Making Data Useful
(Feeling impatient? Scroll past the text and cat photo to get the educational paths!)
First, may I say an enormous thanks to all of you for encouraging me to jot down? I just noticed that here on Medium, my community of followers is 70% the dimensions of Barack Obama’s. Whoa!
I’m honored and humbled by all of the love this amazing community has given me. I don’t know if it’s despite my being a cheerful weirdo or due to it, but thanks! And thanks for being unresponsive to my job title once I turned it on and off at random several times, with no effect on any of the metrics — it means quite a bit that you just’re here for my thoughts and never my labels. Especially because it has been almost 10 years since I’ve had a profession change and who knows what type of madcap adventure I’ll pick when I finally resolve that a change is a great as a vacation.
It was been an excellent pleasure to share helpful musings with you, but now that I’ve published over 180 blog posts, a lot of you have got told me you’re drowning in all my content and I want to index it higher. Seems it’s very confusing for newcomers to my blog to sort through all different topics I write about. I hear you! Not everyone seems to be here for all of the things. Eventually, I’ll prepare a well-curated site to aid you out, but within the meantime, let me take step one towards a fix by adding standardized supertitles to all my articles. That way you’ll know what category you’re coping with every time so you possibly can dive right to those you care about and skip my musings on random esoterica. In essence, it’ll be as if I actually have mini-publications so that you can selected from.
To take a tiny tangent in defense of the wide selection of topics is that in my head, they’re all concerning the same thing: decision intelligence!* Irrespective of how data-oriented the writing, it’s all the time founded on the principle of improving your real-world actions. Decision intelligence is about giving yourself the talents and tools to show information (whether it’s your memories of lunch conversations or it’s your foray through an enormous database) into higher actions (decisions!) at any scale (from tapas bites to petabytes) and in any setting (from picking a university major to constructing an AI system). I find it perfectly natural to span this full range of topics — needed, even, for any serious student of decision-making — though I’ll acknowledge that even with 180+ articles, I’m barely scratching the surface of all the pieces price knowing.
But for those who’re a bit more narrowly focused, hopefully this latest index will add some rhyme and reason to your knowledge feast.
That is where you’ll find advice on find out how to be a greater decision-maker, with or and not using a fancy algorithm. It focuses on the human side of things, like battling your biases, structuring your goals, understanding your irrationality, etc. That is the place for many who seek nuggets of wisdom from disciplines like psychology, economics, neuroscience, managerial science, negotiation, and other classic decision sciences.
A category for the information leaders and aspiring leaders amongst you. That is where I put articles about what’s missing from organizations, what sorts of stuff you is likely to be doing that cause your data people to quit, whom to rent in what order, find out how to construct a data-driven culture, and so forth. I also include data science careers articles from the standpoint of the aspiring team member, comparable to interview inquiries to ask… which is a handy thing for the manager to read too (it sure helps to know what advice your individuals are getting about coping with you).
Examples:
That is where I cover concepts about machine learning and AI within the friendliest way the web has ever seen, or your (it’s all free!) a reimbursement. A few of these articles might be deeper (and snarkier) dives that stretch the teachings in my popular Making Friends with Machine Learning (MFML) course on YouTube (the index is here), while others tackle the AI zeitgeist or whatever recent misunderstandings I’ve had the pleasure to be subjected to. Immunize yourself here so those self same offenses against good sense never cross your personal lips.
Examples:
My beloved VC and CEO crowd, run the opposite way! (Run to any of the categories above, but skip this one.) This one’s for the (everlasting) students. A few of you actually adore it once 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 these things is catnip for the, um, perhaps three of you who like to see pompous terminology taken down a notch, shiny latest software prodded until it confesses, and formulas explained so a child (or pointy haired boss) can understand them. So from time to time, I’ll amuse the 4 of us by showing you ways easy we will make complicated things if we understand them deeply. This can be the place where you’ll discover why a subject is where it’s within the textbook. Each when it ought to be where it’s and when it most definitely shouldn’t (even when nobody told academia yet).
Examples:
I’m a recovering statistician who’s unlikely to ever get well, so there are some many things I actually have to say about statistics. So many! And I’ve said a lot of them is a ten.5h secret course all about statistical decision-making which I haven’t put online yet (the first half hour is accessible in bootleg form, but the majority of it’s waiting for a professional camera crew to capture it — until then, the one approach to see it’s by inviting me to perform it live). Occasionally, I’ll elaborate on a few of the things I say within the course and this category is where you’ll find them.
Examples:
Those that have been following me some time will hopefully recognize these three words… “the discipline of constructing data useful” is my definition of knowledge science. Welcome to the category that spans general data science plus analytics, minus all of the topics that already got sucked into the more specialized categories above. When you’re a practicing data scientist, you’ll need 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 recommendation I gave someone at a Q&A session (often about careers, courage, self improvement, or juggling life) or it’s some type of skill/insight that made me somewhat bit higher at growing into the version of me that y’all know and love (or like to hate, it’s the web in any case, hi). Examples include public speaking suggestions, advice for making latest years resolutions, and thoughts on math impostor syndrome.
Examples:
Oh, and most 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 Select Your Adventure. Because upgrading ourselves ought to be fun and involve a touch of capricious serendipity.
Enjoy!
(And don’t forget to let me know which category you’re most enthusiastic about, since that’ll help shape the balance of topics I pick.)
When you had a good time here and also you’re searching for an applied AI course designed to be fun for beginners and experts alike, here’s one I made on your amusement:
Let’s be friends! Yow will discover me on Twitter, YouTube, Substack, and LinkedIn. Serious about having me speak at your event? Use this type to get in contact.
*Okay, not all of them; I’ll admit that those teaching you about public speaking were born out of a capricious impulse.
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