Thomson’s (Lord Kelvin) First Tide Predicting Machine, 1876
The results were plotted on the paper cylinders, and one literally “turned the crank” to perform the calculations.
The tide calculating machine embodied ideas of Sir Isaac Newton, and Pierre-Simon Laplace (ref), and could predict tide driven water levels by the means of wheels and gears.
The question is: can modern data science tools quickly forecast tides to similar accuracy?
My basic video review of the PyCharm integrated development environment for Python with Anaconda and Jupyter/iPython integration. I like the IDE extensions enough to pay for them early in my evaluation. Highly recommended for data science projects, at least try one of the open-source or the trial versions.
I was recently asked if Win-Vector LLC would move the R wrapr package from a GPL-3 license to an LGPL license. In the end I decided to move wrapr distribution to a “GPL-2 | GPL-3” license. This means the package is now available under both GPL-2 and GPL-3 licensing, allowing the user to pick which of these two licenses they wish to accept the software under. I decided to stick to “GPL-*” style licensing as I endorse the values underlying these licenses, and my (not-legal advice, I am not a lawyer!) opinion this is the licensing pattern closest to the license R itself is distributed under (and hence the closest to the values of the core R community).
Please read on for some background issues I found (not-legal advice, I am not a lawyer!)
A point that differs from our experience struck us in the recent note regarding doing data science in Python:
A development environment [for Python] specifically tailored to the data science sector on the level of RStudio, for example, does not (yet) exist.
Actually, Python has a large number of very capable integrated development environments, some of which are specifically tailored for data science. Please read on for a small list of tools, and my recommendations for a specific data science in Python toolchain.
The following really made my day.
I tell every data scientist I know about vtreat and urge them to read the paper.
Jason, thanks for your support and thank you so much for taking the time to say this (and for your permission to quote you on this).
Chapter of 8 Zumel, Mount, Practical Data Science with R, 2nd Edition, Manning 2019 has a more operational discussion of vtreat (which itself uses concepts developed in chapter 4).
We, the community of Manning R and data science authors, have talked Manning into offering a catalog-wide 40% discount on all books. Please take a look at some great deals on some great technical books here: http://mng.bz/adRj !
Here is simple modeling problem in
We want to fit a linear model where the names of the data columns carrying the outcome to predict (
y), the explanatory variables (
x2), and per-example row weights (
wt) are given to us as string values in variables.