My first TED-format talk is live! In it, I use (A.I.-generated!) visuals to color how A.I. will transform the world in our lifetimes, with particular emphases on climate change, food security, and healthcare innovations.
Thanks to Christina, Banu, and everyone at TEDxDrexelU for inviting me to speak, organizing a slick event, and masterfully editing the footage of my talk.
Thanks to Ed, Andrew, and Shaan at Nebula.io for providing invaluable feedback on drafts of my talk. It's only due to your constructive criticism that the final version turned out as well as it did. Thanks as well to Steven and Alex at Wynden Stark for kindly covering the travel costs of any employees that came down to Philadelphia to see the talk in-person.
Finally, thanks to Taya and Hannah at OpenAI for providing me with early access to custom images from their DALL-E 2 model. These were critical to me being able to tell the effectively convey the narrative I yearned to.
Data Science Interviews with Nick Singh
For an episode all about tips for crushing interviews for Data Scientist roles, our guest is Nick Singh — author of the bestselling "Ace the Data Science Interview" book and creator of the DataLemur SQL interview platform.
Nick:
• Co-authored “Ace the Data Science Interview”, an interview-question guide that has sold over 16,000 copies since it was released last year.
• Created the DataLemur platform for interactively practicing interview questions involving SQL queries.
• Worked as a software engineer at Facebook, Google, and Microsoft.
• Holds a BS in engineering from the University of Virginia.
Today's episode is ideal for folks who are looking to land a data science job for the first time, level-up into a more senior data science role, or perhaps land a data science gig at a new firm.
In this episode, Nick details:
• His top tips for success in data science interviews.
• Common misconceptions about data science interviews.
• How to become comfortable with self-promotion and increase your chances of landing your dream job.
• Strategies for when interviewers ask if you have any questions for them.
• The subject areas and skills you should master before heading into a data science interview.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Thriving on Information Overload
It’s the start of something new, with the first of our extended Five-Minute Friday episodes starting this
week! The author of ‘Thriving on Overload’, Ross Dawson joins Jon to discuss his five powers for
transforming information overwhelm into productivity, abundance and happiness.
Read MoreCausal Machine Learning
Causal ML is today's focus with Dr. Emre Kiciman — Senior Principal Researcher at Microsoft, developer of the DoWhy causal modeling library for Python, and a leader in applying causal research to social sciences.
Emre:
• Has worked within prestigious Microsoft Research for over 17 years.
• Leads Microsoft’s research on Causal Machine Learning.
• Leads development of the DoWhy open-source causal modeling library for Python (part of the PyWhy GitHub project).
• Pioneered the use of social media data to answer causal questions in the social sciences, such as with respect to physical and mental health.
• Has published 100+ papers and been cited 8000+ times.
• Holds a PhD in Computer Science from Stanford University.
Today’s episode is relatively technical, so will probably appeal primarily to folks with technical backgrounds like data scientists, ML engineers, and software developers.
In this episode, Emre details:
• What Causal ML is and how it’s different from "correlational" ML.
• The four key steps of causal inference and how they impact ML.
• The types of data that are most amenable to causal methods and those that aren’t yet… but may be soon.
• Exciting real-world applications of Causal ML.
• The software tools he most highly recommends.
• What he looks for in the data science researchers he hires.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
More Guests on Fridays
Going forward, we are still going to have short, five-minute-ish episodes on Friday that feature me solo, but we will increasingly be interspersing in inspiring guests. And I won’t be making an effort to have these Friday guest episodes be anywhere near five minutes long — to start, I’m thinking of having them typically be 20 to 30 minutes long, but we’ll see how it goes with the guests and what the reception is like from you.
Read MoreOpen-Ended A.I.: Practical Applications for Humans and Machines
In today's remarkable episode, Dr. Kenneth Stanley uses evidence from his machine learning research on Open-Ended A.I. and evolutionary algorithms to inform how you as a human can achieve great life outcomes.
Ken:
• Co-authored the book "Why Greatness Cannot be Planned", a genre-defying book that leverages his ML research to redefine how a human can optimally achieve extraordinary outcomes over the course of their lifetime.
• Was until recently Open-Endedness Team Leader at OpenAI, one of the world’s top A.I. research organizations.
• Led Core A.I. Research for Uber A.I.
• With Prof. Gary Marcus and others, founded A.I. startup Geometric Intelligence, which was acquired by Uber.
• Was Professor of Computer Science at the University of Central Florida.
• Holds a dozen patents for ML innovations, including open-ended and evolutionary (especially neuroevolutionary) approaches.
Today’s episode does get fairly deep into the weeds of ML theory at points so may be best-suited to technical practitioners. That said, the broad strokes of the episode could be not only informative but, again, could indeed be life-perspective-altering for any curious listener.
In this episode, Ken details:
• What genetic ML algos are and how they work effectively in practice.
• How the Objective Paradox — that you fail to achieve an objective you seek — is common across ML and human pursuits.
• How an approach called Novelty Search can lead to superior outcomes than pursuing an explicit objective, again both for machines and humans.
• What Open-Ended A.I. is and its intimate relationship with AGI, a machine with the same learning potential as a human.
• His vision for how A.I. could transform life for humans in the coming decades.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Who Dares Wins
Even if we don’t achieve what we originally set out to achieve, by having dared to achieve it, by having taken action in the direction of the achievement, we learn from the experience and we gain invaluable information about ourselves and the world. Having dared, we find ourselves at a new, enriched vantage point that we otherwise would never have ventured to. From there, whether we achieved the original goal or not, we can iterate — dare again — perhaps to achieve success at the original objective or perhaps we identify some entirely new objective that would have otherwise been inconceivable without having dared.
Read MoreData Mesh
"Data Mesh" may be the trendiest term in data science. What is it and how will its Distributed A.I. transform your organization? The founder of the Data Mesh concept herself, Zhamak Dehghani, explains in this episode.
Zhamak:
• Authored the O'Reilly Media book "Data Mesh" and also co-authored an O’Reilly book on software architecture.
• Is newly the CEO and founder of a stealth tech startup reimagining the future of the data developer experience though the Data Mesh.
• Previously worked as a software engineer, software architect, and as a technology incubation director.
• Holds a Bachelor of Engineering degree in Computer Software from the Shahid Beheshti University in Iran and a Masters in Information Technology Management from the University of Sydney in Australia.
Today’s episode should be broadly interesting to anyone who’s keen to get a glimpse of the future of how organizations will work with data and A.I.
In this episode, Zhamak details:
• What a data mesh is.
• Why data meshes are essential today and will be even more so in the coming years.
• The biggest challenges of distributed data architectures.
• Why now was the right time for her to launch her own data mesh startup.
• Her tricks for keeping pace with the rapid of pace of tech progress.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Daily Habit #11: Assigning Deliverables
To ensure that deliverables are assigned, if you’re running the meeting you can formally set the final meeting agenda item to be something like “assign deliverables”. If you’re not running the meeting, you can suggest having this final agenda item to the meeting organizer at the meeting’s outset or even as the meeting begins to wrap up. By assigning deliverables in this way, we not only make the best use of everyone’s time going forward, but we also maximize the probability that all of the essential action items are actually delivered upon.
Read MoreInferring Causality with Jennifer Hill
Inferring causal direction — as opposed to merely identifying correlations — is central to all real-world data science applications. World-leading expert and author on causality, Prof. Jennifer Hill, is our guest this week.
Jennifer:
• Is Professor of Applied Statistics at New York University, where she researches causality and practical applications of causal research, such as those that are vital to scientific development and government policies.
• Co-directs the NYU Masters in Applied Statistics and directs PRIISM (a center focused on impactful social applications of data science).
• With the renowned statistician Andrew Gelman, wrote the book "Data analysis using regression and multilevel/hierarchical models", an iconic textbook that has been cited over 15k times.
• Holds a PhD in Statistics from Harvard University.
Intended audience:
• Today’s episode largely contains content that will be of interest to anyone who’s keen to better understand the critical concept of causality.
• It also contains technical parts that will appeal primarily to practicing data scientists.
In this episode, Jennifer details:
• How causality is central to all applications of data science.
• How correlation does not imply causation.
• How to design research in order to confidently infer causality from the results.
• Her favorite Bayesian and machine learning tools for making causal inferences within code.
• ThinkCausal, her new graphical user interface for making causal inferences without the need to write code.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Four Thousand Weeks
Assuming you live to be 80 years old, your lifespan will consist of a little over four thousand weeks. If you’re anything like me, feeling as though the weeks seem to fly by in minutes, that means we have startlingly little precious time in our remarkably short lives.
Read MoreUpskilling in Data Science and Machine Learning
This week, iconic Stanford University Deep Learning instructor and entrepreneur Kian Katanforoosh details how ML powers his EdTech platform Workera, enabling you to systematically fill gaps in your data science skills.
Kian:
• Is Co-Founder and CEO of Workera, a Bay Area education technology company that has raised $21m in venture capital to upskill workers, with a particular early focus on upskilling technologists like data scientists, software developers, and machine learning specialists.
• Is a lecturer of computer science at Stanford University (specifically, he teaches the extremely popular CS230 Deep Learning course alongside Prof. Andrew Ng, one of the world’s best-known data scientists).
• Was awarded Stanford’s highest teaching award.
• Is also a founding member of DeepLearning.AI, a platform through which he’s taught over three million students deep learning.
• Holds a Masters in Math and Computer Science from CentraleSupélec.
• Holds a Masters in Management Science and Engineering from Stanford.
By and large, today’s episode will appeal to any listener who’s keen to understand the latest in education technology, but there are parts here and there that will specifically appeal to practicing technologists like data scientists and software developers.
In this episode, Kian details:
• What a skills intelligence platform is.
• Four ways that machine learning drives his skills intelligence platform.
• What frameworks and software languages they selected for building their platform and why.
• What he looks for in the data scientists and software engineers he hires.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Ignition: A Landmark Nuclear Fusion Milestone is Achieved
With nuclear fusion, we’d be able to supply energy to everyone on the planet without burning fossil fuels — indeed, we could use surplus energy to decarbonize our atmosphere and reverse some of the climate change damage humans have caused since the dawn of our industrial revolution.
So if fusion is such a game-changer for humanity, why haven’t we been focusing our enormous amounts of resources on it to obtain it?
Read MoreGeospatial Data and Unconventional Routes into Data Careers
This week, the remarkably well-read Christina Stathopoulos, details open-source software for working with geospatial data... as well as how you can navigate your data-career path, no matter what your background.
Christina:
• Has worked at Google for nearly five years in several data-centric roles.
• For the past year, she’s worked as an Analytical Lead for Waze, the popular crowdsourced navigation app owned by Google.
• Is also an adjunct professor at IE Business School School in Madrid, where she teaches courses on business analytics, machine learning, data visualization, and data ethics.
• Previously worked as a data engineer at media analytics giant Nielsen.
• Holds a Master’s in Business Analytics and Big Data from IE Business School and a Bachelor’s in Science, Tech, and Society from North Carolina State University.
Today’s episode will appeal to a broad audience of technical and non-technical listeners alike.
In this episode, Christina details:
• Geospatial data and open-source packages for working with it.
• Her tips for getting a foothold in a data career if you come from an unconventional background.
• Guidance to help women and other underrepresented groups thrive in tech.
• The hard and soft skills most essential to success in a data role today.
• Her #bookaweekchallenge and her top data book recommendations.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Guest appearance on The Evan Solomon Show: Mimicking the Voice of Dead Relatives- The Future of Voice Cloning and A.I.
Had a fun time getting back on the Evan Solomon talk-radio show this week... This time to discuss voice-mimicking A.I. that (among presumably other applications) allows your dead relatives to read you bedtime stories.
We Are Living in Ancient Times
This article was originally adapted from a podcast, which you can check out here.
James Clear recently drew my attention to a quote by the writer Teresa Nielsen Hayden that I find fascinating and mind-boggling… and that I also vehemently agree with:
“My own personal theory is that this is the very dawn of the world. We are hardly more than an eye blink away from the fall of Troy, and scarcely an interglaciation removed from the Altamira cave painters. We live in extremely interesting ancient times.”
Read MoreVenture Capital for Data Science
Keen to get the inside scoop on the Venture Capital industry and tech startup investing? Sarah Catanzaro, an eloquent VC who specializes in growing the value of Data Science companies, is our guest this week.
Sarah:
• Is a General Partner at Amplify Partners, a Bay Area venture capital firm that specializes in investing in early-stage start-ups that are pioneering new applications of data science, analytics, and machine learning.
• Previously she worked as an investor at Canvas Ventures, as Head of Data at Mattermark, and as an embedded analyst at Palantir.
• She holds a Bachelor of Science degree from Stanford University.
Today’s episode will appeal to anyone who’s keen to understand investing in early-stage start-ups.
In this episode, Sarah details:
• What venture capital is and how it differs from private equity investment.
• How to go from a data science idea to obtaining funding.
• How to pick winning investments.
• What start-ups can do to survive or raise capital in the current economic climate.
• The lessons she’s learned from ten years of experience in the field of data science.
• How to break into the field of venture capital yourself.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Yoga Nidra Practice
Rest and relaxation await as Steve Fazzari joins us this week for a special edition of the podcast! Tune in for a rejuvenating session of Yoga Nidra led beautifully by the expert.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
MLOps: Machine Learning
Analogous to the role DevOps plays for software development, MLOps enables efficient ML training and deployment. MLOps expert Mikiko Bazeley is our guide!
Mikiko:
• Is a Senior Software Engineer responsible for MLOps at Intuit Mailchimp.
• Previously held technical roles at a range of Bay Area startups, with responsibilities including software engineering, MLOps, data engineering, data science, and data analytics.
• Is a prominent content creator on MLOps – across live workshops, her YouTube channel, her personal blog, and the NVIDIA blog.
Today’s episode will appeal primarily to hands-on practitioners such as data scientists and software engineers.
In this episode, Mikiko details:
• What MLOps is.
• Why MLOps is critical for the efficiency of any data science team.
• The three most important MLOps tools.
• The four myths holding people back from MLOps expertise.
• The six most essential MLOps skills for data scientists.
• Her productivity tricks for balancing software engineering, content creation, and her athletic pursuits.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Getting Kids Excited about STEM Subjects
For the fourth and final Friday episode featuring the inimitable Ben Taylor, he provides guidance on how to get kids excited about STEM (science, tech, engineering, math) subjects.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.