Today's episode features Dr. Adam Ross Nelson providing his #1 most useful piece of guidance on "How to Become a Data Scientist" from his book of that very name!
This was filmed live at the Open Data Science Conference (ODSC) East in Boston last week — thanks ODSC East for providing valuable conference space for us to shoot podcast episodes.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Today's super-short episode provides a "Continuous Calendar" for 2024. In my view, far superior to the much more common Weekly or Monthly calendar formats, a Continuous Calendar can keep you on top of all your projects and commitments all year 'round.
I know I’m not the only one who Continuous Calendars because my annual blog post providing an updated continuous calendar for the new year is reliably one of my most popular blog posts. The general concept is that Continuous Calendars enable you to:
1. Overview large blocks of time at a glance (I can easily fit six months on a standard piece of paper).
2. Get a more realistic representation of how much time there is between two given dates because the dates don’t get separated by arbitrary 7-day or ~30-day cutoffs.
The way they work so effectively is that continuous calendars are a big matrix where every row corresponds to a week and every column corresponds to a day of the week.
So if you’d like to get started today with your own super-efficient Continuous Calendar in 2024, simply head to jonkrohn.com/cal24.
At that URL, you’ll find a Google Sheet with the full 52 weeks of the year, which will probably suit most people’s needs. If you print it on standard US 8.5” x 11” paper, it should get split exactly so that the first half of the year is on page one and the second half of the year is on page two.
The calendar template is simple: It’s all black except that we’ve marked U.S. Federal Holidays with red dates. If you’re in another region, or you’d like to adapt our continuous calendar for any reason at all, simply make a copy of the sheet or download it, and then customize it to your liking.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Today, my 94-year-old grandmother shares the secrets behind her radiant happiness. Annie talks about the importance of community, relationships and setting daily intentions, blending time-tested wisdom with forward-thinking optimism.
In today’s episode, Annie discusses:
• Her secret to happiness.
• How she maintains flourishing long-term relationships.
• The routines and mindset she has to still be living independently, including driving herself everywhere, at 94 years old.
• The pace of technological progress in her lifetime and how A.I. could enrich her life in the years to come.
This episode is something different from the usual pure tech focus so I encourage you to provide feedback if you had strong feelings on this episode one way or another. As always, your feedback is invaluable for shaping the direction of the show.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Today, razor-sharp investor George Mathew (of Insight Partners, which has a whopping $100-billion AUM 😮) brings us up to speed on the Machine Learning landscape, with a particular focus on Generative A.I. trends.
George:
• Is a Managing Director at Insight Partners, an enormous New York-based venture capital and growth equity firm ($100B in assets under management) that has invested in the likes of Twitter, Shopify, and Monday.com.
• Specializes in investing in A.I., ML and data "scale-ups" such as the enterprise database company Databricks, the fast-growing generative A.I. company Jasper, and the popular MLOps platform Weights & Biases.
• Prior to becoming an investor, was a deep operator at fast-growing companies such as Salesforce, SAP, the analytics automation platform Alteryx (where he was President & COO) and the drone-based aerial intelligence platform Kespry (where he was CEO & Chairman).
Today’s episode will appeal to technical and non-technical listeners alike — anyone who’d like to be brought up to speed on the current state of the data and machine learning landscape by a razor-sharp expert on the topic.
In this episode, George details:
• How sensational generative A.I. models like GPT-4 are bringing about a deluge of opportunity for domain-specific tools and platforms.
• The four layers of the "Generative A.I. Stack" that supports this enormous deluge of new applications.
• How RLHF — reinforcement learning from human feedback — provides an opportunity for you to build your own powerful and defensible models with your proprietary data.
• The new LLMOps field that has emerged to support the suddenly ubiquitous LLMs (Large Language Models), including generative models.
• How investment criteria differ depending on whether the prospective investment is seed stage, venture-capital stage, or growth stage.
• The flywheel that enables the best software companies to scale extremely rapidly.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The St. Gallen Symposium, held annually in Switzerland since student riots in the 1960s, promotes cross-generational dialogue. This year's theme of "A New Generational Contract" set a path for a more resilient, sustainable future. Throughout the week, I reconnected with many inspiring old friends from previous Symposia and met many exceptional new ones, particularly a large number of electrifying social-impact-oriented entrepreneurs and business leaders. A *lot* happened over my three days there; below are the highlights.
Read MoreWell, another year, another continuous calendar from us here at SuperDataScience!
Read MoreWhat really is Burnout? What causes it? And how can you prevent or treat it? Prof. Christina Maslach — world-leading researcher and author on Burnout — joins me for today's episode to unpack these questions.
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
One of the challenges holding machines back from approaching human-level speech recognition like Whisper has has been acquiring sufficiently large amounts of high-quality, labeled training data. “Labeled” in this case means audio of speech that has a corresponding text associated with it. With enough of these labeled data, a machine learning model can learn to take in speech audio as an input and then output the correct corresponding text.
Read MoreYou might think to yourself “I could be spending this time productively!” But pushing past these inner calls for productivity and leaning into the initial discomfort of atelic activities is likely to be rewarding. When you’re consumed by telic activities, by always pursuing outcomes, you’re missing out on being, on appreciating being alive for the fleeting moments that you have.
Read MoreFor 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.
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 MoreTo 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 MoreThis 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.
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.
This article was originally adapted from a podcast, which you can check out here.
At the beginning of the new year, in Episode #538, I introduced the practice of habit tracking and provided you with a template habit-tracking spreadsheet. Then, we had a series of Five-Minute Fridays that revolved around daily habits and we’ve been returning to this daily-habit theme periodically since.
The habits we covered in January and February were related to my morning routine. In the spring, these habit episodes have focused on productivity, and I’ve got another such productivity habit for you today.
To provide some context on the impetus behind this week’s habit, I’ve got a quote for you from the author Robert Greene, specifically from his book, Mastery: "The human that depended on focused attention for its survival now becomes the distracted scanning animal, unable to think in depth, yet unable to depend on instincts."
This suboptimal state of affairs — where our minds are endlessly flitting between stimuli — is exemplified by countless digital distractions we encounter every day, but none is quite as pernicious as the distraction brought to us by social media platforms. When using free social media platforms, you are typically the product — a product being sold to in-platform advertisers. Thus, to maximize ad revenue, these platforms are engineered to keep you seeking cheap, typically unsatisfying dopamine hits within them for as long as they can.
Read MoreFive-Minute Friday this week is a fun one! My top music/audio recommendations for you while you "deep work" 🎶
The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
This article was originally adapted from a podcast, which you can check out here.
At the beginning of the new year, in Episode #538, I introduced the practice of habit tracking and provided you with a template habit-tracking spreadsheet. Then, we had a series of Five-Minute Fridays that revolved around daily habits and we’ve been returning to this daily-habit theme periodically since.
The habits we covered in January and February were related to my morning routine. In March, we began coverage of habits on intellectual stimulation and productivity, such as reading and carrying out a daily math or computer science exercise.
Read MoreThis article was originally adapted from a podcast, which you can check out here.
At the beginning of the new year, in Episode #538, I introduced the practice of habit tracking and provided you with a template habit-tracking spreadsheet. Then, we had a series of Five-Minute Fridays that revolved around daily habits I espouse, and that theme continues today. The habits we covered in January and February were related to my morning routine.
Starting last week, we began coverage of habits on intellectual stimulation and productivity. Specifically, last week’s habit was “reading two pages”. This week, we’re moving onward with doing a daily technical exercise; in my case, this is either a mathematics, computer science, or programming exercise.
The reason why I have this daily-technical-exercise habit is that data science is both a limitlessly broad field as well as an ever-evolving field. If we keep learning on a regular basis, we can expand our capabilities and open doors to new professional opportunities. This is one of the driving ideas behind the #66daysofdata hashtag, which — if you haven’t heard of it before — is detailed in episode #555 with Ken Jee, who originated the now-ubiquitous hashtag.
Read MoreAt the beginning of the new year, in Episode #538, I introduced the practice of habit tracking and provided you with a template habit-tracking spreadsheet. Then, we had a series of Five-Minute Fridays that revolved around daily habits I espouse and that theme continues today. The habits we covered in January and February were my morning habits, specifically:
Starting the day with a glass of water
Making my bed
Carrying out alternate-nostril breathing
Meditating
Writing morning pages
Now, we’ll continue on with habits that extend beyond just my morning with a block of habits on intellectual stimulation and productivity. Specifically, today’s habit is “reading two pages”.
Read MoreThis article was originally adapted from a podcast, which you can check out here.
At the beginning of the new year, in Episode #538, I introduced the practice of habit tracking and provided you with a template habit-tracking spreadsheet. Since then, Five-Minute Fridays have largely revolved around daily habits and that theme continues today. Indeed, having covered most of my morning habits already, namely:
Starting the day with a glass of water
Making my bed
Carrying out alternate-nostril breathing
Meditating
We’ve now reached my final morning habit, which is to compose something called morning pages.
I learned about the concept of morning pages from Julia Cameron’s book The Artist’s Way. It may seem hard to believe now that I’m releasing two podcast episodes and a YouTube tutorial every single week, but five years ago I had staggeringly little creative capacity. I excelled at evaluating other peoples’ ideas and I could execute on ideas very well once they were passed to me, but I self-diagnosed that if I was going to flourish as a data scientist and entrepreneur, I’d need to hone my creativity.
Read More