Landing Your Data Science Dream Job

This week, the inspiring Harpreet Sahota joins me on the SuperDataScience show to fill you in on how to land your data science dream job — whether it’s a more senior role or your first foray into the field.

Harpreet is an eminent contributor to the data science community. It's unbelievable how much he gives to world each week. He:

• Hosts the The Artists of Data Science podcast

• Is the principal mentor at Data Science Dream Job

• Hosts weekly office hours that anyone can drop in on

• With Kate Strachnyi, founded and hosts the inaugural (and forthcoming) Data Community Content Creator Awards

Despite all of the above, Harpreet's remarkably humble — and capital-S Stoic! Oh, and he has a day job too! He's the lead data scientist at Price Industries, a global industrial leader in HVAC manufacture, and in the episode he details for us data models he's built for them.

You can listen (or watch) here.

The Pomodoro Technique

I’ve experimented with a lot of productivity techniques and quantitatively track their effectiveness every day. One of my long-standing favorites is the Pomodoro Technique to eke creative, highly focused work out of minimal effort 🍅

You can listen to or watch today's Five-Minute-Friday episode on the Pomodoro Technique here.

In addition to Pomodoros, some other techniques I've covered on the podcast are mindfulness (here and here) and a number popularized by James Clear over the years (see 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Syntheia, the latest company Horace founded, augments human lawyers with the power of the (typically millions) of historical documents at their firm.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

Filming "Probability and Stats for ML" LiveLessons

We had a four-day studio session Thursday through Sunday filming what will be ~10 hours of interactive videos on Probability and Statistics for Machine Learning. It was a marathon, but it was also a lot of fun — and I think you'll love the content 😃

On next week's Five-Minute-Friday episode of the SuperDataScience podcast, I'll be providing you with a backstage tour. We shot at Production Central Studios and Stages in Manhattan's Union Square. Thanks as always to Erina Sanders for producing masterfully and Guillaume Rousseau for assisting her impeccably.

All of the Python code is available open-source in GitHub today.

The videos themselves will appear in the O'Reilly learning platform in a couple of months, with thanks to Pearson's Debra Williams for bringing another project concept of mine to life.

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.

Big Global Problems Worth Solving with Machine Learning

In this week's episode, Stephen Welch joined me to discuss 10 big global problems worth trying to solve with data science and machine learning. Perhaps you'll be inspired to tackle one or two yourself!

2020 presented a lot of challenges to a lot of folks around the world. For Stephen, the challenging year was compounded by intense delivery targets for him and his team on a machine vision-enabled system that automatically detects defective industrial products. After delivering this fascinating edge-deployed software into production, Stephen was able to finally come up for air and he reflected on what's most important in one's life — culminating in his list of 10 global problems worth solving.

Stephen is VP of Data Science at Mariner, where he leads a team developing deep learning-based solutions to manufacturing problems. Prior to working with Mariner, Stephen was VP of Machine Learning at Autonomous Fusion, a firm specialized in self-driving cars. He’s also an adjunct professor at University of North Carolina at Charlotte and the creator of the gorgeous math-focused YouTube channel WelchLabs, which has over 200k subscribers and 10M views.

You can listen or watch here.

Six New Videos on Determinants and Eigenvectors

Six new YouTube videos from my Machine Learning Foundations series are live today! These round out a thematic segment of linear algebra videos focused on matrix determinants, eigenvectors, and eigenvalues.

The specific videos are:

1. Matrix Determinants

2. Determinants of Larger Matrices

3. Determinant Exercises

4. Determinants and Eigenvalues

5. Eigendecomposition

6. Eigenvector and Eigenvalue Applications

Up next will be a few videos that focus on real-world, hands-on applications of linear algebra to ML problems, including with singular value decomposition, Moore-Penrose pseudoinversion, and principal component analysis. That will wrap up all of the linear algebra content in the series and then we'll move on to calculus!

The playlist for my entire Machine Learning Foundations series is here.

The series is full of hands-on demos in Python and all of the code is available open-source in GitHub.

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.

Yoga Nidra

This episode features the world-renowned yogi Steve Fazzari. He gives me (and you!) a crash course on Yoga Nidra, a practice of deep relaxation that impacts brain structure, leading to better sleep and ability to respond to stress.

Steve lives up in the mountains of British Columbia as an explorer and instructor of yoga instructors. A ravenous reader and critical thinker, he has a rich understanding of science, the body, and the mind. I learned a ton from Steve during the filming of this episode.

If you like the sound of his explanation (and two-minute demo of) yoga nidra, please let me know because I'd love to bring him back on the SuperDataScience show to lead us through a full-length yoga nidra session.

Listening options here.

Fairness in A.I.

Machines increasingly decide on critical aspects of your life, including medical treatment, mortgages, and job applications. Disturbingly, many such algorithms reinforce historical biases against particular gender and ethnic groups.

This week, Ayodele Odubela joins me on SuperDataScience to discuss the importance of equitability, introspection, and transparency when modeling data (and even in hardware development!), plus a bit about Ayodele’s own personal journey as a data scientist.

Ayodele works at Comet (a machine learning company), is the founder of FullyConnected (a brilliantly-named platform for black and brown data scientists), and the author of Getting Started in Data Science as well as the forthcoming book Uncovering Bias in Machine Learning. I learned a lot from Ayodele; she knows a ton and conveys her knowledge beautifully.

Listening/viewing options, as well as full transcript, available here.

Commercial ML Opportunities Lie Everywhere

In this week’s SuperDataScience guest episode, the staggeringly experienced Dr. Michael Segala fills us in on how commercial opportunities for applying machine learning to real-world problems are everywhere.

Michael is co-founder and CEO of SFL Scientific, a world-leading A.I.-consulting firm that brings state-of-the-art deep-learning possibilities into production across the public and private sectors alike. We survey opportunities for transformative ML applications in the coming decades across a broad range of industries before focusing on the medical industry and governments in particular.

As NVIDIA’s Partner of the Year for AI services, as well as a consultant to firms as diverse as the US Navy, the Smithsonian Institution, and Johnson & Johnson, Dr. Segala may be better-positioned than anyone to be optimistic about the huge positive social impact data and models will have in our lifetimes.

Listening/viewing options, as well as full transcript, available here.

Linear Algebra II: Matrix Operations

This week, I released four new YouTube videos from my Machine Learning Foundations series -- these happen to be the first videos from Subject 2 of the series, which is called Linear Algebra II: Matrix Operations.

The four videos are:

1. Orientation to Subject 2

2. Applying Matrices

3. Affine Transformations

4. Eigenvectors and Eigenvalues
   

Up next -- probably as soon as next week -- I'll be releasing more Linear Algebra videos from Subject 2, covering matrix determinants, eigendecomposition, and the interplay between the two concepts. After that, we'll start releasing videos from Subjects 3 and 4, which are focused on Calculus.

The YouTube playlist for my entire Machine Learning Foundations series 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.

Thanks as always to Sangbin Lee for incredible production. He’s been lately outdoing his already outstanding self.

Bonus "Intro to Linear Algebra" Videos

I recently released three new YouTube videos on linear algebra from my Machine Learning Foundations series. I created these in response to thoughtful questions from students who'd worked through the existing videos in the series -- thank you for the inspiration!

The videos are:

1. Plotting a System of Linear Equations

2. Visualizing Linear Systems

3. Orthogonal Matrix Exercises

The YouTube playlist for the entire ML Foundations series is here.

Conversational A.I. with Sinan Ozdemir

Sinan Ozdemir may be the most articulate explainer of complex concepts I’ve ever met. In the latest SuperDataScience episode, he fills me in on how to design a Conversational A.I. (aka "chatbot") so it's effective for users and businesses alike.

In addition, we discussed AutoML, how a background in pure mathematics is a huge asset in applied data science, and the hard and soft skills you need for a career in natural language processing at a cutting-edge tech company.

Sinan co-founded Kylie.ai, a conversational-AI company, which was acquired by Directly, where Sinan now serves as Director of Data Science. He has a deep well of both technical knowledge and business savvy that I found wildly informative.

Listening/viewing options, as well as full transcript, available here.

The End of Jobs

We’ve all heard the claim: “Robots will take our jobs.” But what actually happens when work is fundamentally changed by technology? Jeff Wald, author of The End of Jobs and co-founder of WorkMarket, joins me on this episode to discuss the future of our 9-to-5’s including how data science, automation, and other macroeconomic factors will reshape work around the globe in the coming decades.

Jeff’s work involves a careful examination of the data around the history of work, and builds on his own experience as co-founder of WorkMarket, a company focused on helping companies manage contractors. We discuss the pandemic, remote work, and the obligations of society towards the people caught up in the churn. 

Listening/viewing options, as well as full transcript, available here.