In Case You Missed It in April 2024

Other than excessive maleness and paleness*, April 2024 was an excellent month for the podcast, packed with outstanding guests. ICYMI, today's episode highlights the most fascinating moments of my convos with them.

Specifically, conversation highlights include:

1. Iconic open-source developer Dr. Hadley Wickham putting the "R vs Python" argument to bed.

2. Aleksa Gordić, creator of a digital A.I.-learning community of 160k+ people, on the movement from formal to self-directed education.

3. World-leading futurist Bernard Marr on how we can work with A.I. as opposed to it lording over of us.

4. Educator of millions of data scientists, Kirill Eremenko, on why gradient boosting is so powerful for making informed business decisions.

5. Prof. Barrett Thomas on how drones could transform same-day delivery.

*Remedied in May!

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The 20% of Analytics Driving 80% of ROI

Today’s episode is with freakin' David Langer, people!! (So obviously it's brilliant, witty, and full of laughs.) He fills us in on the most powerful 20% of analytics — the analytics that drive 80% of companies’ return on investment.

Publishing under his Dave on Data brand, Dave's YouTube channel is top-notch, with several videos that have over a million views (and the thumbnails are hilarious; check 'em out). He is an exceptionally accomplished data scientist and software engineer, including spending nearly a decade at Microsoft's Global HQ, where his titles included principal software architect, principal data scientist, and director of analytics.

Topics in the episode include:

• Surprisingly powerful modeling approaches in spreadsheet tools like Excel

• The SQL databases we'll need if the data sets we're working with are too big for spreadsheets

• Why R programming is easy and should be our default language choice for moderate to advanced statistical analysis

• How companies can maximize value from machine learning

Listen or watch here.