From OpenMoji — CC Share Alike License https://creativecommons.org/licenses/by-sa/4.0/
From OpenMoji — CC Share Alike License https://creativecommons.org/licenses/by-sa/4.0/

Why another guide? I’ll explain. Keep reading.

I have years of experience teaching undergraduate calculus and linear algebra. I also have years of experience keeping up with machine learning and statistical learning as they grew from their infancy. Taking the academic approach — calculus refresher, probability and statistics, linear algebra — is downright hard.

Based on my teaching experience, the main factor that determines success is what you tell yourself. There is no “math gene”, and learning calculus has nothing to do with anyone but you. …


Properly designing your Dockerfiles and careful creation of layers in Docker images speeds up cleaning data sets and rebuilding your development environment.

Express library build dependencies and data cleaning workflows in Docker files. Copyright David Eppstein Creative Commons CC0 1.0 Universal Public Domain Dedication

The most irritating part of any Data Science project is creating a reproducible data cleaning and engineering pipeline. It’s not productive to spend time cleaning data when it’s already been cleaned. Early steps have to be implemented and tested properly in order to focus on downstream steps. Cleaning the entire data set from start to finish in order to modify or improve a downstream step is not productive.

Setting up your development environment is similar. It’s not productive…


Let’s get back to work.

Photo by PhotoMIX Ltd. from Pexels.

Here are my economic proposals. I do not mean these any more than a basis for discussion. Flexibility is the key here.

Lend or guarantee loans to everyone. That’s right — everyone. Keep reading.

Lending requires a cash outlay, but loan or bond guarantees do not. I’d favor loan guarantees, since they are contingent liabilities for the Treasury. In other words, there is no cash outlay unless the firm defaults. This can be done via the banking system since they already know how to make loans. …


Drawing conclusions from the data and using what you know is the key to Data Science.

Use the BBC’s online tool at https://www.bbc.com/news/election/2019/results to compare the vote results.
Use the BBC’s online tool at https://www.bbc.com/news/election/2019/results to compare the vote results.
Use the BBC’s online tool to compare the vote results.

It’s Town versus Gown | U versus non-U in the U.S. and U.K. elections.

I’m referring here to the similarities between the Gibson’s Bakery versus Oberlin College case and the U.K election. It’s interesting how the U.S. and U.K. culture and elections are mirrors of one another. But on to the U.K.

Looking at votes in England versus Scotland, I have to say that I do think the U.K. will break into two pieces.

England is dominated by Leave, and Scotland by Remain. Labour is no longer a large factor in England outside of a few large cities, particularly London…


Not for wildlife enthusiasts, but data scientists. Copyright Chi King under the Creative Commons Attribution 2 Generic License. No changes were made to this image.

By now you have learned the basics of machine learning and a bit of Python 3 and Pandas. Here are a few next steps, and free resources to get you going. I will keep adding information here as I think of it, or from suggestions in the comments.

At this point, you should not read the documentation as if were a book (although you can do so if this works for you). Browse the documentation top-down to familiarize yourself with the various topics available.

Check the documentation before you look on Stack Overflow, and particularly if…

Dale Smith, Ph.D.

Co-Founder and Chief Research Officer — Vallum Software. My interests are in C/C++, machine learning, Python, Pandas, and Jupyter Notebooks.

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