This week's guest is the eloquent and inspiring James Hodson, founder and CEO of the AI for Good Foundation, which leverages data and machine learning to tackle the United Nations' Sustainable Development Goals.
In this episode, James details:
• Globally impactful case studies from his A.I. for Good organization across public health, DEI, and a practical database of A.I. progress on social issues
• How you yourself can get involved in helping apply A.I. for wide-reaching social benefit, whether you're a technical expert or not
• The hard and soft skills that he looks for in the data scientists that he hires
In addition to his leadership of A.I. for Good, James:
• Is an academic research fellow at the Jozef Stefan Institute, where he's focused on Natural Language Processing research
• Is Chief Science Officer at Cognism, a British tech startup
• Served as A.I. Research Manager at Bloomberg LP
• Completed a degree at Princeton University focused on Machine Translation
Thank you to Claudia Perlich for the intro to James! I learned a ton from him while filming this episode.
The episode's available on all major podcasting platforms, on YouTube, and at SuperDataScience.com.
Filtering by Category: Interview
Courses in Data Science and Machine Learning
This week's guest is super fun Sadie St. Lawrence, an exceptionally popular data science instructor with over 300k students all-time. She fills us in on her exciting new ML Certificate and the global impact of her Women In Data org.
Sadie:
• Teaches data science at the University of California, Davis
• Her Coursera course is one of the all-time most popular
• Is Founder and CEO of Women in Data, a community of 20k across 15 countries
• Holds a Master's in Analytics from Villanova University
In this episode, she digs into:
• The content of her existing iconic data science course
• The curriculum of her epic forthcoming Machine Learning Certificate
• The mission, impact, and vision of the Women in Data organization
• Her path into data science from music performance
• Non-fungible tokens (NFTs) and the future of technology
Thanks to Harpreet Sahota for introducing me to Sadie! I absolutely loved filming this episode.
Listen or watch here.
Accelerating Impact through Community — with Chrys Wu
This week's guest is global tech community builder Chrys Wu who details how you too can leverage communities to accelerate your career. This is the first SuperDataScience episode ever recorded in-person!
In addition to accelerating your career with community, Chrys covers:
• K-pop music and its associated cultural movement
• How the Write/Speak/Code and Hacks/Hackers organizations she co-founded leverage community to make a massive global impact for marginalized genders and journalism, respectively
• Her top resources — social media accounts, blogs, and podcasts — for staying abreast of the latest in data science and machine learning
Chrys is a consultant who specializes in product development and change management. She's also a co-founder of both Write/Speak/Code and Hacks/Hackers, the latter of which has grown to 70 chapters across five continents.
Listen or watch here.
Transformers for Natural Language Processing
This week's guest is award-winning author Denis Rothman. He details how Transformer models (like GPT-3) have revolutionized Natural Language Processing (NLP) in recent years. He also explains Explainable AI (XAI).
Denis:
• Is the author of three technical books on artificial intelligence
• His most recent book, "Transformers for NLP", led him to win this year's Data Community Content Creator Award for technical book author
• Spent 25 years as co-founder of French A.I. company Planilog
• Has been patenting A.I. algos such as those for chatbots since 1982
In this episode, Denis fills us in on:
• What Natural Language Processing is
• What Transformer architectures are (e.g., BERT, GPT-3)
• Tools we can use to explain *why* A.I. algorithms provide a particular output
We covered audience questions from Serg, Chiara, and Jean-charles during filming. For those we didn't get to ask, Denis is kindly answering via a LinkedIn post today!
The episode's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Upcoming guest on the SuperDataScience Podcast: Wes McKinney
Next week, I'm interviewing the monumental Wes McKinney — creator of pandas, co-creator of Apache Arrow, and bestselling author of "Python for Data Analysis" — for a SuperDataScience episode.
Got Qs for him? Tweet them @jonkrohnlearns or send them to me on LinkedIn.
Data Science for Private Investing — LIVE with Drew Conway
This week's guest is prominent data scientist and author Dr. Drew Conway. Working at Two Sigma, one of the world's largest hedge funds, Drew leads data science for private markets (e.g., real estate, private equity).
If you aren't familiar with Drew already, he:
• Serves as Senior Vice President for data science at Two Sigma
• Co-authored the classic O'Reilly Media book "ML for Hackers"
• Was co-founder and CEO of Alluvium, which was acquired in 2019
• Advised countless successful data-focused startups (e.g., Yhat, Reonomy)
• Obtained a PhD in politics from New York University
In this episode, he covers:
• What private investing is
• How data science can lead to better private investment decisions
• The differences between creating and executing models for public markets (such as stock exchanges) relative to private markets
• What he looks for in the data scientists he hires and how he interviews them
This is a special SuperDataScience episode because it's the first one recorded live in front of an audience (at the The New York R Conference in September). Eloquent Drew was the willing guinea pig for this experiment, which was a great success: We filmed in a single unbroken take and fielded excellent audience questions.
Listen or watch here.
Accelerating Start-up Growth with A.I. Specialists
This week's guest is the game-changing Dr. Parinaz Sobhani. She leads ML at Georgian — a private fund that sends her "special ops" data science teams into its portfolio companies to accelerate their A.I. capabilities.
In this episode, Parinaz details:
• Case studies of Georgian's A.I. approach in action across industries (e.g. insurance, law, real estate)
• Tools and techniques her team leverages, with a particular focus on the transfer learning of transformer-based models of natural language
• What she looks for in the data scientists and ML engineers she hires
• Environmental and sociodemographic considerations of A.I.
• Her academic research (Parinaz holds a PhD in A.I. from the University of Ottawa where she specialized in natural language processing)
Listen or watch here.
...and thanks to Maureen for making this connection to Parinaz!
Building Your Ant Hill
Five-Minute Friday today features my 91-year-old grandmother sharing her insightful life philosophy that centers around an analogy of ants building ant hills.
Listen here.
Bayesian Statistics
Expert Rob Trangucci joins me this week to provide an introduction to Bayesian Statistics, a uniquely powerful data-modeling approach.
If you haven't heard of Bayesian Stats before, today's episode introduces it from the ground up. It also covers why in many common situations, it can be more effective than other data-modeling approaches like Machine Learning and Frequentist Statistics.
Today's episode is a rich resource on:
• The centuries-old history of Bayesian Stats
• Its particular strengths
• Real-world applications, including to Covid epidemiology (Rob's particular focus at the moment)
• The best software libraries for applying Bayesian Statistics yourself
• Pros and cons of pursuing a PhD in the data science field
Rob is a core developer on the open-source STAN project — a leading Bayesian software library. Having previously worked as a statistician in renowned professor Andrew Gelman's lab at Columbia University in the City of New York, Rob's now pursuing a PhD in statistics at the University of Michigan.
Listen or watch here.
From Data Science to Cinema
SuperDataScience SuperStar Hadelin returns to report on his journey from multi-million-selling video instructor to mainstream-film actor — and he details the traits that allow data scientists to succeed at anything.
Hadelin has created and presented 30 extremely popular Udemy courses on machine learning topics, selling over two million copies so far. Prior to his epic creative period publishing ML courses, Hadelin studied math, engineering and A.I. at the Université Paris-Saclay and he worked as a data engineer at Google. More recently Hadelin has written a book called "A.I. Crash Course" and was co-founder and CEO of BlueLife AI.
Today's episode focuses on:
• Hadelin's recent shift toward acting in mainstream films
• The characteristics that enable an outstanding data scientist to excel in any pursuit
• How to cultivate your passion and achieve your dreams
• Bollywood vs Hollywood
• How to prepare for the TensorFlow Certificate Program
• Software modules for deploying deep learning models into production
Listen or watch here.
Deep Reinforcement Learning for Robotics with Pieter Abbeel
Very special guest this week! Pieter Abbeel is a serial A.I. entrepreneur, host of star-studded The Robot Brains Podcast, and the world's most preeminent researcher of Deep Reinforcement Learning applications.
As a professor of Electrical Engineering and Computer Science at the University of California, Berkeley, Pieter directs the Berkeley Robot Learning Lab and co-directs the Berkeley A.I. Research Lab.
As an entrepreneur, he's been exceptionally successful at applying machine learning for commercial value. Gradescope, a machine learning company in the education technology space that he co-founded, was acquired in 2018. And the A.I. robotics firm Covariant, which he co-founded more recently, has raised $147 million so far, including raising $80 million in a Series C funding round in July.
In this episode, Pieter eloquently discusses:
• His exciting current research in the field of Deep Reinforcement Learning
• Top learning resources and skills for becoming an expert in A.I. robotics
• How academic robotics research is vastly different from R&D for industry
• Productivity tips
• Traits he looks for in data scientists he hires
• Skills to succeed as a data scientist in the coming decades
He also had time to answer thoughtful questions from distinguished SuperDataScience listeners Serg Masís and Hsieh-Yu Li.
Listen or watch here.
Statistical Programming with Friends with Jared Lander
This week's guest is THE Jared Lander! He fills us in on real-life communities that support learning about — and effectively applying — open-source statistical-programming languages like Python and R.
In addition, Jared:
• Overviews what data-science consulting is like (with fascinating use-cases from industrial metallurgy to "Money Ball"-ing for the Minnesota Vikings)
• Details the hard and soft skills of successful data-science consultants
• Ventures eloquently into the age-old R versus Python debate
Jared leads the New York Open Statistical Programming Meetup, which is the world's largest R meetup — but it also features other open-source programming languages like Python — for talks from global leaders in data science and machine learning. And Jared runs the R Conference, which is approaching its seventh annual iteration next week, Sep 9-10.
Jared also wrote the bestselling book "R for Everyone" and teaches stats at both Columbia University in the City of New York and Princeton University. And none of the massive responsibilities that I've just mentioned are Jared's day job! Nope, for that he's the CEO and Chief Data Scientist of Lander Analytics, a data-science consulting firm.
Watch or listen here.
P.S.: Jared is kindly providing 20% off admission to next week's R Conference off using promo code SDS20. See rstats.nyc for more details, including the first-ever live episode of SuperDataScience (with Drew Conway as guest)!
Yoga Nidra
Episode 500 of the SuperDataScience podcast is live today! For this special occasion, world-class yogi Jes Allen guides us through a full, deep session of Yoga Nidra — a centering and transformative meditation-like experience.
I'm so excited to share this practice with you and can't wait to hear what you think of it! Thank you to all of you listeners — as well as of course SuperDataScience founder / 400-plus-episode-host Kirill Eremenko — for bringing this podcast to where it is today. And none of this would be possible without the hundreds of inspiring guests we've had over the years, the indefatigable show manager Ivana, and the awesome production team: Mario, Jaime, and JP.
I am honored and grateful to be able to serve all of you and walk alongside you in your data-science career journey. Keep on rockin'! 🎸
You can listen to or watch the episode here.
Data Meshes and Data Reliability
The fun and brilliant Barr Moses joins me this week to detail for us what organization-transforming Data Meshes are, as well as how to track and improve the "Data Uptime" (reliability) of your production systems.
Barr is co-founder and CEO of Monte Carlo, a venture capital-backed start-up that has grown in head count by a remarkable 10x in the past year. Monte Carlo specializes in data reliability, making sure that the data pipelines used for decision-making or production models are available 24/7 and that the data are high quality.
In this SuperDataScience episode, Barr covers:
• What data reliability is, including how we can monitor for the "good pipelines, bad data" problem
• How reliable data enables the creation of a Data Mesh that empowers data-driven decision-makers across all of the departments of a company to independently create and analyze data
• How to build a data science team
• How to get a data-focused start-up off the ground, generating revenue and rapidly scaled up
In addition, Barr took time to answer questions from listeners, including those from Svetlana, Bernard, and A Ramesh. Thanks to Scott Hirleman for suggesting Barr as a guest on the show and thanks to Molly Vorwerck for ensuring everything ran perfectly.
Listen or watch here.
AI Recruitment Technology & Deep Learning - Guest Appearance on the Engineered-Mind Podcast
Thanks to Jousef Murad for having me on the popular Engineered-Mind podcast.! Jousef had deeply insightful questions and I enjoyed the experience immensely :)
I spoke with Jousef back in April 2021, where we discussed:
- untapt and how AI powered recruiting works
- My background in neuroscience
- Where to get started when learning ML
- Tips for becoming a deep learning specialist
- What is I’m most excited about in terms of AI
- How I come up with the idea of writing a book
You can listen to the podcast anywhere podcasts are available including Apple Podcasts, Spotify, and Anchor.fm. You can also check out the video directly on YouTube here.
Maximizing the Global Impact of Your Career
This week, expert Benjamin Todd details how you can find purpose in your work and maximize the global impact of your career. In particular, he emphasizes how data scientists can exert a massive positive influence.
In this mind-expanding and exceptionally inspiring episode, Ben details:
• An effective process for evaluating next steps in your career
• A data-driven guide to the most valuable skills for you to obtain regardless of profession
• Specific impact-maximizing career options that are available to data scientists and related professionals, such as ML engineers and software developers.
Ben has invested the past decade researching how people can have the most meaningful and impactful careers. This research is applied to great effect via his charity 80,000 Hours, which is named after the typical number of hours worked in a human lifetime. The Y Combinator-backed charity has reached over eight million people via its richly detailed, exceptionally thoughtful, and 100% free content and coaching.
Listen or watch here.
Successful AI Projects and AI Startups
This week, the rockstar Greg Coquillo fills us in on how to get a return on investment in A.I. projects and A.I. start-ups. He also introduces Quantum Machine Learning.
In addition, through responding to audience questions, Greg details:
• Element AI's maturity framework for A.I. businesses
• How A.I. startup success comes from understanding your long-term business strategy while iterating tactically
• How machines typically are much faster than people but tend to be less accurate
(Thanks to Bernard, Serg, Kenneth, Nikolay, and Yousef for the questions!)
Greg is LinkedIn's current "Top Voice for A.I. and Data Science". When he's not sharing succinct summaries of both technically-oriented and commercially-oriented A.I. developments with his LinkedIn followers, Greg's a technology manager at Amazon's global HQ in Seattle. Originally from Haiti, Greg obtained his degrees in industrial engineering and engineering management from the University of Florida before settling into a series of management-level process-engineering roles.
Listen or watch here.
Bringing Data to the People
This week's guest is super-cool Anjali Shrivastava. Anjali makes data accessible and broadly appealing by analyzing pop culture — from TikTok mansions to Star Wars timelines — in her fun and creative YouTube videos.
Anjali is an expert in data-science visualization. She has used this skill set to engineer visualizations of data in production systems in a number of roles and recently took up a data science role at the lab technology giant Thermo Fisher Scientific.
We dig into her technical expertise, including her favorite software tools and applications for viz. We also discuss Anjali's mission to bring a face to data, which she accomplishes through journalism as well as through her brilliant and fun "Vastava" YouTube channel.
Anjali holds dual degrees from the prestigious University of California, Berkeley in data science, as well as in industrial engineering and operations research. A recent graduate, she fill us in on what a data science degree curriculum is like at a top university like Berkeley, as well as how anyone can access their world class data science lectures online.
Listen or watch here.