The numbers are in and the top 10 SuperDataScience episodes of 2022 are finally here! Whether you’re new to the podcast or a seasoned listener, you won’t want to miss these value-packed hours that our audience just couldn’t get enough of.
The SuperDataScience Podcast has grown a whopping 90% in 2022, and it’s all thanks to you! Your word-of-mouth recommendations have helped us nearly double our audience size and take in over 3.3 million downloads and YouTube views!
With 2023 already shaping up to be our biggest year yet, Jon is here to highlight the top ten data science podcasts of 2022 for all of our newest listeners who don’t want to miss out on our most popular episodes.
10 – SDS 637: How to Influence Others with Your Data — with Ann K. Emery
Do you think you can skip the data visualization and storytelling skills as a data scientist? Think again! Owner of Depict Data Studio, Ann K. Emery delivers a practical episode packed with tips on effective data visualization, data presentation, and data storytelling.
9 – SDS 537: Data Science Trends for 2022 — with Sadie St. Lawrence
Always a crowd favourite, founder and CEO of Women in Data, Sadie St. Lawrence returns to the podcast to deliver our roundup of data science trends for the year ahead.
8 – SDS 605: Upskilling in Data Science and Machine Learning — with Kian Katanforoosh
Stanford lecturer and Workera CEO joined us to discuss the machine learning models, tools, and frameworks that power his platform and remote team.
7 – SDS 569: A.I. For Crushing Humans at Poker and Board Games — with Noam Brown
Research Scientist at Meta AI, Noam Brown sits down with Jon to chat about his award-winning no-limit poker-playing algorithms, and the real-world implications of his AI breakthroughs.
6 – SDS 619: Tools for Deploying Data Models into Production — with Erik Bernhardsson
Is there anything he can’t do? Erik Bernhardsson took Spotify from digital music startup to AI-driven streaming giant, founded Modal Labs to help engineers build and scale tools, and now he’s sitting down with Jon to tell us how he got it all done.
5 – SDS 563: How to Rock at Data Science — with Tina Huang
Ever wonder what it’s like to work at one of the world’s largest tech companies? Youtuber extraordinaire, Tina Huang, is here to demystify the most-wanted data science role, and reveals how to prepare for it from scratch.
4 – SDS 623: Data Analyst, Data Scientist, and Data Engineer Career Paths — with Shashank Kalanithi
Shashank Kalanithi is a data engineer working in the sports betting industry. In this episode, the self-taught data science Youtuber discusses the essential differences between data science roles.
3 – SDS 639: Simplifying Machine Learning — with Mariya Sha
Mariya Sha, creator of the wildly popular Python Simplified YouTube channel, teams up with Jon for putting Python into practice, how to hack your productivity and how to start creating your own data science videos.
2 – SDS 607: Inferring Causality — with Jennifer Hill
Dr. Jennifer Hill, Professor of Applied Statistics at New York University, sits down with Jon and gets real about the role of causality in data science applications and how to infer causality from your results confidently.
1 – SDS 557: Effective Pandas — with Matt Harrison
Best-selling author and Python and Data Science expert, Matt Harrison graces the podcast and shares his top Pandas tips, tricks and best practices to help you get the most out of the Python data analysis library.
ITEMS MENTIONED IN THIS PODCAST:
- SDS 640: What I Learned in 2022
- SDS 637: How to Influence Others with Your Data — with Ann K. Emery
- SDS 537: Data Science Trends for 2022 — with Sadie St. Lawrence
- SDS 605: Upskilling in Data Science and Machine Learning — with Kian Katanforoosh
- SDS 569: A.I. For Crushing Humans at Poker and Board Games — with Noam Brown
- SDS 619: Tools for Deploying Data Models into Production — with Erik Bernhardsson
- SDS 563: How to Rock at Data Science — with Tina Huang
- SDS 623: Data Analyst, Data Scientist, and Data Engineer Career Paths — with Shashank Kalanithi
- SDS 639: Simplifying Machine Learning — with Mariya Sha
- SDS 607: Inferring Causality — with Jennifer Hill
- SDS 557: Effective Pandas — with Matt Harrison
- SDS 552: The Most Popular SuperDataScience Episodes of 2021
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Podcast Transcript
(00:05):
This is Five-Minute Friday on the Most Popular Episodes of 2022.
(00:27):
As I mentioned in my year-end recap episode on December 29th, in 2022 this show enjoyed growth of 90% — that means the show’s audience nearly doubled relative to 2021, netting over 3.3 million podcast downloads and YouTube views in 2022. So, first off, thank you so much for listening to the show and for telling your friends and colleagues about the show — essentially all of our growth is organic so we’re highly dependent on your personal recommendations.
As I mentioned in my year-end recap episode on December 29th, in 2022 this show enjoyed growth of 90% — that means the show’s audience nearly doubled relative to 2021, netting over 3.3 million podcast downloads and YouTube views in 2022. So, first off, thank you so much for listening to the show and for telling your friends and colleagues about the show — essentially all of our growth is organic so we’re highly dependent on your personal recommendations.
(00:55):
Anyway, given our near-doubling in 2022, I didn’t think the show could be growing much faster and thus I’ve been delightfully surprised that so far in 2023, growth has accelerated, with January being by far our biggest month ever and February not far behind.
Anyway, given our near-doubling in 2022, I didn’t think the show could be growing much faster and thus I’ve been delightfully surprised that so far in 2023, growth has accelerated, with January being by far our biggest month ever and February not far behind.
(01:14):
With all of you new SuperDataScience listeners out there then, I put together today’s episode to fill you in on the most listened-to episodes of 2022, giving you a data-backed set of outstanding episodes that you might want to go back to and check out if you’re hankering for more content. For veteran listeners, this episode could be informative too: It’ll ensure that you didn’t miss any of the most popular episodes from last year that sound interesting to you.
With all of you new SuperDataScience listeners out there then, I put together today’s episode to fill you in on the most listened-to episodes of 2022, giving you a data-backed set of outstanding episodes that you might want to go back to and check out if you’re hankering for more content. For veteran listeners, this episode could be informative too: It’ll ensure that you didn’t miss any of the most popular episodes from last year that sound interesting to you.
(01:38):
One thing you might be wondering is why I’m airing a best-of-2022 episode in March. Well, there are two factors. First, internally at the SuperDataScience Podcast we use the 30-day mark after an episode’s release as our quantitative Key Performance Indicator as to how an episode’s been received by you. Second, I typically record episodes several weeks ahead of their release to give our production team plenty of time to clean them up real nice for you and to leave a few episodes in the pipeline in case I get sick.
One thing you might be wondering is why I’m airing a best-of-2022 episode in March. Well, there are two factors. First, internally at the SuperDataScience Podcast we use the 30-day mark after an episode’s release as our quantitative Key Performance Indicator as to how an episode’s been received by you. Second, I typically record episodes several weeks ahead of their release to give our production team plenty of time to clean them up real nice for you and to leave a few episodes in the pipeline in case I get sick.
(02:07):
All right, explanations out of the way, let’s dig into — quantitatively-speaking — the ten top-performing episodes of 2022.
All right, explanations out of the way, let’s dig into — quantitatively-speaking — the ten top-performing episodes of 2022.
(02:15):
The tenth-most popular episode featured Ann Emery providing a slick overview of how to influence others with your data. This was a tremendously practical episode packed with tips on effective data visualization, data presentation, and data storytelling. Evidently, a lot of folks were hankering for this practical info, so thanks, Ann!
The tenth-most popular episode featured Ann Emery providing a slick overview of how to influence others with your data. This was a tremendously practical episode packed with tips on effective data visualization, data presentation, and data storytelling. Evidently, a lot of folks were hankering for this practical info, so thanks, Ann!
(02:34):
The ninth-most popular episode starred the sage Sadie St. Lawrence, who — in the first episode of 2022 — predicted, remarkably accurately, it turns out, the data science trends for the year ahead. Special mention to Sadie for cracking the top ten two years in a row! She’s our only guest who have done that.
The ninth-most popular episode starred the sage Sadie St. Lawrence, who — in the first episode of 2022 — predicted, remarkably accurately, it turns out, the data science trends for the year ahead. Special mention to Sadie for cracking the top ten two years in a row! She’s our only guest who have done that.
(02:51):
In eighth place is an episode with Kian Katanforoosh, a renowned Stanford lecturer and CEO of Workera, a fast-growing platform for data scientists, software engineers, and other technical practitioners to upskill systematically while staying in their job.
In eighth place is an episode with Kian Katanforoosh, a renowned Stanford lecturer and CEO of Workera, a fast-growing platform for data scientists, software engineers, and other technical practitioners to upskill systematically while staying in their job.
(03:05):
In seventh place is an episode featuring Dr. Noam Brown. His episode — on A.I. for crushing humans at poker — is close to my heart because it was the first one ever recorded with a live audience. I was nervous about it for months and so it’s a huge relief to me that the inaugural live recording was well-received by SuperDataScience listeners. If you’re looking for more info on game-playing A.I., the very next episode of this show, #663, which will be released on Tuesday, features Dr. Brown’s colleague Alexander Holden Miller detailing Meta’s astounding new A.I. that uses natural language to negotiate and build trust with humans in order to excel at an extremely complex board game. It’s an extraordinary achievement — in my view, a much bigger deal than ChatGPT — and so I highly recommend checking that forthcoming episode out.
In seventh place is an episode featuring Dr. Noam Brown. His episode — on A.I. for crushing humans at poker — is close to my heart because it was the first one ever recorded with a live audience. I was nervous about it for months and so it’s a huge relief to me that the inaugural live recording was well-received by SuperDataScience listeners. If you’re looking for more info on game-playing A.I., the very next episode of this show, #663, which will be released on Tuesday, features Dr. Brown’s colleague Alexander Holden Miller detailing Meta’s astounding new A.I. that uses natural language to negotiate and build trust with humans in order to excel at an extremely complex board game. It’s an extraordinary achievement — in my view, a much bigger deal than ChatGPT — and so I highly recommend checking that forthcoming episode out.
(03:55):
Oops, I’m supposed to be focusing on the past! Ok, back to the countdown: The sixth most listened-to episode in 2022 was with the renowned and exceptionally wise technologist Erik Bernhardsson covering tools for deploying data models efficiently into production.
Oops, I’m supposed to be focusing on the past! Ok, back to the countdown: The sixth most listened-to episode in 2022 was with the renowned and exceptionally wise technologist Erik Bernhardsson covering tools for deploying data models efficiently into production.
(04:09):
In fifth was YouTube superstar Tina Huang who opened up about her typical workday at one of the world’s largest tech companies, her strategies for efficient learning, and how best to prepare for a career in data science from scratch.
In fifth was YouTube superstar Tina Huang who opened up about her typical workday at one of the world’s largest tech companies, her strategies for efficient learning, and how best to prepare for a career in data science from scratch.
(04:21):
In fourth was another YouTube sensation, Shashank Kalanathi, who detailed how to get started in a data analytics career and then where you can grow to from that first data analyst role.
In fourth was another YouTube sensation, Shashank Kalanathi, who detailed how to get started in a data analytics career and then where you can grow to from that first data analyst role.
(04:32):
Spotting a clear trend here, our bronze medal goes to yet another YouTuber, this time the brilliant mind behind the mega-popular Python Simplified channel, that’s Ms. Mariya Sha. In her wide-ranging and deeply philosophical appearance, amongst many topics Mariya covered how you can make learning any new machine learning concept much simpler.
Spotting a clear trend here, our bronze medal goes to yet another YouTuber, this time the brilliant mind behind the mega-popular Python Simplified channel, that’s Ms. Mariya Sha. In her wide-ranging and deeply philosophical appearance, amongst many topics Mariya covered how you can make learning any new machine learning concept much simpler.
(04:53):
In second place — our silver medalist — was New York University professor Jennifer Hill. A personal icon of mine — whose statistical modeling textbook I fell in love with when I was starting my PhD 16 years ago — Prof. Hill’s episode dug into how to design experiments in order to confidently infer causality from the results as well as her favorite Bayesian methods for analyzing causal direction. Hmm, 2021’s most popular episode also featured Bayesian stats so it seems like I should be booking for more Bayesian experts in 2023!
In second place — our silver medalist — was New York University professor Jennifer Hill. A personal icon of mine — whose statistical modeling textbook I fell in love with when I was starting my PhD 16 years ago — Prof. Hill’s episode dug into how to design experiments in order to confidently infer causality from the results as well as her favorite Bayesian methods for analyzing causal direction. Hmm, 2021’s most popular episode also featured Bayesian stats so it seems like I should be booking for more Bayesian experts in 2023!
(05:24):
Finally… are you ready for it? In first place this year, the most popular SuperDataScience episode of 2022 featured the author and educator Matt Harrison’s episode on effectively programming in the ubiquitous Pandas library for data processing. Matt’s highly practical episode – #557 – has already ratcheted up over 60,000 listens with no doubt tens of thousands more still to come.
Finally… are you ready for it? In first place this year, the most popular SuperDataScience episode of 2022 featured the author and educator Matt Harrison’s episode on effectively programming in the ubiquitous Pandas library for data processing. Matt’s highly practical episode – #557 – has already ratcheted up over 60,000 listens with no doubt tens of thousands more still to come.
(05:50):
What’s even more impressive about Matt’s stand-out episode — as well as others in the top ten from the first few months of last year such as Sadie, Tina, and Noam’s episode — is that my simple popularity-assessing methodology for this episode contains a critical flaw. Because the SuperDataScience podcast became significantly more popular each quarter of last year, guests whose episodes aired near the end of the year had an advantage over those whose episodes aired earlier. As I mentioned in my top-ten-episodes-of-2021 episode a year ago, it would have been more sophisticated to, say, fit locally estimated scatterplot smoothing to the data and then rank the episodes that had the largest residual above the regression curve. I considered that again this year, but then it adds a bit of voodoo and controversy to my results, for example, did I choose the correct regression method? What if I picked a different one? And so for yet another year, I’m considering my easy-to-understand approach of taking listens at the 30-day mark to be sufficient.
What’s even more impressive about Matt’s stand-out episode — as well as others in the top ten from the first few months of last year such as Sadie, Tina, and Noam’s episode — is that my simple popularity-assessing methodology for this episode contains a critical flaw. Because the SuperDataScience podcast became significantly more popular each quarter of last year, guests whose episodes aired near the end of the year had an advantage over those whose episodes aired earlier. As I mentioned in my top-ten-episodes-of-2021 episode a year ago, it would have been more sophisticated to, say, fit locally estimated scatterplot smoothing to the data and then rank the episodes that had the largest residual above the regression curve. I considered that again this year, but then it adds a bit of voodoo and controversy to my results, for example, did I choose the correct regression method? What if I picked a different one? And so for yet another year, I’m considering my easy-to-understand approach of taking listens at the 30-day mark to be sufficient.
(06:48):
Speaking of the show becoming more popular over time, to give you a sense of how much the show has taken off in recent months, the first five episodes of 2023 all would have cracked into 2022’s top ten. Q1 of 2023 could be the first time we hit one million listens in a single quarter — not only are we the most listened-to show in the data science industry, we’re also in the top 15 technology podcasts and are tantalizingly close to breaking into top 1000 podcasts worldwide across any category. I think this is pretty impressive given the narrow niche data science is, so, as I said at the beginning of this episode, thank you so much for listening and spreading the word about our show!
Speaking of the show becoming more popular over time, to give you a sense of how much the show has taken off in recent months, the first five episodes of 2023 all would have cracked into 2022’s top ten. Q1 of 2023 could be the first time we hit one million listens in a single quarter — not only are we the most listened-to show in the data science industry, we’re also in the top 15 technology podcasts and are tantalizingly close to breaking into top 1000 podcasts worldwide across any category. I think this is pretty impressive given the narrow niche data science is, so, as I said at the beginning of this episode, thank you so much for listening and spreading the word about our show!
(07:31):
All right, that’s it for Five-Minute Friday today. Until next time, keep on rockin’ it out there, folks, and I’m looking forward to enjoying another round of the SuperDataScience podcast with you very soon.
All right, that’s it for Five-Minute Friday today. Until next time, keep on rockin’ it out there, folks, and I’m looking forward to enjoying another round of the SuperDataScience podcast with you very soon.
