#227: Demystifying Complex Data Science Concepts for Non-Technical Audiences with Dr. Nicholas Cifuentes-Goodbody

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09/05/23 • 61 min

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One of the biggest challenges for the analyst or data scientist is figuring out just how wide and just how deep to go with stakeholders when it comes to key (but, often, complicated) concepts that underpin the work that's being delivered to them. Tell them too little, and they may overinterpret or misinterpret what's been presented. Tell them too much, and they may tune out or fall asleep... and, as a result, overinterpret or misinterpret what's been presented. On this episode, Dr. Nicholas Cifuentes-Goodbody from WorldQuant University joined Julie, Val, and Tim to discuss how to effectively thread that particular needle. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Previous Episode

#226: Training Analysts to be Curious and Use Business Context with MaryBeth Maskovas

We were curious about... curiosity. We know it's a critical trait for analysts, but is it an innate characteristic, a teachable skill, or some combination of both? We were curious. How can the breadth and depth of a candidate's curiosity be assessed as part of the interview process? We were curious. Who could we kick these questions (and others) around with? We were NOT curious about that! MaryBeth Maskovas, founder and Principal Consultant at Insight Lime Analytics, joined Michael, Julie, and Tim to explore the topic. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Next Episode

#228: What AI Can't Do with Dr. Brandeis Marshall

It's a lot of work to produce each episode of this show, so we were pretty sure that, by this time, we would have just turned the whole kit and kaboodle over to AI. Alas! It seems like the critical thinking and curiosity and mixing of different personalities in a discussion are safely human tasks... for now. Dr. Brandeis Marshall joined Michael, Julie, and Moe for a discussion about AI that, not surprisingly, got a little bleak at times, but it also had a fair amount of hope and handy perspectives through which to think about this space. We recommend listening to it rather than running the transcript through an LLM for a summary! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.