## Data Science Bite: A/B Testing

I’d like to share my latest “data science bite“: A/B Testing.

I’d like to share my latest “data science bite“: A/B Testing.

Nina Zumel and John Mount will be speaking at the online University of San Francisco Seminar Series in Data Science! How and why to use probability models to outperform decision rules Friday April 30, 2021 12:30pm – 2pm Pacific Time See here for full details and to RSVP In this […]

I am trying a new idea: “data science bites.” Data science bites are small articles and videos explaining only one idea each. This first one explains what supervised machine learning is, without going into the details of how it is realized. (link)

I have up what I think is a really neat tutorial on how to plot multiple curves on a graph in Python, using seaborn and data_algebra. It is great way to show some data shaping theory convenience functions we have developed. Please check it out.

Introduction Teaching basic data science, machine learning, and statistics is great due to the questions. Students ask brilliant questions, as they see what holes are present in your presentation and scaffolding. The students are not yet conditioned to ask only what you feel is easy to answer or present. They […]

I’d like to share a new talk on bilingual data science. It is limited to R and Python, so it is a bit of a “we play all kinds of music, both Country and Western.” It has what I feel is a really neat example how I used Jetbrains Intellij […]

I’ve now shared the code for my “Variable Utility is not Intrinsic” article here: https://github.com/WinVector/Examples/tree/main/Variable_Utility_is_not_Intrinsic. And I have also ported the entire article to Python. It is actually kind of neat to be able to compare the two and see how close doing data science in R and in Python […]

There is much ado about variable selection or variable utility valuation in supervised machine learning. In this note we will try to disarm some possibly common fallacies, and to set reasonable expectations about how variable valuation can work. Introduction In general variable valuation is estimating the utility that a column […]

Introduction Here is a quick data-scientist / data-analyst question: what is the overall trend or shape in the following noisy data? For our specific example: How do we relate value as a noisy function (or relation) of m? This example arose in producing our tutorial “The Nature of Overfitting”. One […]

Introduction I would like to talk about the nature of supervised machine learning and overfitting. One of the cornerstones of our data science intensives is giving the participants the experiences of a data scientist in a safe controlled environment. We hope by working examples they can quickly get to the […]