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.

Automating Data Analytics

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.

Tutorial on "Big O Notation"

Brand-new, hands-on intro to "Big O Notation" — an essential computer science concept. "Big O" allows us to weigh the compute-time vs memory-usage trade-offs of all algorithms, including machine learning models.

This YouTube video is a 45-minute, standalone excerpt from my six-hour "Data Structures, Algorithms, and ML Optimization" course, which focuses on code demos in Python to make understanding concepts intuitive, fun, and interactive.

If you have an O'Reilly Media subscription, the full course was recently published here.

If you'd like to purchase the course, Pearson is offering it this week (until August 28th) at a 70% discount as their "Video Deal of the Week". The URL for this unusually deep discount is here.

This "DSA and ML Optimization" course is the fourth and final quarter of my broader ML Foundations curriculum. All of the associated code is available open-source via GitHub.

Filming ""Data Structures, Algorithms, and Machine Learning Optimization"" LiveLessons

Silly times on set filming my "Data Structures, Algorithms, and Machine Learning Optimization" videos, which — over 6.5 interactive hours — introduce critical Computer Science concepts for ML and Data Science.

These videos were recently published in the O'Reilly Media platform.

These CS-focused videos are the fourth and final quarter of the subject areas covered in my broader "ML Foundations" curriculum — the first three being Linear Algebra, Calculus, and Probability. All of the code from the curriculum is available open-source in GitHub.

And my "Math for ML" playlist on O'Reilly captures all of the videos in this curriculum in one place.

How to Thrive as an Early-Career Data Scientist

Getting started in data science? Today's episode is for you! Sidney Arcidiacono is absolutely crushing her first year in the field; we discuss the options for getting started in the field and top tips for early-career success.

Trained as a phlebotomist (blood-sample collection), Sidney was inspired by the potential for machine learning to revolutionize healthcare, so she jumped feet first into a full-time computer science degree at Make School, specializing in the data science track. From no familiarity with code or models just a year ago, Sidney's immersion has paid off: She's now fluent in the modern data science software stack and landed a summer data science internship at GreenLight Biosciences, Inc., an RNA-molecule therapeutics firm (like the Pfizer/BioNTech/Moderna vaccines).

Sidney is terrifically sharp and engaging; I think you'll enjoy hearing from her as much as I did during filming.

Watch or listen here.