A Year of Learning Data Analytics (Part 2/2)

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.

Preparation, preparation,
preparation.

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.

Statistics in action.

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.

Box-and-Whiskers.

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.

Keep learning!
T___T