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.

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.

Resilient Machine Learning

Machine learning is often fragile in production. For today's Five-Minute Friday episode, Dr. Dan Shiebler details how we can make ML more resilient.

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

AutoML: Automated Machine Learning

AutoML with Erin LeDell — it rhymes! In today's episode, H2O.ai's Chief ML Scientist guides us through what Automated Machine Learning is and why it's an advantageous technique for data scientists to adopt.

Dr. LeDell:
• Has been working at H2O.ai — the cloud A.I. firm that has raised over $250m in venture capital and is renowned for its open-source AutoML library — for eight years.
• Founded (WiMLDS) Women in Machine Learning & Data Science (100+ chapters worldwide).
• Co-founded R-Ladies Global, a community for genders currently underrepresented amongst R users.
• Is celebrated for her talks at leading A.I. conferences.
• Previously was Principal Data Scientist at two acquired A.I. startups.
• Holds a Ph.D. from the Berkeley focused on ML and computational stats.

Today’s episode is relatively technical so will primarily appeal to technical listeners, but it would also provide context to anyone who’s interested to understand how key aspects of data science work are becoming increasingly automated.

In this episode, Erin details:
• What AutoML — automated machine learning — is and why it’s an advantageous technique for data scientists to adopt.
• How the open-source H2O AutoML platform works.
• What the “No Free Lunch Theorem” is.
• What Admissible Machine Learning is and how it can reduce the biases present in many data science models.
• The new software tools she’s most excited about.
• How data scientists can prepare for the increasingly automated data science field of the future.

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

Upskilling in Data Science and Machine Learning

This week, iconic Stanford University Deep Learning instructor and entrepreneur Kian Katanforoosh details how ML powers his EdTech platform Workera, enabling you to systematically fill gaps in your data science skills.

Kian:
• Is Co-Founder and CEO of Workera, a Bay Area education technology company that has raised $21m in venture capital to upskill workers, with a particular early focus on upskilling technologists like data scientists, software developers, and machine learning specialists.
• Is a lecturer of computer science at Stanford University (specifically, he teaches the extremely popular CS230 Deep Learning course alongside Prof. Andrew Ng, one of the world’s best-known data scientists).
• Was awarded Stanford’s highest teaching award.
• Is also a founding member of DeepLearning.AI, a platform through which he’s taught over three million students deep learning.
• Holds a Masters in Math and Computer Science from CentraleSupélec.
• Holds a Masters in Management Science and Engineering from Stanford.

By and large, today’s episode will appeal to any listener who’s keen to understand the latest in education technology, but there are parts here and there that will specifically appeal to practicing technologists like data scientists and software developers.

In this episode, Kian details:
• What a skills intelligence platform is.
• Four ways that machine learning drives his skills intelligence platform.
• What frameworks and software languages they selected for building their platform and why.
• What he looks for in the data scientists and software engineers he hires.

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

Learning Deep Learning Together

I'm joined today by Prof. Konrad Körding of the University of Pennsylvania, a world-leading researcher on links between biological neuroscience and A.I. He also leads Neuromatch Academy, a super cool group-based deep learning school.

Neuromatch is an innovative, hands-on program for learning deep learning that matches students with similar interests, languages, and time zones into tight-knit study teams. This matching approach is wildly successful, with 86% of students completing the program, compared to a 10% industry average.

In the first half of the episode, we go over the details of the Neuromatch curriculum, providing you with a survey of all of the state-of-the-art deep learning approaches. The second half is a mind-blowing exploration of the limits of artificial neural networks today and how incorporating more biological neuroscience may enable machines to develop artificial general intelligence (AGI) — i.e., machines that learn as well as humans do.

Listen or watch here.

The History of Data

Last month, I thought I was taking a risk by doing an episode on the History of Algebra, but it was an unusually popular episode! To follow up, today's Five-Minute Friday is on the four-billion-year History of Data — hope you enjoy it 😁

You can watch or listen here.

New Series: Calculus for Machine Learning! Plus a New Video Schedule

With all of the Linear Algebra of my Machine Learning Foundations series already available on YouTube, it's time for a new subject: Calculus! The first Calculus video — an intro to the subject — is live today!

Up until now, I've been releasing my YouTube videos in thematic blocks, however I'm going to try something new now to set clearer delivery-timeline expectations with you. Starting with this video, I'll release a new YouTube video every Monday and every Thursday.

My Calculus for ML YouTube playlist is here. The series is full of hands-on demos in Python (particularly the NumPy, TensorFlow, and PyTorch libraries) and all of the code is available open-source in GitHub.

The first quarter of the series covered Linear Algebra. We're now kicking off the second quarter, on Calculus. The third quarter will be on Probability and Statistics. The final one will be on Computer Science (Data Structures, Algorithms, and Optimization).

Time-Series Analysis

Matt Dancho joins me on this week's SuperDataScience guest episode, which is dedicated to time-series analysis. We cover what it is (modeling financial data and other quantities that vary over time) as well as the state-of-the-art techniques and tools.

Matt is the founder and CEO of Business Science, an educational platform dedicated to commercial applications of data science. He's a heavy contributor to open-source projects, particularly the Modeltime ecosystem of R packages he devised. Modeltime makes working with (and modeling!) time-series data both tidy and easy.

Listen or watch here!

The History of Algebra

Algebra lies at the core of all modern data science approaches. We've been working on it for a while though — it's at least 3900 years old! For Five-Minute Friday today, I cover the Babylonian genesis of the field through to today's ML applications.

Listen or watch here.

Tackling Climate Change with ML

Vince Petaccio II joins me on the SuperDataScience podcast this week to detail how individuals in general — and data scientists in particular — can make a meaningful difference in the fight against climate change.

Particular green machine learning applications we covered include:
• Optimizing energy delivery
• Precision agriculture and vertical farming
• Identification of misinformation
• Climate modeling

Vince is a data scientist at Amazon Web Services (AWS), a sorely-missed former colleague of mine at untapt/GQR, and a brilliantly articulate advocate for climate action through his work as a volunteer lobbyist for the Citizens'​ Climate Lobby.

Listen or watch here.

Legal Tech, Powered by Machine Learning

This week, Horace Wu fills us in on how to bootstrap an ML start-up — without outside capital while still earning an income! We also cover how AI can advance the legal sector and how to pivot your career from services to start-up founder.

Syntheia, the latest company Horace founded, augments human lawyers with the power of the (typically millions) of historical documents at their firm. Using a combination of NLP and machine vision (then surfaced in Microsoft Word using javascript), Syntheia provides lawyers with suggestions on tricky points in contracts and automates the generation of clauses.

Prior to founding Syntheia, Horace was an attorney at top firms in Australia and the US. At Syntheia, he works closely with his team of data scientists and developers to solve complex problems for the legal profession. Especially for a supposedly non-technical founder, he displays a stunning depth of knowledge of NLP models and full-stack model deployments.

You can listen and watch here: superdatascience.com/455

The Staggering Pace of Progress (Part 2)

This episode was adapted from a post that I wrote for the GQR blog.

In today’s Five-Minute Friday episode, I continue last week’s theme. We overview exponentially accelerating technological progress and examine today’s augmentation of human intelligence: the first babysteps of the AI revolution.

In Part 3, coming next month, we'll speculate on how data and automation will dramatically change work and life over the coming decades.

Listen or watch here.

The Staggering Pace of Progress

This podcast episode is based on a blog post I published for GQR. See here.

For millennia, you might not have witnessed a single innovation in your lifetime. In the 20th Century, any job changed dramatically. In the coming decades, automation will dramatically change all aspects of life many times over.

In this week's FiveMinuteFriday, I examine this acceleration — this staggering pace of progress — through the lens of the recruitment industry that is facilitating a lot of the change.

Listening and viewing options here.

Translating PhD Research into ML Applications

Dan Shiebler has a pretty unambitious hobby: a full-time University of Oxford PhD 😂! Dan researches Category Theory, a math branch that categorizes items by their behavior. By day, he's Staff Machine Learning Engineer at Twitter.

In this week's SuperDataScience episode, Dan explains to me how relatively pure mathematical research translates to improving real-world ML applications.

We also discuss:
• How Twitter labels huge datasets
• How Revenue Science can boost ad performance
• What the responsibilities of a staff software engineer at a big tech company are
• What skills are sought after in data science hires at Twitter

Listening and viewing options here.