Bayesian Methods and Applications, with Alexandre Andorra

Is he a man or a country? Find out in today's episode with Alexandre Andorra — developer of the leading Bayesian library for Python, implementer of commercial Bayesian models and leading Bayesian educator/podcaster!

More on Alex:

• Co-Founder and Principal Data Scientist at PyMC Labs, a firm that develops PyMC (the leading Python library for Bayesian statistics) and consults with their clients to implement profit-increasing Bayesian models.

• Co-Founder and Instructor at an online learning platform called Intuitive Bayes that provides free Bayesian stats education.

• Creator and Host of an excellent podcast called Learning Bayesian Statistics.

Today’s episode will probably appeal most to hands-on practitioners like statisticians, data scientists and machine learning engineers, but the episode also serves as an introduction to Bayesian statistics for anyone who’d like to learn about this important, unique and powerful field.

In today’s episode, Alex details:

• What Bayesian statistics is.

• The situations where Bayesian stats can solve problems that no other approach can.• Resources for learning Bayesian stats.

• The key Python libraries for implementing Bayesian models yourself.

• How Gaussian Processes can be incorporated into a Bayesian framework in order to allow for especially advanced and flexible models.

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

The Tidyverse of Essential R Libraries and their Python Analogues, with Dr. Hadley Wickham

Many-time bestselling author and prolific open-source R developer Hadley Wickham is our guest today. In it, we discuss Posit's rebrand and why the Tidyverse needs to be in every data scientist's toolkit.

More on Hadley:
• Chief Scientist at Posit PBC
• Adjunct Professor of Statistics at Stanford University, Rice University and The University of Auckland.
• Is best-known as the creator of the Tidyverse suite of open-source R libraries for data science, including the essential libraries dplyr and ggplot2.
• Has written seminal books on R programming for O'Reilly, Springer and CRC Press, including the mega-bestselling "R for Data Science".

Today’s episode will primarily be of interest to hands-on practitioners like data scientists and machine learning engineers. In it, Hadley details:
• Why the iconic open-source company RStudio rebranded to Posit.
• The philosophy of the tidyverse, amusing backstories on its most iconic packages and why the tidyverse is invaluable for all data scientists to be familiar with.
• The open-source projects he’s most excited about today.
• How you can easily get involved with career-bolstering open-source projects yourself.

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

PyMC for Bayesian Statistics in Python

Learn how Bayesian Statistics can be more powerful and interpretable than any other data modeling approach from Dr. Thomas Wiecki, a Core Developer of PyMC — the leading Bayesian software library for Python.

Thomas:
• Has been a Core Developer of PyMC for over eight years.
• Is Co-Founder and CEO of PyMC Labs, which solves commercial problems with Bayesian data models.
• Previously, he worked as VP Data Science at Quantopian Inc.
• Holds a PhD in Computational Neuroscience from Brown University.

Today’s episode is more on the technical side so will appeal primarily to practicing data scientists.

In this episode, Thomas details:
• What Bayesian statistics is.
• Why Bayesian statistics can be more powerful and interpretable than any other data modeling approach.
• How PyMC was developed and how it trains models so efficiently.
• Case studies of large-scale Bayesian stats applied commercially.
• The extra flexibility of *hierarchical* Bayesian models.
• His top resources for learning Bayesian stats yourself.
• How to build a successful company culture.

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

Bayesian, Frequentist, and Fiducial Statistics in Data Science

Harvard stats prof Xiao-Li MENG founded the trailblazing Harvard Data Science Review. We cover that and why BFFs (Bayesians, frequentists and fiducial statisticians) should be BFFs (best friends forever).

Xiao-Li:
• Is the Founding Editor-in-Chief of the Harvard Data Science Review, a new publication in the vein of the renowned Harvard Business Review.
• Has been a full professor in Harvard’s Dept of Statistics for 20+ years.
• Chaired the Harvard Stats Dept for 7 years.
• Was Dean of Harvard’s Grad School of Arts and Sciences for 5 years.
• Has published 200+ journal articles on statistics, machine learning, and data science, and been cited over 25,000 times.
• Holds a PhD in Statistics from — yep! — Harvard.

Today’s episode will be of interest to anyone who’s keen to better understand the biggest challenges and most fascinating applications of data science today.

In the episode, Xiao-Li details:
• What the Harvard Data Science Review is, why he founded it, and the most popular topics covered by the Review so far.
• The concept of “data minding”.
• Why there’s no “free lunch” with data — tricky trade-offs abound no matter what.
• The surprising paradoxical downside of having lots of data.
• What the Bayesian, Frequentist, and Fiducial schools of statistics are and when each of them is most useful in data science.

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

Exercises on Event Probabilities

In recent weeks, my YouTube videos have covered Probability concepts like Events, Sample Spaces, and Combinatorics. Today's video features exercises to test and cement your understanding of those concepts.

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

More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.

Combinatorics

Combinatorics is a field of math devoted to counting. In this week's YouTube video, we use examples with real numbers to bring Combinatorics to life and relate it to Probability Theory.

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

More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.

Multiple Independent Observations

In this week's YouTube tutorial, we consider probabilistic events where we have multiple independent observations — such as flipping a coin two or more times instead of just once.

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

More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.

Events and Sample Spaces

In this week's YouTube tutorial, I introduce the most fundamental atoms of probability theory: events and sample spaces. Enjoy 😀

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

More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.

What Probability Theory Is

This week, we start digging into the actual, uh, theory of Probability Theory. I also highlight the field's relevance to Machine Learning and Statistics. Enjoy 😀

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

More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.

Probability & Information Theory — Subject 5 of Machine Learning Foundations

Last Wednesday, we released the final video of my Calculus course, so today we begin my all-new YouTube course on Probability and Information Theory. This first video is an orientation to the course curriculum, enjoy!

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

More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Calculus, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.

The Statistics and Machine Learning Quests of Dr. Josh Starmer

Holy crap, it's here! Joshua Starmer, the creative genius behind the StatQuest YouTube channel (over 675k subscribers!) joins me for an epic episode on stats, ML, and his learning and communication secrets.

Dr. Starmer:
• Provides uniquely clear statistics and ML education via his StatQuest You Tube channel.
• Is Lead A.I. Educator at Grid.ai, a company founded by the creators of PyTorch Lightning that enables you to take an ML model you have on your laptop and train it seamlessly on the cloud.
• Was a researcher at the University of North Carolina at Chapel Hill for 13 years, first as a postdoc and then as an assistant professor, applying statistics to genetic data.
• Holds a PhD in Biomathematics and Computational Biology.
• Holds two bachelor degrees, one in Computer Science and another in Music.

In this episode filled with silliness and laughs from start to finish, Josh fills us in on:
• His learning and communication secrets.
• The single tool he uses to create YouTube videos with over a million views.
• The software languages he uses daily as a data scientist.
• His forthcoming book, "The StatQuest Illustrated Guide to Machine Learning".
• Why he left his academic career.
• A question you might want to ask yourself to check in on whether you’re following the right life path yourself.

Today’s epic episode is largely high level and so will appeal to anyone who likes to giggle while hearing from one of the most intelligent and creative minds in education on data science, machine learning, music, genetics, and the intersection of all of the above.

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

Thanks to Serg, Nikolay, Phil, Jonas, and Suddhasatwa for great audience questions!

O'Reilly + JK October & November ML Foundations LIVE Classes open for registration

We're halfway through my live 14-class "ML Foundations" curriculum, which I'm offering via O'Reilly Media. The first Probability class is on Wednesday with five of the seven remaining classes open for registration now:

• Oct 6 — Intro to Probability
• Oct 13 — Probability II and Information Theory
• Oct 27 — Intro to Statistics
• Nov 3 — Statistics II: Regression and Bayesian
• Nov 17 — Intro to Data Structures and Algorithms

The final two classes will be in December and are on Computer Science topics: Hashing, Trees, Graphs, and Optimization. Registration for them should open soon.

All of the training dates and registration links are provided at jonkrohn.com/talks.

A detailed curriculum and all of the code for my ML Foundations series is available open-source in GitHub here.

Bayesian Statistics

Expert Rob Trangucci joins me this week to provide an introduction to Bayesian Statistics, a uniquely powerful data-modeling approach.

If you haven't heard of Bayesian Stats before, today's episode introduces it from the ground up. It also covers why in many common situations, it can be more effective than other data-modeling approaches like Machine Learning and Frequentist Statistics.

Today's episode is a rich resource on:
• The centuries-old history of Bayesian Stats
• Its particular strengths
• Real-world applications, including to Covid epidemiology (Rob's particular focus at the moment)
• The best software libraries for applying Bayesian Statistics yourself
• Pros and cons of pursuing a PhD in the data science field

Rob is a core developer on the open-source STAN project — a leading Bayesian software library. Having previously worked as a statistician in renowned professor Andrew Gelman's lab at Columbia University in the City of New York, Rob's now pursuing a PhD in statistics at the University of Michigan.

Listen or watch here.

Statistical Programming with Friends with Jared Lander

This week's guest is THE Jared Lander! He fills us in on real-life communities that support learning about — and effectively applying — open-source statistical-programming languages like Python and R.

In addition, Jared:
• Overviews what data-science consulting is like (with fascinating use-cases from industrial metallurgy to "Money Ball"-ing for the Minnesota Vikings)
• Details the hard and soft skills of successful data-science consultants
• Ventures eloquently into the age-old R versus Python debate

Jared leads the New York Open Statistical Programming Meetup, which is the world's largest R meetup — but it also features other open-source programming languages like Python — for talks from global leaders in data science and machine learning. And Jared runs the R Conference, which is approaching its seventh annual iteration next week, Sep 9-10.

Jared also wrote the bestselling book "R for Everyone" and teaches stats at both Columbia University in the City of New York and Princeton University. And none of the massive responsibilities that I've just mentioned are Jared's day job! Nope, for that he's the CEO and Chief Data Scientist of Lander Analytics, a data-science consulting firm.

Watch or listen here.

P.S.: Jared is kindly providing 20% off admission to next week's R Conference off using promo code SDS20. See rstats.nyc for more details, including the first-ever live episode of SuperDataScience (with Drew Conway as guest)!

O'Reilly + JK October ML Foundations LIVE Classes open for registration

The Linear Algebra classes of my "ML Foundations" curriculum, offered via the O'Reilly Media platform, are in the rear-view mirror. Two Calculus classes are coming up soon and the Probability classes just opened for registration:

• Sep 15 — Calculus III: Partial Derivatives
• Sep 22 — Calculus IV: Gradients and Integrals
• Oct 6 — Intro to Probability
• Oct 13 — Probability II and Information Theory
• Oct 27 — Intro to Statistics

Overall, four subject areas are covered:
• Linear Algebra (3/3 classes DONE)
• Calculus (2/4 classes DONE)
• Probability and Statistics (4 classes)
• Computer Science (3 classes)

Hope to see you in class! Sign up opens about two months prior to each class. All of the training dates and registration links are provided at jonkrohn.com/talks

A detailed curriculum and all of the code for my ML Foundations series is available open-source in GitHub here.

Intro to Regression Models – O'Reilly Live Lessons

My new 80-minute intro to Regression Models is up on YouTube! It's packed with hands-on code demos in Python-based Jupyter notebooks to make learning regression intuitive, interactive, and maybe even fun :)

This lesson is an excerpt from my 9-hour "Probability and Statistics for Machine Learning" video tutorial, which is available via O'Reilly here.

All of the code is available open-source via GitHub.