Making data useful
How to quickly dive into data-related topics
(Feeling impatient? Scroll past the text and cat photo for learning paths!)
First of all, can I thank you all for encouraging me to write? I just noticed that here on Medium, my community of followers is 70% the size of Barack Obama’s. Wow!
I am honored and humbled by all the love this amazing community has given me. I don’t know if it’s because I’m a cheerful weirdo or because of it, but thanks! And thanks for not responding to my job title when I’ve randomly turned it off and on multiple times, with no effect on any of the metrics; it means a lot that you are here for my thoughts and not my labels. Especially since I haven’t had a career change in almost 10 years and who knows what kind of crazy adventure I’ll choose when I finally decide a change is as good as a vacation.
It’s been a pleasure sharing helpful thoughts with you, but now that I’ve published over 180 blog posts, many of you have told me that you’re drowning in all my content and that I need to index it better. It 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 things. Eventually, I’ll put together a well-curated site to help you, but in the meantime, let me take the first step toward a solution by adding standardized supertitles to all my articles. That way, you’ll know which category you’re dealing with each time so you can dive right into the ones you care about and skip my musings on random esoterica. In essence, it will be like having mini-posts to choose from.
Taking a small tangent in defense of the wide range of topics is that in my mind, they are pretty much the same thing: decision intelligence!* As data-driven as writing is, it’s always based on the principle of improving your actions in the real world. Decision intelligence is about giving you the skills and tools to turn information (whether it’s your memories of lunch conversations or your foray through a massive database) into better actions (decisions !) at any scale (from pints to petabytes) and in any setting (from choosing a college major to building an AI system). It seems perfectly natural to me to cover this full range of topics, even necessary for any serious student of decision-making, although I recognize that even with over 180 articles, I’m barely scratching the surface. what is worth knowing.
But if you’re a little more focused, hopefully this new index will add some rhyme and reason to your feast of knowledge.
This is where you’ll find tips on how to make better decisions, with or without a fancy algorithm. It focuses on the human side of things, like fighting your biases, structuring your goals, understanding your irrationality, etc. This is the place for those looking for nuggets of wisdom from disciplines like psychology, economics, neuroscience, management science, negotiation, and other classic decision sciences.
Examples:
A category for the data leaders and aspiring leaders among you. This is where I post articles about what organizations are missing, what kinds of things you might be doing that are causing your data people to leave, who to hire in what order, how to create a data-driven culture, and more. I also include articles about data science careers from the aspiring team member’s point of view, such as interview questions to ask… which is also useful for the manager (it sure helps know what advice your people are getting on how to deal). with you).
Examples:
This is where I cover machine learning and artificial intelligence concepts in the friendliest way the internet, or your money, has ever seen (it’s all free!). Some of these articles will be deeper (and more burlesque) dives that expand on lessons from my popular Making Friends with Machine Learning (MFML) course on YouTube (index here), while others address the zeitgeist of the ‘IA or any recent misunderstanding whatsoever. I have had the pleasure of being subdued. Immunize yourself here so that these same offenses against common sense never pass your lips.
Examples:
My dear VC and CEO crowd, run the other way! (Believe in any of the above categories, but skip this one.) This one is for (eternal) students. Some of you really like it when I pick a random esoteric jargon term and gleefully spell it out for you to make it sound intuitive. Yes, it’s super enriching! Yes, most of you don’t care! But this stuff is catnip for the, um, maybe three of you who like to see pompous terminology turned down a notch, shiny new software until it confesses, and formulas explained so that a child (or pointy-haired head ) can understand them. So from time to time, I will 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 will find out Because a topic is where it is in the textbook. Both when it should be where it is and when it definitely shouldn’t be (although no one in academia has said that yet).
Examples:
I’m a recovering statistician who is unlikely to ever recover, so there’s a lot I have to say about statistics. Lots of! And I’ve said that a lot of it is a 10.5 hour secret course on statistical decision making that I haven’t put online yet (the first half hour is available in bootleg form, but most of it is waiting for a professional camera ). the crew to capture it; until then, the only way to see it is by inviting me to perform it live). From time to time I will explain some of the things I say in the course and in this category you will find them.
Examples:
For those who have been following me for a while, I hope you recognize these three words… “the discipline of making data useful” is my definition of data science. Welcome to the category that covers general data science and analytics, minus all the topics already covered in the more specialized categories above. If you’re a practicing data scientist, you’ll want to follow this category in addition to the one above that most floats your boat.
Examples:
If it’s none of the above categories, it’s a summary of advice I gave someone in a Q&A session (often about careers, courage, self-improvement, or juggling life) or it’s some kind of skill/ knowledge that made me a little. a little better to become the version of me you all know and love (or love to hate, it’s the internet after all, hello). Examples include tips for public speaking, tips for making New Year’s resolutions, and thoughts on math imposter syndrome.
Examples:
Oh, and many of the links in my articles take you to other articles I wrote related to the featured word (and other links take you to Easter eggs and humor), so my blog is an elaborate network of Choose Your Adventure . Because updating us should be fun and bring a touch of whimsical serendipity.
Enjoy!
(And don’t forget let me know which category you’re most excited about, as this will help shape the balance of topics you choose.)
If you’ve had fun here and are looking for an Applied AI course designed to be fun for both beginners and experts, here’s one for your enjoyment:
Let’s be friends! You can find me at Twitter, YouTube, Substack and LinkedIn. Interested in having him speak at your event? Use this form to get in touch.
*Okay, no all of them; I admit that those who teach you how to speak in public were born out of a whimsical impulse.
Source link
.]
At Ikaroa, the leading full stack tech company, we believe that the best way to stay ahead in today’s ever-evolving and dynamic field of data science and AI is continuous learning. An important part of this learning process is choosing and taking the best learning paths. This is why we highly recommend and support the path described by Cassie Kozyrkov, Chief Decision Scientist at Google, in her article “The Best Learning Paths For AI and Data Leadership”.
Kozyrkov’s path is quite practical and focused on the reality of being a data scientist or AI leader today – that is, the need to acquire the technical knowledge and skills, but also the “softer” skills like problem solving, communication, and critical thinking. As Kozyrkov explains, this comprehensive approach is the key to success, as “data, after all, is merely a means to an end: understanding trends in order to take action”.
Kozyrkov breaks down her recommended path into four sections: Beginner, Intermediate, Advanced, and Expert. The Beginner section offers the basics, such as data cleaning, data visualization, and basic statistics, and Kozyrkov advises completing online courses to learn each of these topics.
In the Intermediate section, she suggests courses in Python, SQL, machine learning algorithms, and programming. As is the case with the Beginner section, Kozyrkov offers plenty of online and “in-person” course options.
The Advanced section emphasizes the importance of understanding how to communicate effectively and improve user experience. To do this, she recommends focusing on AI as a Service, experimentation design and optimization, enterprise AI, and AI platform engineering.
Finally, the Expert section encourages data scientists and AI leaders to become problem solvers. Kozyrkov suggests taking time to “immerse in real-world datasets and business problems” and exploring advanced technologies such as conversational AI, unsupervised learning, and computer vision.
At Ikaroa, we believe that Kozyrkov has outlined the right path for data science and AI leaders. We highly recommend taking time to reflect upon the recommended path and ensure that you are taking the necessary steps to stay ahead of the curve and stay at the forefront of the industry.
