Data Science Salon Podcast

If you've been in the data game long enough, you've probably seen this before: a stakeholder or product owner approaches you with a project that's 95% done, and they'd like you to ... "sprinkle some AI on it." They've heard that this "AI" thing can be useful so they want some of it in their latest effort.Data scientist-turned-product person Noelle Saldana has experienced the "sprinkle some AI on it" request more times than she'd care to remember. Our Senior Content Advisor Q McCallum met up with Noelle to explore this phenomenon. How does this happen? (Hint: "corporate FOMO.") What should you do when stakeholders insist on implementing AI that isn't actually going to help? What about when your data scientist peers seem like they're doing this for the sake of "résumé-driven development?"Ultimately, the pair work through the bigger issue: how do you make peace with companies throwing money at AI like this? And how can these companies use this approach to their advantage?As a bonus, Noelle shares how she made the move from a data scientist role into product management. If this path sounds interesting to you, take a listen.

• Noelle's Data Council talk, "Hot Takes and Tragic Mistakes: How (not) to Integrate Data People in Your App Dev Team Workflows"
• Find Noelle on LinkedIn: https://www.linkedin.com/in/noellesio/
• Q's blog post (which came out much better thanks to Noelle's help): "AI isn't something you just add to a company"

Sometimes the most valuable data IN your company ... is the data LEAVING your company.That's Solomon Kahn's view on data products, as well as the premise behind his latest venture: Delivery Layer.For this episode, our Senior Content Advisor Q McCallum reached out to Solomon to check in on the new startup, and to tap his expertise in the world of data products.Solomon's been at this a while. He's run high-revenue data products in some notable places, including Nielsen. Over the years he's learned a lot and we're excited for him to share some of that hard-earned knowledge here on the show.In this extended conversation, the two explore: the reasons why building a data product is different (and, in many ways, more difficult) than building traditional software products; how the people involved can impact the outcome; why a good sense of risk management can make all the difference; and what purple cars have to do with all of this. (No, seriously. Purple cars.)Along the way, the pair talk about the early days of the data field, and how much it has changed.

• Solomon is active on LinkedIn. You can follow him for his daily updates at https://www.linkedin.com/in/solomonkahn
• Delivery Layer: https://www.deliverylayer.com/

In this episode, James and Q explore:

• The ideas of risk and uncertainty.
• What is "probabilistic thinking" and why is it important for data scientists?
• The career progression of a data analyst, and what it means to develop statistical acumen.
• Thinking in terms of distributions, and thinking in different moments of a distribution.
• Seeing BI, AI, and simulation in terms of punctuation. (No, seriously.)
• How to bridge the gap into thinking probabilistically

And, just a reminder: James only speaks for himself in this episode and he does not represent his employer.Links mentioned during our discussion:

• You can find James on Twitter at @cmastication and on LinkedIn at https://www.linkedin.com/in/jamesdlong/
• R Cookbook, 2nd Edition (which James co-authored)
• RenRe's open roles: https://bit.ly/renrejobs
• Q's blog posts on "punctuation in data": Periods and Question Marks (BI and AI), and then Ellipses (simulation)

The list of books James mentioned:

• Thinking in Bets (Annie Duke)
• Fortune's Formula (Poundstone)
• The Lady Tasting Tea (Salsburg)
• Fooled by Randomness (Taleb)

We've all heard the term "economist," sure. But exactly what does and economist do? And as economics is a very data-driven field, where does their work intersect with data science, machine learning, and AI?

To answer that question, Senior Content Advisor Q McCallum spoke with Amar Natt, PhD. She's an economist at Econ One Research, and her work focuses on advanced analytics and predictive modeling. Does that sound like ML to you? Well, Amar explains that it's similar in some ways, different in others. From there, she tells us about techniques economists can learn from data scientists, and what data scientists can pick up from econ. (Hint: "causal inference." You heard it here first.) You can find Amar online:

• LinkedIn: https://www.linkedin.com/in/amarita-natt-ph-d-79028313/
• Econ One Research: https://www.econone.com/staff-member/amarita-natt/

Be part of the conversation and connect with the data science community at DSS Miami Hybrid on September 21, 2022.

Book your ticket now.