst to dive into all of these different areas? English philosophy expert The Notorious C.M.O. (aka, Simo Ahava) returns to the show to share his thoughts on the subject in this episode. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Tell me about a time you produced an amazing analysis. Please provide your response in the form of a Jupyter notebook that uses Python or R (or both!) to pull words from a corpus that contains all words in the OED stored in a BigQuery table. I mean, that's a fair question to ask, right? No? Well, what questions and techniques are effective for assessing an analyst's likelihood of succeeding in your organization? How should those techniques differ when looking for a technical analyst as opposed to a more business-oriented one? On this episode of the show -- recorded while our recording service clearly thought it was in a job interview that it needed to deliberately tank -- Simon Rumble from Snowflake Analytics joined the gang to share ideas on the topic. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

WHY does Tim simply not give Python its due? Isn't Python a perfectly acceptable -- possibly even better -- option when it comes to diving into programming with data? It's open source, too. Some say it's easier to learn than R. And, frankly, isn't a programming language named after a snake just inherently cooler than one named after a letter of the alphabet? The fellas tackled the topic with Ryan Praskieviecz from EY on this episode...and possibly wound up tackling it in a way that will leave Python lovers that much more ready to strangle them (as pythons are wont to do). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.