This article was originally adapted from a podcast, which you can check out here.
Recently, I had dinner with my wonderful friend Jake Zerrer, who’s a Senior Software Engineer at Flexport, a logistics and supply chain start-up based in San Francisco.
Conversation with Jake is never dull, but I particularly enjoyed a part of the conversation where he brought up an idea for framing problems: He described this framework on the basis of mutable versus immutable conditions.
Read MoreThis article was originally adapted from a podcast, which you can check out here.
I was recently asked if I had a list of favorite A.I. thought leaders that I recommended following on Twitter. I didn’t, but that spurred the idea of today’s episode in which I’ll provide you with my ten picks.
My picks aren’t in a particular order overall, but #1 does happen to be my #1 favorite data scientist, and that’s Andrej Karpathy. Andrej is today the Director of A.I. at Tesla, but I’ve been a huge fan since 2016…
Read MoreThis article was originally adapted from a podcast, which you can check out here.
It has been two years since my first book, Deep Learning Illustrated, was published. The years I spent writing the book were filled with persistent darkness and anxiety.
I had a sense of dread that when readers got their hands on the book, they would find catastrophic errors or humiliating gaps in my writing. I feared that some unidentified issue or collection of issues with the book would be so massive that the book would be perceived as a joke or that I would be perceived as a fraud. (I've learned since that such fears are common amongst authors, particularly whilst penning their first book.)
Read MoreThis article was originally adapted from a podcast, which you can check out here.
Three weeks ago for Five-Minute Friday, I covered the highest-paying programming languages for data scientists based on the results of O’Reilly’s 2021 Data/AI Salary Survey. Two weeks ago, we used Five-Minute Friday to get our definitions of data tools and data frameworks straight so that last week we could dig into the highest-paying data tools and now, this week we’ll wrap this series on compensation up by covering the highest-paying data platforms.
Read MoreThis article was originally adapted from a podcast, which you can check out here.
Two weeks ago for Five-Minute Friday, I covered the highest-paying programming languages for data scientists based on the results of O’Reilly’s 2021 Data/AI Salary Survey. Last week we used Five-Minute Friday to get our definitions of data tools and data frameworks straight so that today we could dig into the highest-paying data tools — while next week, in turn, we’ll tackle the highest-paying data platforms. If you get through today’s episode and don’t feel 100% clear about what a data tool is then consider popping back to Episode #522 to clarify.
The most widely-used tool in the survey — used by nearly a third of respondents — was Microsoft’s Excel program for working with data in spreadsheets. Despite its popularity, Excel — along with other click-and-point tools in the survey — was associated with a below-average salary. Specifically, the mean across all respondents was $146k but those who indicated that they used Excel were paid on average $8k/year less at $138k.
Read MoreThis article was originally adapted from a podcast, which you can check out here.
The beloved technical publisher O’Reilly recently released their 2021 Data/AI Salary Survey. It covers responses from 3000 survey respondents in the US combined with another 300 respondents from the UK. All of the respondents are subscribers to O’Reilly’s Data & AI Email Newsletter.
There’s quite a lot of detail in the report on how the salaries of data professionals vary, including by gender, level of education, career stage, industry, and geographic location. Today, I’m focusing on how salaries vary by programming language since this is an attribute that you can easily change about yourself, simply by learning something new.
Read MoreFail more! Failing is very very good. For Five-Minute Friday this week, I elaborate on why.
SuperDataScience episodes are available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
For Five-Minute Friday yesterworkday, I concluded my three-part series on whether caffeine disrupts productivity by broadening from the experiment I ran on myself to the academic literature on the topic.
My Jupyter notebook of data and analysis of the caffeine vs productivity experiment I ran on myself is here.
SuperDataScience episodes are available on all major podcasting platforms and YouTube, and on SuperDataScience.com.
For Five-Minute Friday this week, I lay out my hypothesis that caffeine decreases people's capacity to focus deeply on work. Next week, we'll review the results of the months-long coffee experiment I ran on myself!
(If you can't wait to see the experiment results, you can head to jonkrohn.com/coffee to check them out.)
Listen or watch here.
Five-Minute Friday today is an intro to (deep) reinforcement learning, which has diverse cutting-edge applications: E.g., machines defeating humans at complex strategic games and robotic hands solving Rubik’s cubes.
You can watch or listen here.
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.
Five-Minute Friday this week is a high-level intro to the two largest categories of Machine Learning approaches: Supervised Learning and Unsupervised Learning.
Listen or watch here.
Five-Minute Friday this week is a high-level introduction to Classification and Regression problems — two of the main categories of problems tackled by Machine Learning algorithms.
Listen or watch here.
The Five-Minute Friday episode this week is on Imposter Syndrome, including what it is and how to manage it.
Thanks to Nikolay for the episode idea and Micayla for doing most of my homework for it!
Listen or watch here.
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.
For Five-Minute Friday this week, I tried something different: I wrote a short sci-fi story! Let me know if you liked it or hated it and, based on your feedback, I'll either do more of it or consider never doing it again :)
Watch or listen here.
In today's always-connected world, it's easy to get caught up in thought after thought after thought. For Five-Minute Friday this week, here's a trick to jolt yourself into the present and bask in the simple appreciation of being alive.
Listen or watch here.
Feel like the world is kinda poopy? Well, it is! BUT, covid pandemic not withstanding, it's also WAY better than ever before. I articulate this idea with data and charts for this week's Five-Minute Friday episode.
Thanks to Benjamin Todd for pointing me in the direction of a blog post by Max Roser (founder of Our World in Data) that formed the basis of this podcast episode.
Watch or listen here.
Public Service Announcement for this week's Five-Minute Friday: Don't use pie charts! (Nor, in almost all circumstances, ANY circular chart!)
Listen or watch here.
Time is money. Every second of your life is yours to use and one of the options you have is to generate income. You can do this hourly, or, as a data scientist, invest time in a digitally-sharable product with a huge potential ROI.
Listen or watch here.