In Case You Missed It in June 2025

We had exceptional guests on my podcast in June. In today's "In Case You Missed It" episode, hear the best parts of all my June convos. Here's a quick summary to tantalize you:

1. Strategy consultant Diane Hare with five tricks for gaining buy-in on A.I. transformation in your organization.

2. Renowned data-career educator Avery Smith on the two portfolio projects every aspiring data analyst should build.

3. SuperDataScience founder Kirill Eremenko on what you're missing if you're struggling to land an A.I. job.

4. San Fran-based venture capitalist Shaun Johnson on the traits that make a great A.I. startup founder.

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

In Case You Missed It in May 2025

We had stellar guests in May, including one episode that had the most positive social-media response of any episode ever. In today's "In Case You Missed It" episode, hear the best parts of all my May convos.

The specific conversation highlights included in today's episode are:

1. John Roese, Dell Technologies' global CTO and Chief A.I. Officer, on the biggest A.I. opportunities for enterprises in the coming months/years. (This is the episode that received an unprecedented social-media response.)

2. The authors of the brand-new O'Reilly book "Python Polars: The Definitive Guide", Jeroen Janssens and Thijs Nieuwdorp, on a real-world Polars success story.

3. Space engineer and entrepreneur Mary Spio on solving global talent shortages with A.I.-infused virtual reality hardware.

4. Martin Brunthaler, serial entrepreneur/CTO, on how platforms like Adverity allow you to talk with your data in natural language.

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

Serverless, Parallel, and AI-Assisted: The Future of Data Science is Here, with Zerve’s Dr. Greg Michaelson

What are "code nodes" and "RAG DAGs"? Listen to today's episode with the highly technical (but also highly hilarious) Dr. Greg Michaelson to get a glimpse into the future of data science and A.I. model development.

Greg:

• Is a Co-Founder of Zerve AI, a super-cool platform for developing and delivering A.I. products that launched to the public on this very podcast a little over a year ago.

• Previously spent 7 years as DataRobot’s Chief Customer Officer and 4 years as Senior Director of Analytics & Research for Travelers.

• Was a baptist pastor while he obtained his PhD in Applied Statistics!

Today’s episode is on the technical side and so will appeal most to hands-on practitioners like data scientists, AI/ML engineers and software developers… but Greg is such an engaging communicator that anyone interested in how the practice of data science is rapidly being revolutionized may enjoy today’s episode.

In it, Greg details:

• How Zerve's collaborative, graph-based coding environment has matured over the past year, including their revolutionary 'Fleet' feature (in beta) that allows massive parallelization of code execution without additional cost.

• How AI assistants are changing the coding experience by helping build, edit, and connect your data science projects.

• Why the rise of LLMs might spell trouble for many SaaS businesses as building in-house solutions becomes increasingly viable.

• The innovative ways companies are using retrieval-augmented generation (RAG) to create more powerful A.I. applications.

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

In Case You Missed It in December 2024

Today's "In Case You Missed it Episode"... is one not to miss! Several of the most fascinating conversations I've ever had on the SuperDataScience Podcast I host happened in December.

The specific conversation highlights included in today's episode are:

1. The legendary Dr. Andrew Ng on why LLM cost doesn't matter for your A.I. proof of concept.

2. Building directly on Andrew's segment, CTO (and my fellow Nebula.io co-founder) Ed Donner on how to choose the right LLM for a given application.

3. Extremely intelligent and clear-spoken Dr. Eiman Ebrahimi (CEO of Protopia AI) on the future of autonomous systems and data security in our Agentic A.I. future.

4. From our 2024 recap episode, Sadie St. Lawrence's three biggest A.I. "wow" moments of the year... as well as the biggest flop of the year. (One company was behind both!)

5. Harvard/MIT humanist chaplain Greg Epstein (and bestselling author on tech in society) on the ethics of accelerating A.I. advancements. Should we, for example, consider slowing A.I. progress down?

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

Are “Citizen Data Scientists” A Myth? With Keith McCormick

In a recent episode, Nick Elprin and I laughed that "citizen data scientists" don't exist. Keith McCormick joins me today to eloquently rebut us and demonstrate the clear value of low-code/no-code tools.

Keith is:

• Data Science Principal at the enterprise A.I. consultancy Further.

• Creator of dozens LinkedIn Learning courses on machine learning and A.I. with, in aggregate, over a million students!

• Author of four statistics books.

Today’s short episode should be of interest to just about any listener. In it, Keith details:

• Common circumstances where low-code/no-code data science tools are the best option for you, even if you are a coding whiz.

• Whether citizen data scientists are myth or reality.

• How AutoML fits into the data science workflow - and why it won't replace data science teams.

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

The Positron IDE, Tidy NLP and MLOps with Dr. Julia Silge

Prepare to have your brain tickled by Dr. Julia Silge. In today's episode, Julia details the IDE she's been developing for data scientists, "Tidy" NLP, and open-source libraries that make MLOps a breeze.

More on Julia:

• Engineering Manager at Posit PBC (makers of RStudio... and the company formerly known as RStudio).

• Authored the bestselling O'Reilly books “Text Mining with R” and “Tidy Modeling with R".

• Previously worked as a Data Scientist at Stack Overflow and Datassist.

• Prior to joining industry, was an academic researcher and professor at Yale University.

• Holds a PhD in Astronomy from The University of Texas at Austin.

Today’s episode will probably appeal most to hands-on practitioners like data scientists, software developers and ML engineers. In it, Julia details:

• The brand-new IDE Positron (free to use and source-available) that she’s been developing.

• Her favorite LLMs for code generation.

• The open-source software libraries that make MLOps easy.

• Her top tips for effective Natural Language Processing, including when more traditional NLP techniques should be used instead of an LLM.

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

Explaining AGI to a 94-Year-Old

In today's short episode, I explain "data", "data science", "A.I." and AGI to a 94-year-old woman (my brilliant grandmother) who previously had no familiarity with the terms.

Perhaps the episode will be helpful to folks who are unfamiliar with any of these terms themselves, or to folks who'd like ideas for how to explain any of them to laypeople.

("AGI" is Artificial General Intelligence, btw!)

The "Super Data Science Podcast with Jon Krohn" is available on your favorite podcasting platform and the video version (which today is simply an audio waveform!) is on YouTube. Today's episode is #816.

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

Open-Source Libraries for Data Science at the New York R Conference

For today's short episode, I asked four data-science luminaries about their favorite open-source libraries. Hear what Emily Zabor, James David Long, Drew Conway and Jared Lander chose, live on stage!

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

The Six Keys to Data Scientists’ Success, with Kirill Eremenko

For today's episode, Kirill Eremenko — who has taught more than 2.8 million people data science — fills us in on his six most valuable insights about data science careers.

More on Kirill:

• Founder and CEO of SuperDataScience, an e-learning platform that is the namesake of this very podcast.

• Launched the SuperDataScience Podcast in 2016 and hosted the show until he passed me the reins four years ago.

• Has reached more than 2.8 million students through the courses he’s published on Udemy, making him Udemy’s most popular data science instructor.

At a high level, Kirill's six data science insights are:

1. Unlike many other careers, there’s no need for formal credentials to become a data scientist.

2. Mentors can be invaluable guides in a DS career, but you should also try to give back to your mentors when you can.

3. Portfolios are the key to landing the DS job of your dream because they showcase your DS abilities for all to see.

4. Hands-on labs are a fun, interactive way to develop your portfolio and are a great complement to classes.

5. Collaborations can make lots of aspects of DS career development fun, including learning new materials, completing labs and developing your portfolio.

6. Data scientists can come from any background and work from anywhere in the world with an Internet connection.

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

How to Become a Data Scientist, with Dr. Adam Ross Nelson

Today's episode features Dr. Adam Ross Nelson providing his #1 most useful piece of guidance on "How to Become a Data Scientist" from his book of that very name!

This was filmed live at the Open Data Science Conference (ODSC) East in Boston last week — thanks ODSC East for providing valuable conference space for us to shoot podcast episodes.

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

The Tidyverse of Essential R Libraries and their Python Analogues, with Dr. Hadley Wickham

Many-time bestselling author and prolific open-source R developer Hadley Wickham is our guest today. In it, we discuss Posit's rebrand and why the Tidyverse needs to be in every data scientist's toolkit.

More on Hadley:
• Chief Scientist at Posit PBC
• Adjunct Professor of Statistics at Stanford University, Rice University and The University of Auckland.
• Is best-known as the creator of the Tidyverse suite of open-source R libraries for data science, including the essential libraries dplyr and ggplot2.
• Has written seminal books on R programming for O'Reilly, Springer and CRC Press, including the mega-bestselling "R for Data Science".

Today’s episode will primarily be of interest to hands-on practitioners like data scientists and machine learning engineers. In it, Hadley details:
• Why the iconic open-source company RStudio rebranded to Posit.
• The philosophy of the tidyverse, amusing backstories on its most iconic packages and why the tidyverse is invaluable for all data scientists to be familiar with.
• The open-source projects he’s most excited about today.
• How you can easily get involved with career-bolstering open-source projects yourself.

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

NumPy, SciPy and the Economics of Open-Source, with Dr. Travis Oliphant

Huge episode today with iconic Dr. Travis Oliphant, creator of NumPy and SciPy, the standard libraries for numeric operations (downloaded 8 million and 3 million times PER DAY, respectively). Hear about the future of open-source, including the impact of GenAI.

More on Travis:

• Founded Anaconda, Inc., the company behind the also-ubiquitous Python package manager.

• Founded the massive PyData conferences and communities as well as its associated non-profit foundation, NumFOCUS.

• Currently serves as the CEO of two firms: OpenTeams and Quansight.

• Holds a PhD in biomedical engineering from the Mayo Clinic in Minnesota.

Today’s episode will primarily be of interest to hands-on practitioners like data scientists, software developers and machine learning engineers.

In it, Travis details:

• How his journey creating open-source software began and how NumPy and SciPy grew to become the most popular foundational Python libraries for working with data.

• How he identifies commercial opportunities to support his vast open-source efforts and communities.

• How AI, particularly generative AI, is transforming open-source development.

• Where open-source innovation is headed in the years to come.

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

Blend Any Programming Languages in Your ML Workflows, with Dr. Greg Michaelson

The revolutionary Zerve IDE for data science launches today! Zerve gives ML teams a unified space to collaborate, build and deploy projects... and is free for most use-cases! In today's episode, Zerve co-founder Dr Greg Michaelson explains all the details.

Greg is a super-insightful, crisp and clear communicator; I think you'll really enjoy our conversation. More on him:
• He's Co-Founder and Chief Product Officer of Zerve, which just raised $3.8m in pre-seed funding.
• Previously spent 7 years as DataRobot’s Chief Customer Officer and 4 years as Senior Director of Analytics & Research for Travelers.
• Was a baptist pastor while he obtained his PhD in Applied Statistics from The University of Alabama — that perhaps explains some of the variance in how he’s such a silver-tongued communicator!

Today’s episode will appeal most to hands-on practitioners like data scientists, machine learning engineers and software developers, but may also be of interest to anyone who wants to stay on top of the latest approaches to developing and deploying ML models.

In this episode, Greg details: 
• Why his swish new Zerve IDE is so sorely needed.
• How their open-source Pypelines project uniquely generates Python code for Automated Machine Learning.
• Why AutoML is not suitable to most commercial use cases.
• Why most commercial A.I. projects fail and how to ensure they succeed.
• The straightforward way you can develop speaking skills as slick as his.

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

Data Science for Clean Energy, with Emily Pastewka

How can data science and machine learning power the transition toward a sustainable global economy? The ML leader (and exceptional communicator of technical concepts!) Emily Pastewka is my guest today to fill us in on Green Data Science.

Emily:

• Leads the data function at Palmetto, a cleantech startup focused on home electrification.

• Prior to Palmetto, spent more than 10 years building consumer data products and solving marketplace problems as a data science and ML leader at huge fast-growing tech companies like Uber and Rent The Runway.

• Holds a Masters degree in Computer Science from Columbia University and undergraduate degrees in Economics and Environmental Policy from Duke.

Today’s episode should be accessible to technical and non-technical folks alike because whenever Emily got technical, she did an exquisite job of explaining the concepts.

In this episode, Emily details:

• How data science and A.I. can make the world greener by shifting us to clean energy.

• The team of people needed to bring cleantech data solutions to life.

• How econometrics plays a key role in nudging consumers toward greener decisions.

• Her top tips for excelling as a data leader.

• What she looks for in the scientists and engineers she hires.

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

Engineering Biomaterials with Generative AI, with Dr. Pierre Salvy

Today, the brilliant Dr. Pierre Salvy details the "double deep-tech sandwich" that blends cutting-edge A.I. (generative LLMs) with cutting-edge bioengineering (creating new materials). This is a fascinating one, shot live at the Merantix AI Campus in Berlin.

Pierre:

• Has been at Cambrium for three years. Initially as Head of Computational Biology and then Head of Engineering for the past two years, growing the team from 2 to 7 to bridge the gap between wet-lab biology, data science, and scientific computing.

• Holds a PhD in Biotechnology from EPFL in Switzerland and a Master’s in Math, Physics and Engineering Science from Mines in Paris.

Today’s episode touches on technical machine learning concepts here and there, but should largely be accessible to anyone.

In it, Pierre details:

• How data-driven R&D allowed Cambrium to go from nothing to tons of physical product sales inside two years.

• How his team leverages Large Language Models (LLMs) to be the biological-protein analogue of a ChatGPT-style essay generator.

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

The Equality Machine

Many recent books and articles spread fear about data collection and A.I. Today's guest, Prof. Orly Lobel, offers the antidote with her book "The Equality Machine" — an optimistic take on the future of data science.

Data Analytics Career Orientation

Considering a Data Analytics career? Today's episode with YouTube icon Luke Barousse (273k subscribers) will be particularly appealing to you, but the terrifically interesting guest makes for an episode that anyone will love.

Luke:
• Is a full-time YouTuber, creating highly educational — but nevertheless hilarious — videos focused on Data Analytics.
• Previously worked as a Lead Data Analyst and Data Engineer at BASF.
• Worked for seven years in the US Navy on nuclear-powered submarines.
• Holds a degree in mechanical engineering, a graduate qualification in nuclear engineering, and an MBA in business analytics.

In this episode, Luke details:
• The must-have skills for entry-level data analyst roles.
• The data analyst skills mistakenly and erroneously pursued by many folks considering the career.
• How his submariner experience prepared him well for a data career.
• His favorite tools for creating interactive data dashboards.
• His favorite scraping libraries for collecting data from the web.
• The skills to learn now to be prepared for the data careers of the future.
• The benefits of CrossFit beyond just the fitness improvements.

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

Resilient Machine Learning

Machine learning is often fragile in production. For today's Five-Minute Friday episode, Dr. Dan Shiebler details how we can make ML more resilient.

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

Software for Efficient Data Science

In today's episode, Dr. Jodie Burchell details a broad range of tools for working efficiently with data, including data cleaning, reproducibility, visualization, and natural language processing.

Jodie:
• Is the Data Science Developer Advocate for JetBrains, the developer-tools company behind PyCharm (one of the most widely-used Python IDEs) and DataLore (their new cloud platform for collaborative data science).
• Previously was Data Scientist or Lead Data Scientist at several tech companies, developing specializations in search, recommender systems, and NLP.
• Co-authored two books on data visualization libraries: "The Hitchhiker's Guide to ggplot2" and "The Hitchhiker's Guide to Plotnine".
• Prior to entering industry, was a postdoctoral fellow in biostatistics at the University of Melbourne.
• Holds a PhD in Psychology from the Australian National University.

Today’s episode is primarily intended for a technical audience as it's packed with practical tips and software for data scientists.

In this episode, Jodie details:
• What a data science developer advocate is and why you might want to consider it as a career option.
• How to work effectively, efficiently, and confidently with real-world data.
• Her favorite Python libraries, such as ones for data viz and NLP.
• How to have reproducible data science workflows.
• The subject she would have majored in if she could go back in time.

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

The Critical Human Element of Successful A.I. Deployments

For today's episode, I sat down with the prolific data-science instructor, author and practitioner Keith McCormick to discuss how critical user considerations are for developing a successful A.I. application.

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