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 […]
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 […]
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, […]
This is a short note on what machine learning fitting actually does. We usually teach: A correct statistical or machine learning fitting procedure will, with high probability, correctly identify or infer a system that is close to the one actually producing our training examples. For this to actually happen we […]
One of the great conveniences of performing a data science style analysis using Jupyter is that Jupyter notebooks are literate containers that combine code, text, results, and graphs. This is also one of the pain points in working with Jupyter notebooks with partners or with source control. That is: Jupyter […]
I just got back from a workshop meeting called Digital Transformation of Decision Analysis. This was a workshop organized by Eyas Raddad, David Matheson, and John-Mark Agosta. It was sponsored by The Society of Decision Professionals and Microsoft. Microsoft generously hosted at their new Experience Center at the Microsoft Silicon […]
Taking a break from weekend’s Elden Ring gaming to work out the probability of winning a tournament. The article can be found here: Some Math Inspired by Losing in Elden Ring. It is a variation on a “persuasion by calculation of examples” style I am working on.
Part of the deal of having a package up on CRAN is: at any time one may be sent an automated email like the following. Dear maintainer, Please see the problems shown on URL. Please correct before TODAY+14DAYS to safely retain your package on CRAN. The CRAN Team If this […]
I have a new theoretical finance note up: an appreciation of Cover’s universal portfolio in Python.
Introduction The data algebra is a Python system for designing data transformations that can be used in Pandas or SQL. The new 1.3.0 version introduces a lot of early checking and warnings to make designing data transforms more convenient and safer. An Example I’d like to demonstrate some of these […]