Posts for 2022

During the COVID-19 pandemic that shook the world, people underwent varying degrees of distress and discomfort. Consequently, we all coped in different ways. I, personally, in addition to working from home and enduring lockdowns, was putting up with being separated from Mrs TeochewThunder. I coped by keeping myself really busy.

More specifically, I signed up for yet another Specialist Diploma. This one was in Data Analytics.

No, I'm not some kind of masochist. But as a software developer, it was a professional obligation on my part to take an interest, if not be some kind of Jedi Master, in the various ways new skills could be picked up and applied. Also, I was past 40 at this point and the financial burden of taking up these courses was made negligible by generous subsidies from the Singapore government.

And what better time to do this, than at a time I didn't really have much else going on? What else would I be doing with my time - planning overseas vacations I couldn't go to? It wasn't like FOMO was going to kick in here. Also, as mentioned before in a prior blogpost, I had been exploring Data Visualization and this looked like a good direction to continue.

So yep. I bit the bullet.


After all the messy administration work, the October of 2020 arrived and it was time to start learning shit. Since this was the COVID-19 pandemic, there would be no exams. All lessons would be delivered via video calls and we would be assessed on coursework such as school projects and weekly assignments. Another small mercy, because the biggest pain in the ass I remember from past experience was all the traveling to and from school and office. Now with strict lockdowns imposed due to the pandemic, no more of this crap!

Learning Python

The first term started fairly simple. It involved one of my favorite activities - learning a new programming language. This language was Python, and while I had dabbled with it in the past, now I had actual professional guidance. On my MacBook, I installed Jupyter and that was my IDE.


Of course, we had to deal with the usual basic stuff like operators, logic blocks and iteration. But once we got past that, wow, Python's data structures were really something. I'm talking lists, arrays, dictionaries, series, datasets, the works. And at the end of it, by the time we got to HTML scraping using Python libraries, I was convinced. Python is some seriously good shit.

Not having to hunt for missing semi-colons and do curly brackets, was a nice change. It didn't even bother me too much that Python insists on proper indentation - I do that all the time anyway. Generally, just in terms of syntax alone, Python gave me almost as much pleasure as Ruby.

Learning Data Visualization

As mentioned earlier, Data Visualization was one of the areas I was looking to expand on. My instructors went one better - they delved into storytelling. I had expected maybe a little bit of D3 or some frontend code or other. Instead, I was given Data Visualization software to work with.


This was Tableau Desktop, and it made my work a whole lot easier, especially when you consider that it wasn't just Data Visualization, there was also the storytelling part I had to contend with. I was also shown a whole new world of storytelling with regard to data - how other people did it, and all the fascinating examples of how one could visually represent data to present a narrative.

There was plenty more to the science of storytelling, but all that can be explored at another time.

All in all, the first term went by smoothly. There was a bit of a time crunch when semestral assignments were due and the timing coincided with the festive periods of Christmas and Chinese New Year - moneymakers for my company.


Fortunately, I've always been a proponent of doing my coursework and required reading (usually provided via LinkedIn Learning) on time, sometimes in advance. In fact, tutorial sessions for me weren't for doing the exercises, but for clarifying any doubts I encountered while making the above-mentioned advance preparations for those tutorial sessions.

The second term was a little more alien in terms of familiar territory, but it was entirely within expectations on that score.

Learning Statistics

Now, this subject was both fascinating and intimidating in equal measure. It involved a shit ton of mathematics and formulae. Fortunately, it was not necessary to memorize most of the formulae since the objective was to use Python for the calculations, and Python utilizes built-in libraries for said calculations. So the point here was usually to understand the objective of the calculations, and what numbers to use in order to feed the Python library functions and arrive at the required conclusions.



What we were given were statistical concepts such as Z-score, P-hat and Hypothesis Testing that I understood after some studying, but never quite hammered into my brain. Good thing there wasn't some kind of year-end exam, eh?

Learning Data Wrangling

Data Wrangling was an expansion on all the data cleaning that we did in the previous term while working with Data Visualization. Using Python, we codified and cleaned data to make it more consistent and usable. In fact, we took it a step further and converted all data to make it suitable for Machine Learning. This went a little further down the rabbit hole than I really wanted to go.


Still, there were some nifty tricks I picked up. This was the first time I ever encountered a Box-and-Whiskers chart, or used a Heat Map to find correlation.

All the Python I had learned during the previous term came in useful, because we really used a lot of it for this subject. In fact, the assignment for this subject was really tough. I spent many weekends on this, revising my code and the consequent report, and even then it just felt like I hadn't done enough.

The Educational Conclusion

Last October, I received this lovely letter from Ngee Ann Polytechnic. I was no longer a student. I'd graduated, with what felt like my hundredth Diploma at this point. Technically, only my fourth, but that's three more Diplomas than most people ever manage. My grades ranged from Bs to an A Plus. I didn't get a Distinction, but an A Plus is one step below that. It confirms what I've always known - that while my intelligence is painfully average, my enthusiasm and willingess to grind out results is always going to be the deciding factor.

Self-congratulations aside, this experience really made me feel my age. By the time I was done with my newly-minted Specialist Diploma in Data Analytics, I was a spent force for a while there. The fatigue was unbelievable, and this was actually easier on me than previous experiences! Even with my usual strategy of planning work on time instead of leaving it to the last minute, this was a strain. To be honest, I'm not sure how long I can keep this up.

However, as usual, I'm thankful for everything I've learned and committed to using whatever I can. It's only one more qualification on my ever-expanding list, but the myriad of things I discovered en route to this, is nothing to sniff at.