AI ROI: How to get a profitable return on an AI-project investment

Relative to other kinds of software R&D, A.I. projects are typically expensive. Today's sage guest, Keith McCormick, details approaches for ensuring that A.I. projects not only are transparent, but that they are profitable too.

Keith:
• Is Executive Data Scientist in Residence at Pandata LLC, a consulting firm focused on transparent, human-centered A.I.
• Is predictive analytics instructor at UC Irvine.
• Has created 20 LinkedIn Learning courses on machine learning and A.I. with, in aggregate, hundreds of thousands of students.
• Authored four books, recurringly focused on statistics with SPSS Modeler.

Today’s episode should appeal to anyone who’s eager to get a return on an investment in an A.I. project, no matter whether you have technical or non-technical background.

In today’s episode, Keith details:
• His straightforward approach to ensuring that A.I. projects are successful.
• How A.I. projects need to be set up and managed in order to get a profitable return on the project.
• The corporate roles that need to be in place in order for a data science team to complete projects that drive value.
• What A.I. transparency is and how it relates to the field of Explainable A.I.
• How data scientists who have advanced software-writing skills could benefit from the use of low-code/no-code tools.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Mike Wimmer: The 14-Year-Old A.I. Entrepreneur

His CS degree (with a perfect GPA!) completed, 14-year-old sensation Mike Wimmer is now turning his attention to socially-impactful A.I. projects (such as conserving the world's coral reefs) and his tech startups.

Meet the (very funny!) teenager in today's episode and hear about:
• How he got started in A.I. at such a young age.
• How he's using A.I. to detect invasive species with remote-operated vehicles.
• His preferred software stack for building machine learning applications.
• His vision for an automated future with tons of people inspired to create socially-impactful solutions.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Efficiently Glean-ing Insights from Vast Data Warehouses

In today's episode, the super-sharp and super-technical founder Carlos Aguilar details how he's built a platform to democratize data analytics and efficiently Glean insights from across vast data warehouses 📊

Carlos:
• Is Founder and CEO of Glean, a New York-based start-up that has raised $7m in venture capital to democratize data analytics and data insights.
• Spent six years as the VP of Data Insights Engineering at Flatiron Health, a cancer data platform that was acquired by Swiss pharma giant Roche.
• Spent five years engineering robots at Amazon.
• Holds Master’s in mechanical and aerospace engineering from Cornell University.

Today’s episode includes some technical content near the beginning that will appeal most to data science and software engineering practitioners, but we do break down the main takeaways from those technical discussions so that any interested listener can partake. The remainder of the episode will appeal to anyone who’s keen to hear from an extremely intelligent technical founder on how to successfully launch a data-centric start-up.

In this episode, Carlos details:
• The software stack Glean built their platform with to enable their customers to quickly obtain slick data visualizations and insights from their vast data warehouses.
• How to grow an entrepreneurial idea into an investable company.
• The essential characteristics to look for in your founding team.
• How to ensure your early clients are continuously delighted by your evolving software platform.
• How he used genetic algorithms to enable a robotic arm to paint beautiful, creative art onto real-world canvases 🎨

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Intentional Use of Color in Data Communication

In today's episode, Kate Strachnyi— author of the new book ColorWise — opened my eyes to the vastly underutilized power of the intentional use of color in data visualizations. Now you can harness its power too!

Kate:
• Is a multi-time data science book author.
• Her latest book, ColorWise, was published by O'Reilly Media: It’s a beautiful, comprehensive guide to the effective use of color when communicating data visually.
• Founded the DATAcated Circle, a community of data professionals committed to engaging and learning together.
• Is a megastar on LinkedIn where she has over 170k followers and was twice recognized as a LinkedIn Top Voice for Data Science & Analytics.
• Is big into long-distance running; her longest to date was a 50-mile (!!!) ultramarathon in New York’s Central Park.

Today’s episode should appeal to technical and non-technical folks alike because I suspect that pretty well any listener of this show presents data and could benefit from learning how to do so more effectively with the intentional use of color.

In today’s episode, Kate details:
• Why the intentional use of color matters.
• What thought process you should follow to select a color scheme for a visualization.
• Special considerations for color choice, such as accessibility, cultural understanding, and due to human psychology.
• How to effectively use multiple visualizations together in a document, presentation or dashboard.
• Her favorite data viz tools.

Want a free digital copy of ColorWise? The first five people to comment on this post that they want one, get one! Thanks to O'Reilly's Suzanne Huston for offering this to our listeners

If you miss out on one of the five copies, you can use my special code "SDSPOD23" to get a free 30-day trial of the O'Reilly platform and read the book there.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Introduction to Machine Learning

After a multi-year hiatus, Hadelin and Kirill — the most popular data science instructors on Udemy, with 2+ million students — have released a new ML course. In this episode, they introduce what ML is from scratch.

Kirill Eremenko:
• Is Founder and CEO of SuperDataScience, an e-learning platform.
• Founded the SuperDataScience Podcast in 2016 and hosted the show until he passed me the reins two years ago.

Hadelin de Ponteves:
• Was a data engineer at Google before becoming a content creator.
• In 2020, took a break from Data Science content to produce and star in a Bollywood film featuring "Miss Universe" Harnaaz Sandhu.

Together, Kirill and Hadelin:
• Have created dozens of data science courses.
• Are the most popular data science instructors on the Udemy platform, with over two million students.
• After a multi-year hiatus from creating courses, they recently published a new course called “Machine Learning in Python: Level 1".

This episode serves as an introduction to machine learning so will primarily appeal to folks who aren’t already expert at ML — that said, I’ve been doing ML for over 15 years and still learned a few critical new pieces of information during filming so this episode could serve as a fun, light-hearted refresher for experts.

In this episode, Kirill and Hadelin introduce ML concepts such as:
• Supervised vs unsupervised learning
• Classification errors
• Logistic regression
• Feature scaling
• The Adjusted R-Squared metric
• The assumptions of linear regression
• The Elbow Method

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Is Data Science Still Sexy?

Had far too much fun filming today's episode with Prof. Tom Davenport, many-time author of bestselling books on analytics and coiner of data science as "sexiest job of the century". A decade on, does he still think so?

Tom:
• Has published over 20 books, such as the bestselling "Competing on Analytics", "The A.I. Advantage", and "Analytics at Work".
• Has penned 300+ articles in publications like the Harvard Business Review and writes regular columns for Forbes and The Wall Street Journal.
• Is President's Distinguished Professor of IT and Management at Babson College.
• Is Visiting Professor at the Saïd Business School, University of Oxford.
• Is Senior Advisor to the A.I. practice for the global professional services giant Deloitte.
• With nearly 300k followers, he’s recognized as a LinkedIn Top Voice.

Today’s episode is equally well-suited to technical and non-technical listeners alike. Every part of it should be appealing to anyone who’s keen to hear about the leading edge of commercial applications of A.I.

In this episode, Prof. Davenport details:
• The discrete A.I. maturity levels of organizations.
• How organizations become A.I. fueled.
• Which jobs are susceptible to replacement by A.I.
• Which jobs are ripe for augmenting with A.I.
• What roles other than data scientist are required to deploy effective machine learning models.
• What the future of data science will look like and, having coined data science as “the sexiest job of the 21st century” a decade ago, whether he still thinks it is today.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

ChatGPT: How to Extract Commercial Value Today

Brewer and e-commerce entrepreneur Zack Weinberg joins me in today's episode to detail countless examples of how anyone can extract commercial value from ChatGPT in minutes, right now.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Machine Learning for Video Games

Carly Taylor — Lead ML Engineer for the "Call of Duty" franchise — joined me for today's fun, super informative episode on low-latency software engineering, real-time ML, and the future of gaming.

Carly:
• Grew rapidly from a Sr Data Scientist role to simultaneously holding "Expert ML Engineer" and "Sr Mgr — Security Strategy" titles since joining Activision two years ago.
• At Activision, specifically works on Call of Duty, one of the top-grossing video game franchises of all time, with over $30 billion in sales and 250m global users annually.
• Prior to Activision, rapidly grew from Analyst to Data Scientist roles.
• Has amassed a LinkedIn following of 75k+ by regularly posting fruitful tips on breaking into a data science career and progressing within it.
• Advocates for women in STEM, tech, and gaming careers.
• Offers 1:1 career consulting to anyone who desires it.
• Holds a Masters in Computational Chemistry from the University of Colorado and completed the Galvanize Data Science Immersive program.

Today’s episode certainly has technical tidbits throughout that will be useful to hands-on practitioner but much of the wide-ranging conversation will be fascinating to any listener, particularly if you have an interest in video games, the so-called metaverse, or real-time machine learning.

In this episode, Carly details:
• What the future of gaming holds.
• Why low-latency is critical for an optimal gaming experience and the tools that online engineers use to make it happen.
• Her favorite operating systems, software packages, and keyboards.
• How to transition effectively from a quantitative academic background into data science.
• How to file a patent.
• Why she’s called the “Rebel Data Scientist”.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

A Framework for Big Life Decisions

The biggest decisions we make involve trade-offs between professional opportunity (money) and our personal life (love). Today, Stanford labor economist Prof. Myra Strober provides a framework for making a big choice.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

A.I. for Medicine

Machine learning is ushering in a new era of medicine, e.g., by predicting the shape of therapeutic drugs and assisting in their design. Witty Prof. Charlotte Deane of the University of Oxford and Exscientia explains how.

Charlotte:
• Is a global-leading expert on using ML for designing therapeutic drugs.
• Has been faculty at the University of Oxford for over 20 years, where serves as Professor of Structural Bioinformatics and heads the 25-person Protein Informatics Lab.
• Is Chief Scientist Biologics A.I. at Exscientia, a NASDAQ-listed pharmatech company that uses computational approaches to drive drug development in a fraction of the time of traditional drug companies.
• Was COVID-Response Director for UK Research and Innovation, resulting in Queen Elizabeth II honoring her as a Member of the Most Excellent Order of the British Empire.

Today’s episode should appeal to technical and non-technical folks alike as it features an absolutely brilliant scientist and communicator describing how we can use A.I. to speed the discovery of new molecules that help our body fight off ailments as diverse as viruses and cancer.

In this episode, Prof. Deane details:
• How your immune system works.
• What biologics are and why they’re such an important class of drugs.
• What’s holding back the widespread use of precision medicines that are pinpoint-customized to a specific tumor in a specific person.
• What the celebrated AlphaFold algorithm does exquisitely and where it (and all other computational models of protein folding) still need to improve.
• How she used data to marshall the UK’s scientific response to Covid.
• How data and machine learning will transform drug development over the coming years.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Data Science Trends for 2023

Happy New Year! To kick it off, the entrepreneur, futurist, and mega-popular Machine Learning instructor Sadie St. Lawrence joins me to predict the biggest data science trends of 2023 🍾

We start the episode off by looking back at how our predictions for 2022 panned out from a year ago and then we dive into our predictions for the year ahead. Specific trends we discuss include:
• Data as a product
• Multimodal models
• Decentralization of enterprise data
• A.I. policy
• Environmental sustainability

This episode will appeal to technical and non-technical folks alike — anyone who’d like to understand the trends that will shape the field of data science and the broader world not only in 2023 but also in the years beyond.

Sadie:
• Has created data science and ML courses enjoyed by 350k+ students.
• Is Founder and CEO of Women In Data, a community of over 20k women across 17 countries.
• Serves on multiple start-up boards.
• Hosts the Data Bytes podcast.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Simplifying Machine Learning

Today, Mariya Sha — host of the wildly popular "Python Simplified" YouTube channel (140k subscribers!) — taps her breadth of A.I. expertise to provide a fun and fascinating finale to SuperDataScience guest episodes for 2022.

Mariya:
• Is the mind behind the "Python Simplified" YouTube channel that makes advanced concepts (e.g., ML, neural nets) simple to understand.
• Her videos cover Python-related topics as diverse as data science, web scraping, automation, deep learning, GUI development, and OOP.
• Is renowned for taking complex concepts such as gradient descent or unsupervised learning and explaining them in a straightforward manner that leverages hands-on, real-life examples.
• Is pursuing a bachelor's in Computer Science (with a specialization in A.I. and Machine Learning) from the University of London.

Today’s episode should appeal to anyone who’s interested in or involved with data science, machine learning, or A.I.

In this episode, Mariya details:
• How the incredible potential of ML in our lifetimes inspired her to shift her focus from web-development languages like JavaScript to Python.
• Why automation and web scraping are critical skills for data scientists.
• How to make learning any apparently complex data science concept straightforward to comprehend.
• Her favorite Python libraries and software tools.
• One rarely-mentioned topic that every data scientist would benefit from.
• The pros and cons of pursuing a 100% remote degree in computer science.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

How to Influence Others with Your Data

If you ever use data to make decisions or to persuade those around you to make data-driven decisions, today’s episode is jam-packed with relevant, practical tips from data presentation guru Ann K. Emery.

Ann:
• Is an internationally-acclaimed speaker who delivers 100+ keynotes, workshops, and webinars each year to enable people to share data-driven insights more effectively.
• She has consulted on data visualization, data reporting, and data presentation with over 200 organizations — the likes of the United Nations, the US Centers for Disease Control, and Harvard University.
• She holds a BA in Psychology and Spanish from the University of Virginia and a Masters in Educational Psychology Evaluation, Assessment, and Testing from George Mason University.

I rarely say that everyone should listen to an episode, but this is one of those rare cases.

In this episode, Ann details:
• What data storytelling is.
• Best practices for data visualization.
• Surprising tricks you can pull off with spreadsheet software.
• How to report on data effectively.
• Her top tips for presenting data in a slideshow.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Equality Machine

Many recent books and articles spread fear about data collection and A.I. Today's guest, Prof. Orly Lobel, offers the antidote with her book "The Equality Machine" — an optimistic take on the future of data science.