The Best Learning Paths for AI and Data Leadership | by Cassie Kozyrkov | Apr, 2023
Making Data Useful
How to muscle up on data-related topics quickly
(Feeling impatient? Scroll past the text and cat photo to get the learning paths!)
First, may I say a huge thank you to all of you for encouraging me to write? I just noticed that here on Medium, my community of followers is 70% the size of Barack Obama’s. Whoa!
I’m honored and humbled by all the love this amazing community has given me. I don’t know if it’s despite my being a cheerful weirdo or because of it, but thank you! And thank you for being unresponsive to my job title when I turned it off and on at random several times, with no effect on any of the metrics — it means a lot that you’re here for my thoughts and not my labels. Especially since it has been almost 10 years since I’ve had a career change and who knows what kind of madcap adventure I’ll pick when I eventually decide that a change is a good as a holiday.
It was been a great pleasure to share helpful musings with you, but now that I’ve published over 180 blog posts, many of you have told me you’re drowning in all my content and I need to index it better. Turns out it’s very confusing for newcomers to my blog to sort through all the different topics I write about. I hear you! Not everyone is here for all the things. Eventually, I’ll prepare a well-curated site to help you out, but in the meantime, let me take the first step towards a fix by adding standardized supertitles to all my articles. That way you’ll know what category you’re dealing with each time so you can dive right to the ones you care about and skip my musings on random esoterica. In essence, it’ll be as if I have mini-publications for you to chose from.
To take a tiny tangent in defense of the wide range of topics is that in my head, they’re all about the same thing: decision intelligence!* No matter how data-oriented the writing, it’s always founded on the principle of improving your real-world actions. Decision intelligence is about giving yourself the skills and tools to turn information (whether it’s your memories of lunch conversations or it’s your foray through a massive database) into better actions (decisions!) at any scale (from tapas bites to petabytes) and in any setting (from picking a college major to building an AI system). I find it perfectly natural to span this full range of topics — necessary, even, for any serious student of decision-making — though I’ll acknowledge that even with 180+ articles, I’m barely scratching the surface of everything worth knowing.
But if you’re a bit more narrowly focused, hopefully this new index will add some rhyme and reason to your knowledge feast.
This is where you’ll find advice on how to be a better decision-maker, with or without a fancy algorithm. It focuses on the human side of things, like battling your biases, structuring your goals, understanding your irrationality, etc. This is the place for those who seek nuggets of wisdom from disciplines like psychology, economics, neuroscience, managerial science, negotiation, and other classic decision sciences.
Examples:
A category for the data leaders and aspiring leaders among you. This is where I put articles about what’s missing from organizations, what kinds of things you might be doing that cause your data people to quit, whom to hire in what order, how to build a data-driven culture, and so on. I also include data science careers articles from the point of view of the aspiring team member, such as interview questions to ask… which is a handy thing for the manager to read too (it sure helps to know what advice your people are getting about dealing with you).
Examples:
This is where I cover concepts about machine learning and AI in the friendliest way the internet has ever seen, or your (it’s all free!) money back. Some of these articles will be deeper (and snarkier) dives that extend the lessons in my popular Making Friends with Machine Learning (MFML) course on YouTube (the index is here), while others take on the AI zeitgeist or whatever recent misunderstandings I’ve had the pleasure to be subjected to. Immunize yourself here so those same offenses against good sense never cross your own lips.
Examples:
My beloved VC and CEO crowd, run the other way! (Run to any of the categories above, but skip this one.) This one’s for the (eternal) students. Some of you really love it when I pick a random esoteric jargon term and explain the hell out of it for you cheerfully so it feels intuitive. Yes, it’s super nitty-gritty! Yes, most of you don’t care about it! But this stuff is catnip for the, um, perhaps three of you who love to see pompous terminology taken down a notch, shiny new software prodded until it confesses, and formulas explained so a kid (or pointy haired boss) can understand them. So every now and then, I’ll amuse the four of us by showing you how simple we can make complicated things if we understand them deeply. This is also the place where you’ll find out why a topic is where it is in the textbook. Both when it should be where it is and when it most definitely shouldn’t (even if no one told academia yet).
Examples:
I’m a recovering statistician who’s unlikely to ever recover, so there are some many things I have to say about statistics. So many! And I’ve said many of them is a 10.5h secret course all about statistical decision-making which I haven’t put online yet (the first half hour is available in bootleg form, but the bulk of it is waiting for a pro camera crew to capture it — until then, the only way to see it is by inviting me to perform it live). Occasionally, I’ll elaborate on some of the things I say in the course and this category is where you’ll find them.
Examples:
Those who have been following me a while will hopefully recognize these three words… “the discipline of making data useful” is my definition of data science. Welcome to the category that spans general data science plus analytics, minus all the topics that already got sucked into the more specialized categories above. If you’re a practicing data scientist, you’ll want to follow this category plus whichever preceding one most floats your boat.
Examples:
If it’s not any of the categories above, then it’s either a summary of advice I gave someone at a Q&A session (often about careers, courage, self improvement, or juggling life) or it’s some kind of skill/insight that made me a little bit better at growing into the version of me that y’all know and love (or love to hate, it’s the internet after all, hi). Examples include public speaking tips, advice for making new years resolutions, and thoughts on math impostor syndrome.
Examples:
Oh, and many of the links in my articles take you to other articles I wrote related to the highlighted word (and other links take you to easter eggs and humor), so my blog is an elaborate network of Choose Your Adventure. Because upgrading ourselves should be fun and involve a touch of capricious serendipity.
Enjoy!
(And don’t forget to let me know which category you’re most excited about, since that’ll help shape the balance of topics I pick.)
If you had fun here and you’re looking for an applied AI course designed to be fun for beginners and experts alike, here’s one I made for your amusement:
Let’s be friends! You can find me on Twitter, YouTube, Substack, and LinkedIn. Interested in having me speak at your event? Use this form to get in touch.
*Okay, not all of them; I’ll admit that the ones teaching you about public speaking were born out of a capricious impulse.
Making Data Useful
How to muscle up on data-related topics quickly
(Feeling impatient? Scroll past the text and cat photo to get the learning paths!)
First, may I say a huge thank you to all of you for encouraging me to write? I just noticed that here on Medium, my community of followers is 70% the size of Barack Obama’s. Whoa!
I’m honored and humbled by all the love this amazing community has given me. I don’t know if it’s despite my being a cheerful weirdo or because of it, but thank you! And thank you for being unresponsive to my job title when I turned it off and on at random several times, with no effect on any of the metrics — it means a lot that you’re here for my thoughts and not my labels. Especially since it has been almost 10 years since I’ve had a career change and who knows what kind of madcap adventure I’ll pick when I eventually decide that a change is a good as a holiday.
It was been a great pleasure to share helpful musings with you, but now that I’ve published over 180 blog posts, many of you have told me you’re drowning in all my content and I need to index it better. Turns out it’s very confusing for newcomers to my blog to sort through all the different topics I write about. I hear you! Not everyone is here for all the things. Eventually, I’ll prepare a well-curated site to help you out, but in the meantime, let me take the first step towards a fix by adding standardized supertitles to all my articles. That way you’ll know what category you’re dealing with each time so you can dive right to the ones you care about and skip my musings on random esoterica. In essence, it’ll be as if I have mini-publications for you to chose from.
To take a tiny tangent in defense of the wide range of topics is that in my head, they’re all about the same thing: decision intelligence!* No matter how data-oriented the writing, it’s always founded on the principle of improving your real-world actions. Decision intelligence is about giving yourself the skills and tools to turn information (whether it’s your memories of lunch conversations or it’s your foray through a massive database) into better actions (decisions!) at any scale (from tapas bites to petabytes) and in any setting (from picking a college major to building an AI system). I find it perfectly natural to span this full range of topics — necessary, even, for any serious student of decision-making — though I’ll acknowledge that even with 180+ articles, I’m barely scratching the surface of everything worth knowing.
But if you’re a bit more narrowly focused, hopefully this new index will add some rhyme and reason to your knowledge feast.
This is where you’ll find advice on how to be a better decision-maker, with or without a fancy algorithm. It focuses on the human side of things, like battling your biases, structuring your goals, understanding your irrationality, etc. This is the place for those who seek nuggets of wisdom from disciplines like psychology, economics, neuroscience, managerial science, negotiation, and other classic decision sciences.
Examples:
A category for the data leaders and aspiring leaders among you. This is where I put articles about what’s missing from organizations, what kinds of things you might be doing that cause your data people to quit, whom to hire in what order, how to build a data-driven culture, and so on. I also include data science careers articles from the point of view of the aspiring team member, such as interview questions to ask… which is a handy thing for the manager to read too (it sure helps to know what advice your people are getting about dealing with you).
Examples:
This is where I cover concepts about machine learning and AI in the friendliest way the internet has ever seen, or your (it’s all free!) money back. Some of these articles will be deeper (and snarkier) dives that extend the lessons in my popular Making Friends with Machine Learning (MFML) course on YouTube (the index is here), while others take on the AI zeitgeist or whatever recent misunderstandings I’ve had the pleasure to be subjected to. Immunize yourself here so those same offenses against good sense never cross your own lips.
Examples:
My beloved VC and CEO crowd, run the other way! (Run to any of the categories above, but skip this one.) This one’s for the (eternal) students. Some of you really love it when I pick a random esoteric jargon term and explain the hell out of it for you cheerfully so it feels intuitive. Yes, it’s super nitty-gritty! Yes, most of you don’t care about it! But this stuff is catnip for the, um, perhaps three of you who love to see pompous terminology taken down a notch, shiny new software prodded until it confesses, and formulas explained so a kid (or pointy haired boss) can understand them. So every now and then, I’ll amuse the four of us by showing you how simple we can make complicated things if we understand them deeply. This is also the place where you’ll find out why a topic is where it is in the textbook. Both when it should be where it is and when it most definitely shouldn’t (even if no one told academia yet).
Examples:
I’m a recovering statistician who’s unlikely to ever recover, so there are some many things I have to say about statistics. So many! And I’ve said many of them is a 10.5h secret course all about statistical decision-making which I haven’t put online yet (the first half hour is available in bootleg form, but the bulk of it is waiting for a pro camera crew to capture it — until then, the only way to see it is by inviting me to perform it live). Occasionally, I’ll elaborate on some of the things I say in the course and this category is where you’ll find them.
Examples:
Those who have been following me a while will hopefully recognize these three words… “the discipline of making data useful” is my definition of data science. Welcome to the category that spans general data science plus analytics, minus all the topics that already got sucked into the more specialized categories above. If you’re a practicing data scientist, you’ll want to follow this category plus whichever preceding one most floats your boat.
Examples:
If it’s not any of the categories above, then it’s either a summary of advice I gave someone at a Q&A session (often about careers, courage, self improvement, or juggling life) or it’s some kind of skill/insight that made me a little bit better at growing into the version of me that y’all know and love (or love to hate, it’s the internet after all, hi). Examples include public speaking tips, advice for making new years resolutions, and thoughts on math impostor syndrome.
Examples:
Oh, and many of the links in my articles take you to other articles I wrote related to the highlighted word (and other links take you to easter eggs and humor), so my blog is an elaborate network of Choose Your Adventure. Because upgrading ourselves should be fun and involve a touch of capricious serendipity.
Enjoy!
(And don’t forget to let me know which category you’re most excited about, since that’ll help shape the balance of topics I pick.)
If you had fun here and you’re looking for an applied AI course designed to be fun for beginners and experts alike, here’s one I made for your amusement:
Let’s be friends! You can find me on Twitter, YouTube, Substack, and LinkedIn. Interested in having me speak at your event? Use this form to get in touch.
*Okay, not all of them; I’ll admit that the ones teaching you about public speaking were born out of a capricious impulse.
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