i’m loathe to admit i only learned the difference a few years ago. both are abbreviations using the first letters of a word (typically the initial letter only).

essentially, acronyms can be read/pronounced as they mostly form a word (or something similarly sounding to one). for initialisms, you say each letter and the “short-cut” need not form a recognised word.

i don’t mean to be pedantic or correct anyone – i just find it nice-to-know.

initially, i just planned on posting a “short” “blurb” on my blog and GitHub Python page as there seemed to me to be a “virtual triangle” among machine learning, statistics, and data visualisation. i’m still likely to make a brief GitHub “file” but upon serious reflection this post may not be a “cursory” post.

it took me awhile to come up with this post because i was partly busy with an online machine learning course, and, frankly, didn’t know what to write – and i’m finding it difficult to figure out how to do it – it didn’t help that there was a “time-consuming” upgrade of the Jupyter notebook environment that i use to store my ipynb files online.

my last experience started me thinking on how i learn- i still need to reflect more on it. i’ve done “ok” academically but i’ve discovered i can understand better if “alternatives” are provided for me to choose from. programming is, essentially, divergent: that is, sometimes there is more than a single way to arrive at an “acceptable solution”. why can’t “formal” education be that way? i know that the human brain can be easily overloaded by many things but perhaps offering a few choices might result in more students understanding. i’m realistic and pragmatic enough to understand that most teachers are overworked (at least those that care about the development of others) and that maybe there needs to be a more “active” open-source community like coding: sharing can make lighter work.

here is my initial attempt at my updated repository:


apparently, i was wrong: both think and thing are acceptable. like so many others, i had been inadvertently “influenced” by the Judas Priest lyrics.

i discovered my mistake when i “played” commonly confused words : https://www.merriam-webster.com/word-games/more-confusing

this had caused me to do a bit of digging and i stumbled upon this article: https://www.theguardian.com/media/mind-your-language/2014/nov/18/mind-your-language-another-think

if mondegreens are often misheard lyrics, what do call “misinterpreted” language from a song?

although we use the metric system in Australia, i still mainly use the English (or Imperial, pick your poison) equivalents for height and weight. maybe IMHO it makes more “sense”, maybe it’s just a force of habit (as we use a “mixed” version in the Philippines. strangely, we’ve not committed to one nor the other), or maybe some kind of combination of these.

apparently, lb is short for the Latin libra which roughly translates to “pound weight” – this etymology is also the root of the currency.

i partly recall the jingle to fully “convert” the population to metric that’s why i remember these ratios: cm = in * 2.54; lbs = kg * 2.2…

i’m not a botanist so this is unfamiliar to me – so naturally i googled it.

“serendipitously”, i ran across this “foreign” term in one of my data science courses in learning about a computer language. they are usually green and are the leaf-like vestiges that “protect” the petals of a flower in bud form and act as supports in the blooming process.

i’m currently taking: Applied Plotting, Charting & Data Representation in Python and have been introduced to a “relevant” model.

as validated by my years of professional experience in ICT, communication is a major part. as technologist, we almost only always focus on the processing and analyses of information. i’m glad that Data Science “explicitly emphasises” the importance of also communication of results. most people just refer to it as IT (but that IMHO is an “antiquated” form of thinking}. not just because it was “recently” rebranded as ICT by some governments and agencies, but because it highlights the other part of the equation and is a much more holistic approach to technology.

for your reference, here’s the Visualization Whee/ by Alberto Cairo:


i also added it to my GitHub repository:


i did a lot of coding in my time and was introduced to neural networks at school so it wasn’i really a stretch learning Python. i only knew aspects of statistics so it became obvious to me that it was something i had to strengthen to upgrade my data science skills because i had a lot of exposure to programming and a little background on artificial intelligence – let me preface it by saying, it’s been awhile since i’ve “actively” done both and technology has advanced, that said, i’ve been developing a GitHub repository because i believe the expression that says you teach best what you need to learn.

to brush on the basics and truly understand Descriptive Statistics i’m perusing version 2 of the ebook Think Stats: Exploratory Data Analysis by Allen B. Downey. it’s supposedly framed for programmers and better suited for them in learning statistics.

aside from personal growth, my wife (although she’s well versed in machine learning and teaching programming) and her work team are looking at doing some research that may require this. so there’s a greater incentive to study this.