Filtering by Category: Podcast

Daily Habit #2: Start the Day with a Glass of Water

Added on January 17, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

On Five-Minute Friday last week, I introduced the life-transforming concept of daily habit tracking and, if you‘re keen to try it for yourself, I provided an example spreadsheet to get you up and running quickly on your own daily habit tracking journey. If you check out the YouTube version of last week’s episode, you’ll also see that I screenshared the final part of the episode in order to provide a hands-on demonstration of how to configure my habit-tracking template for whatever habits you’d like to track.

Starting with today’s episode, I’ll be recurringly using Five-Minute Fridays to cover the daily habits I track that are most influential in my life. Perhaps they’ll provide food for thought for you or maybe you’ll even try adopting some of them into your own life.

The Confusion Matrix

Added on January 13, 2022 by Jon Krohn.

This video is a quick introduction to the Confusion Matrix, which thankfully really isn’t all that confusing! Understanding what the Confusion Matrix is is key to an Integral Calculus application coming up shortly in this video series.

We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.

More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.

Interpretable Machine Learning — with Serg Masís

Added on January 13, 2022 by Jon Krohn.

This week's guest is Serg Masís, an absolutely brilliant data scientist who's specialized in modeling crop yields and climate change. He's also a world-leading author and expert on Interpretable Machine Learning.

Serg:
• Is a Climate & Agronomic Data Scientist at Syngenta.
• Wrote "Interpretable Machine Learning with Python", an epic hands-on guide to techniques that enable us to interpret, improve, and remove biases from ML models that might otherwise be opaque black boxes.
• Holds a Data Science Masters from the Illinois Institute of Technology.

In this episode, Serg details:
• What Interpretable Machine Learning is.
• Key interpretable ML approaches we have today / when they're useful.
• Social and financial ramifications of getting model interpretation wrong.
• What agronomy is and how it’s increasingly integral to being able to feed the growing population on our warming planet.
• What it’s like to be a Climate & Agronomic Data Scientist day-to-day and why you might want to consider getting involved in this fascinating, high-impact field.
• His productivity tips for excelling when you have as many big commitments as he does.

Today’s episode does get technical in parts but Serg and I made an effort to explain many technical concepts at a high level where we could, so today’s episode should be equally appealing to both practicing data scientists and anyone who’s keen to understand the importance and impact of interpretable ML or agronomic data science. Enjoy!

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Daily Habit #1: Track Your Habits

Added on January 10, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

In September 2016, Konrad Kopczynski — who happened to be the guest on episode #465 of the SuperDataScience podcast — introduced me to the idea of daily habit tracking.

I appreciate that it’s easy to throw around an expression like “life-changing”, but tracking my habits every day really has been a dramatically life-changing experience. When you wake up every morning and report honestly to yourself on whether you did or didn’t perform a particular good or bad habit yesterday, you open up your eyes to who you really are in a way that our minds otherwise trick us into ignoring or exaggerating.

Data Science Trends for 2022

Added on January 10, 2022 by Jon Krohn.

Happy New Year! To kick it off, this week's episode features the marvelous Sadie St. Lawrence predicting the data science trends for 2022. Topics include AutoML, Deep Fakes, model scalability, NFTs, and data literacy.

In a bit more detail, we discuss:
• How the SDS podcast predictions for 2021 panned out (pretty well!)
• The AutoML tools that are automating parts of data scientists’ jobs.
• The social implications of Deep Fakes, which are becoming so lifelike and easy to create.
• Principles for making A.I. models infinitely scalable in production.
• The impact of the remote-working economy on data science employment.
• Practical uses of blockchain and non-fungible token tech in data science.
• Improving the data literacy of the global workforce across all industries.

Sadie:
• Has taught over 300,000 students data science and machine learning.
• Is the Founder and CEO of WomenInData.org, a community of over 20,000 data professionals across 17 countries.
• Is remarkably well-read on the future of technology across industries.

This episode is relatively high-level. It will be of interest to anyone who’d like to understand the trends that will shape the field of data science and the broader world not only in 2022, but also in the years beyond.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

What I Learned in 2021

Added on January 2, 2022 by Jon Krohn.

This article was originally adapted from a podcast, which you can check out here.

For the final episode of 2017, of 2018, of 2019, and of 2020, Kirill Eremenko — the founding host of the SuperDataScience podcast — provided a long guest episode that he called “1-on-1 with Kirill: What I Learned in the Past Year”.

For today’s episode, to cap off 2021, I’m going to do something similar. Instead of a long 1-on-1 guest episode, I’m doing it as a shorter Five-Minute Friday episode because I had too many exciting guest interviews in the recording pipeline that I couldn’t wait to publish and share with you.

Like Kirill in his annual recaps, I’m going to go over a list of specific lessons. At the end of 2021, I’ve got five:

Consistency Leads to Results
Delegation is the Key to Successful Scaling
Remote Working Works
Real-Life Smiles Are Essential
All Work and No Play Makes me a Dull Boy

Consistency Leads to Results

All right, so let’s start with Consistency Leads to Results. This one is the kind of advice that sounds obvious, yet we often struggle to actually do it. If you want great results at some challenging pursuit, it ain’t gonna come easy. However, if you work at it with unwavering consistency, the gains do eventually come surprisingly easily and the gains build on themselves rapidly.

To illustrate this, I’m going to give you two examples with hard data to back me up. The first is on growing an audience and the second is on weightlifting.

How to Found, Grow, and Sell a Data Science Start-up

Added on January 2, 2022 by Jon Krohn.

This week's guest is terrifically witty Austin Ogilvie, a prodigiously successful data science entrepreneur. He was founder and CEO of the iconic start-up Yhat and is now founder/CEO of rapidly-scaling Laika.

Austin:
• Was the Founder and CEO of Yhat, a start-up that built tools for data scientists and had a loyal cult following in the data science community.
• In 2018, Yhat was acquired by Alteryx, an analytics automation company.
• More recently he founded Laika, a “compliance-as-a-service” company that dramatically improves your capacity to sell your products.
• Laika last month closed a $35m Series B funding round, bringing the total raised by the firm over two years to a staggering $48m.

In this episode, Austin describes:
• His journey from an arts degree studying foreign languages to teaching himself programming and machine learning, and then bootstrapping a data science start-up into a respected brand and acquisition target.
• His unique take on what makes a great data scientist.
• The hands-on data science tools he finds great value in coding with day-to-day as the founder and CEO of fast-growing tech start-ups.
• His practical tips for growing into a successful technical founder, whether you have a technical background yourself today or not.

Today’s episode will be of great interest to anyone who’s interested in founding, growing, and/or successfully exiting a tech start-up, particularly if you’re thinking of incorporating data or A.I. elements.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

A Holiday Greeting

Added on December 25, 2021 by Jon Krohn.

Five-Minute Friday this week is a holiday greeting from me. Enjoy :D

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Fusion Energy, Cancer Proteomics, and Massive-Scale Machine Vision — with Dr. Brett Tully

Added on December 21, 2021 by Jon Krohn.

This week's guest is Dr. Brett Tully, who leverages his rich cross-domain experience to detail how data science is applied to the fields of nuclear fusion energy, cancer biology, and massive-scale aerial machine vision.

In today’s episode, Brett details for us:
• What nuclear fusion is, how harnessing fusion power commercial could be a pivotal moment in the history of humankind, and how data simulations may play a critical role in realizing it
• How the study of the healthy proteins versus the proteins present in someone with a particular cancer type is accelerating the availability and impact of personalized cancer treatment
• What it means to be a Director of A.I. Output Systems and how this role fits in with other A.I. activities in an organization, such as model research and development
• His favorite software tools for working with geospatial data
• His tricks for the effective management of a team of ML Engineers
• His take on the big A.I. opportunities of the coming years

Brett:
• Is the Director of A.I. Output Systems at Nearmap, a world-leading aerial imagery company that uses massive-scale machine vision to detect and annotate vast images of urban and rural areas with remarkable detail
• As the Head of Simulation at First Light Fusion, he developed state-of-the-art data simulations that could be a key stepping stone toward enabling commercial nuclear fusion reactors
• As the Group Leader of Software Engineering at a major research hospital, he worked to characterize the differences in the proteome — the complete catalog of proteins in your body — between cancer patients and healthy individuals
• As a PhD student at the University of Oxford, he simulated how the cerebrospinal fluid present in our brains flows in order to better understand neurological abnormalities

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Mutable vs Immutable Conditions

Added on December 19, 2021 by Jon Krohn.

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.

Data Science at the Command Line

Added on December 19, 2021 by Jon Krohn.

This week's guest is Dr. Jeroen Janssens, a global expert and bestselling author on effectively leveraging the Unix command line as a data scientist.

Jeroen:

Wrote the popular book "Data Science at the Command Line", the second edition of which was published by O'Reilly Media in October
Is the Founder Data Science Workshops B.V., which provides hands-on workshops to global orgs such as Amazon and The New York Times
Is Organizer of the Data Science Netherlands Meetup (3000+ members)
Former Assistant Professor at the Jheronimus Academy of Data Science
Has worked as a data scientist for Elsevier, YPlan, and Outbrain
Holds a PhD in A.I. from Tilburg University

In today’s episode, Jeroen details:

Why being able to do data science at the command-line — for example, in a Bash terminal — is an invaluable skill for a data scientist to have
How mastering the command line is the glue that facilitates “polyglot data science”, the ability to seamlessly borrow functions from any programming language in a single workflow
His PhD research on detecting anomalous events in time-series data
Why LaTeX is a great typesetting language to consider using particularly for creating lengthy documents or technical figures that adapt automatically to new data
How his consulting company, Data Science Workshops, grew organically out of his success as an author

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Ten A.I. Thought Leaders to Follow (on Twitter)

Added on December 13, 2021 by Jon Krohn.

This 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…

A.I. Robotics at Home

Added on December 13, 2021 by Jon Krohn.

This week's guest is mad genius Dave Niewinski, who creates A.I.-powered robots for use at home (e.g., cold beer retriever, flame-throwing weed killer) and to teach people A.I. robotics via his popular YouTube channel.

In the episode, Dave covers:
• The specific robotics hardware and open-source software incorporated into his wildest and most well-known robots
• Where machine vision algorithms, particularly deep learning models, are critical for enabling robot functionality
• His tips for folks who’d like to get started in A.I. robotics themselves
• How his interest in robotics led him to founding his Dave's Armoury Ltd. consulting business, which allows him to automate and improve real-world industrial processes with robots
• What excites him most about the societal impact A.I. robotics will have in our lifetimes

Specific robots of Dave's that we detail on the show include ones that:
• Play the sandbag-tossing game "cornhole"
• Defend his machine shop from kids by spraying them with a hose
• Exterminate weeds by throwing flames
• Carve pumpkins for Halloween
• Solve Rubik's cubes
• Use Lego pieces to create 2D artwork
• Race kids at assembling 3D Lego unicorns
• Bring cold beer from the fridge to wherever you are in the house

Thanks to Graham McCormick for introducing me to Dave! I learned so much from him and had such a good time hanging with him "on air".

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com. You can check out the Dave's Armoury YouTube channel here!

The Normal Anxiety of Content Creation

Added on December 6, 2021 by Jon Krohn.

This 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.)

Automating Data Analytics

Added on December 6, 2021 by Jon Krohn.

Meet the brilliant Dr. Peter Bailis. Inspired by research he carried out as a professor at Stanford University, three years ago he founded Sisu to automate enterprise data analytics and the firm has already raised $128m.

In today’s episode, Peter details:
• The revolutionary work being carried out by Sisu: generating automated, actionable reports in minutes that might otherwise take a team of data analysts days
• His guidance for people looking to succeed at growing a tech start-up, particularly if they come from an academic or technical background
• What he looks for in the data scientists and software engineers he hires
• His most important daily tools for developing software productively
• The academic research he carried out at Stanford that’s behind Sisu’s innovative capabilities

Peter:
• Is CEO of Sisu, the firm he founded in San Francisco
• Has raised $128m in venture capital from some of the most prestigious VC firms out there such as Andreessen Horowitz
• Was an assistant professor of computer science at Stanford University, where he’s still an adjunct faculty member today
• Holds several computer science degrees: an undergrad from Harvard University and a PhD from the University of California, Berkeley

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Highest-Paying Data Frameworks

Added on November 27, 2021 by Jon Krohn.

This 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.

Hurdling Over Data Career Obstacles

Added on November 23, 2021 by Jon Krohn.

This week's guest is the sensational Karen Jean-François: mathematician, award-winning data analyst, podcast host, and French national champion in the 400m hurdle. She details how to hurdle over data career obstacles.

More specifically, in today’s episode Karen fills us in on:
• How to overcome Imposter Syndrome in the data science industry
• Why you might want to consider becoming a data science manager versus remaining a more specialized individual contributor
• The data tools that she uses regularly
• The productivity and prioritization techniques that enable her to juggle her day job, her thriving podcast, and her world-class athletic pursuits

Karen:
• Manages banking analytics at publicly-listed Cardlytics
• Is the producer and host of the "Women in Data" podcast
• Was recognized last year as one of the "Twenty in Data and Technology"
• Holds degrees in mathematics and computing from Paris-Sud University (Paris XI)
• Was French national champion in the 400m hurdle and bronze medalist in the 100m hurdle

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Highest-Paying Data Tools

Added on November 22, 2021 by Jon Krohn.

This 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.

Open-Source Analytical Computing (pandas, Apache Arrow)

Added on November 16, 2021 by Jon Krohn.

The legend Wes McKinney is this week's guest! He details the genesis of the ubiquitous pandas library, the forthcoming edition of his bestselling book, and how Apache Arrow brings analytics into the distributed computing era.

Wes:
• Created pandas, the industry-standard Python library for data analytics
• Co-created Apache Arrow, a language-agnostic open-source library for efficient analytics on modern distributed CPUs and GPUs
• Wrote the classic O'Reilly Media desk reference "Python for Data Analysis"
• Has worked as technical expert at prestigious firms like Cloudera, RStudio PBC, Two Sigma, and AQR Capital Management
• Today serves as co-founder and CTO of Voltron Data

In this episode, Wes takes us on a technical deep-drive through:
• The creation story of his now-ubiquitous pandas library
• A sneak peek at the third edition of his international-bestselling book
• What the Apache Arrow project is and why it's poised to revolutionize the data science and software industries
• The software and hardware tools that he uses daily to be such an epically productive software developer and entrepreneur
• Responses to great questions by listeners Daniel, David, Doug, and Brett

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Skyrocket Your Career by Sharing Your Writing

Added on November 9, 2021 by Jon Krohn.

This week's guest is Khuyen Tran, one of the most preeminent voices of data science: Her blog alone receives over 100k views/month. In this episode, she details how by publishing your writing, you too can skyrocket your career.

Khuyen:
• Became an author for the Towards Data Science blog less than two years ago; already her articles garner 100k+ views per month
• Writes practical daily posts featuring Python code right here on LinkedIn, leading to her developing a highly engaged following of over 25k — in just one year!
• Recently became a technical writer for NVIDIA's Developer Blog
• Has landed four data science jobs, including her current role at Ocelot Consulting, in part thanks to her writing
• Has a perfect 4.0 GPA in the computational and applied mathematics undergraduate degree that she's on track to complete next year

In today's episode, Khuyen fills us in on:
• How publishing your writing can skyrocket your technical career
• Her tricks for maximizing engagement with the content you publish
• Her favorite data science tools and approaches
• Her tricks for prioritizing and being as epically productive as she is across her studies, her data science work, and her prodigious technical writing

And thanks to Krzysztof and Nikolay for the outstanding audience questions that Khuyen addressed on-air.

The episode's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.