I am sharing a new short data science video: Parameterized Juypter Notebooks. It is an example from the wvpy package showing how to programmatically re-run the same notebook with many different inputs. If you are doing data science in Python, this may help you with your projects. link

Estimated reading time: 24 seconds

I am sharing yet another data transform tutorial here! It is about coordinatized data, the larger theory encompassing pivot and un-pivot. The example is in Python, but we also supply a similar package for R users.

Estimated reading time: 18 seconds

The data algebra is a system for composing data manipulation tasks in Python. In the data algebra, operator pipelines (or even directed acyclic graphs) are the primary objects. Applying operations composes small data pipelines into larger ones. This allows the fluid specification, inspection, and sharing of data processing and data […]

Estimated reading time: 1 minute

I’ve been seeing a lot of hot takes on if one should do data science in R or in Python. I’ll comment generally on the topic, and then add my own myopic gear-head micro benchmark. I’ll jump in: If learning the language is the big step: then you are a […]

Estimated reading time: 5 minutes

I’ve just started experimenting with the Polars data frame library in Python. I really like the programmable API it exposes. In fact I am starting an experimental adapter from the data algebra to Polars. When this is complete one can use the data algebra to run the same data transform […]

Estimated reading time: 46 seconds

I am excited to share my guest lecture for Department of Statistics at the University of Illinois STAT 447: Data Science Programming Methods. And thank you to Dirk Eddelbuettel for inviting me! The talk was titled “Data Science: Street Fighting Statistics” and demonstrates two simple supervised modeling tasks in R. […]

Estimated reading time: 35 seconds

We have had some trouble with some articles being damaged or hard to access in the Win Vector blog. I (John Mount) do want to apologize for that. In particular the graphs are missing for Dr. Nina Zumel’s wonderful y-aware Pricipal Components regression series. The complete R .md and .Rmd […]

Estimated reading time: 2 minutes

I would like to share what I have found to be a very effective personal Jupyter workflow for data science development. DALL-E “An Effective Personal Jupyter Data Science Workflow” Jupyter (nee IPython) workbooks are JSON documents that allow a data scientist to mix: code, markdown, results, images, and graphs. They […]

Estimated reading time: 10 minutes

I say: if you are a data scientist or working on an analytics project, worry over columns not rows. In analytics “rows” are instances, and “columns” are possible measurements. For example: each click on a website might generate a row recording the visit, and this row would be populated with […]

Estimated reading time: 2 minutes

I (John Mount) am recommending a book that I just started reading. The publisher Manning recently reached out to me and asked if I would accept a free copy of Effective Data Science Infrastructure by Ville Tuulos in exchange for considering helping to promote it. No obligation to promote it, […]

Estimated reading time: 2 minutes