i ran into this recently. i almost forgot about this since the last time i “discussed” this was in school (either “late” primary or secondary).

it’s a cross between a question mark and an exclamation point. it’s a punctuation mark designed for an “exclamatory rhetorical question” (whatever that actually is).

out of curiosity, has anyone seen it actually used in the wild?

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:

https://github.com/LinsAbadia/Python/blob/master/Machine%20Learning/Learning.ipynb

i was so hung up on words that i “overlooked” visualisations can deceive audiences. i’ve been recently exposed to the works of Edward Tufte and Alberto Cairo on Information Graphics (commonly known by its portmanteau, Infographics). Aside from the important role it can play in emphasising statistics, it also has the power to mislead “consumers” of the information (whether intentional or not). The main point is that they need to be designed carefully and not simply thrown in to break the “monotony” of words or “pretty” things up – they must only be included to serve a particular purpose.

here are a few guidelines to help make the figure you generate “better”:

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833

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:

undefined

i also added it to my GitHub repository:

https://github.com/LinsAbadia/Python/blob/master/Visualisation/VisualizationWheelAlbertoCairo.jpeg

i’ve “shared” something a bit unusual as this Jupyter notebook is comprised of all “Markdown” (and no Python Code) cells as it mainly talks about the initial step required referred to as data “cleaning”. some “transformations” are warranted after importing datasets before working with them or performing Exploratory Data Analyses.

as it is mainly words it may be “ambiguous” ( as everything seems “obvious” to me ) to some. kindly let me know if there are things that aren’t “clear” or can be explained much better so i can post these. or if you know of supplementary (hyper)links or other resources “freely” available online, please let me know so i can make sure to include them.

my updated GitHub repository is at:

https://github.com/LinsAbadia/Python/blob/master/Analyses/Transformation.ipynb

to “complete” “slicing” DataFrames, i discuss loc and iloc. i think this enough to cover the “basics” of Python. as you know, i will start trying to delve into statistics to a.) further my skills, and b.) see if i can be “useful” to my wife.

i was always planning to tackle “advanced” topics -it was just “accelerated” sooner rather later.

here’s something i “shared” so i can “move on” to statistics :

https://github.com/LinsAbadia/Python/blob/master/DataFrames/LocVIloc.ipynb

That said, i can consider revisiting “past” topics based on feedback.

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.