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’m not a statistician so kindly bear with my “crudeness”. initially, i just planned on discussing the “3ms”: Mean, Median & Mode. however, aside from these appearing “too short” and after what the describe method returns, it seemed more sensible to cover all the outputs.

as a former educator, i’m open to content being improved : “iteration” is often necessary in endeavouring to present something simpler – so if you have an idea on how to do this “better”, kindly let me know.

here’s the updated GitHub repository:

https://github.com/LinsAbadia/Python/blob/master/Statistics/Descriptive.ipynb

i’m currently taking a visualisation course in Python and it has reminded me of red and green colour blindness: both hues appear similar to them.

while they are still granted driver’s licenses as a “strong” convention for traffic lights exist, the position and not just the colour convey information.

this made me think of truly inclusive designs: where a “best effort” is placed that a design is accessible by default (or a “reasonable” alternative or accomodation is provided). this is “good” to know since coming up with a “universal” design can be “problematic” (as more effort can be required) but in media without guidelines this can invaluable.

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.

it’s complicated

November 3, 2019

i put a draft of QuickSort implemented in Python – admittedly, i’m open to suggestions to further improve it and any other examples that will help understanding. Like my experiences before, it was “difficult” for me to find a “simple” explanation online.  Since some programming languages implemented it as part of a standard library, some ICT professional aren’t familiar with its internal workings and don’t bother to learn it.  i’m all for black boxes and abstraction but when trying to master a language it helps to implement fundamentals – this doesn’t only sharpen one’s thinking ( sort – pun intended – of a form of mental gymnastics) but also to familiarise oneself with the intricacies/quirks of a language.

this absence of “simple” resources seem to be due to a number of things.  my direct experience is that it is sometimes due to the attitude and education/training of technical personnel.  some of them just want to feel superior/smarter than the rest of us – their “hang-ups” from school is evident so that they in turn mistreat others that’s why, IMHO, hazing practices persist.  some act, understandably, as “gate-keepers” to try and make this knowledge exclusive in order to protect their jobs (i.e. economic reasons) or status (i.e. social motivations) or both. and while they most are capable enough to understand, they are not clever enough, equipped to, or motivated to (there’s an obvious misalignment of objectives) make these concepts “easily digestible” for others.  the willingness to help masks their hubris or condescension  – a humble brag of sorts. this fact necessitates me to query my own motivations.

while i don’t recall it being discussed (probably due to my specialisation), it may have been covered in passing by a course in my masters, i could no longer remember how it worked exactly before this endeavour.

the updated GitHub repository can be found at:

https://github.com/LinsAbadia/Python/tree/master/Problems/Algorithms

 

pick your poison

September 15, 2019

i still need to workout guidelines (it’s an evolving thing like much of software development). i need to determine the most appropriate file format (e.g. blog post, Jupyter notebook, PDF, presentation, straight text file etc.) for samples in my Python GitHub repository. That said i should also try to provide alternative representation(s) based on UDL guidelines (http://udlguidelines.cast.org/)

as an educator i’ve always kept that saying in mind.  However, it’s not until i became an online student that i fully appreciated it. Having been a Subject Matter “Expert” (SME) on most of the courses i taught, there was a “barrier” of sorts in trying to design it to maximise student learning.  In learning Python i’ve encountered difficulties despite my experience, academic qualifications, and, most recently, having completed a professional certification. There is still much I have to learn in the field of Data Science.  With this in mind i’ve recently made a new Github Repository with a MIT license that’s publicly accessible (https://github.com/LinsAbadia/Python) that i hope to gradually populate – properly structuring it will involve trial and error.  This is to 1.) Develop my “Code Portfolio” and show i can still be productive in spite of my “disability”, 2.)  Help document and assess my progress, and 3.) Helping me understand concepts fully by seeing if i can explain it “simply” to others (e.g. pointing out “gotchas” in learning and the language).