On learning

Sometime in the past this blog was called "Musings from a PhD candidate", despite hardly writing any blog posts that were of the contemplative sort. It later evolved to "Musings from an unlikely candidate" because I had received my PhD, which I thought was quite an unlikely event given my background. However, this carried a somewhat negative connotation as a reader of my blog pointed out to me in an email. Agreeing with him, I changed the name of my blog to Dave Tang's blog, which is boring but the best I could come up with at the time. Since then, I've maintained the same name because I haven't found a need to change it again.

Why the history lesson on the exciting transition of my blog name? Well, I'm going to write a post of the musing type. I'd like to reflect on learning because it's something I have become obsessed with ever since embarking on a PhD. And I think it has to do with what is succinctly articulated in the following quote:

Eigentlich weiß man nur, wenn man wenig weiß, mit dem Wissen wächst der Zweifel.”
--Johann Wolfgang von Goethe (Maximen und Reflexionen)

which translate to:

We know accurately only when we know little; with knowledge, doubt increases.”
--Johann Wolfgang von Goethe (Maxims and Reflections)

I guess it's in line with this more commonly used phrase: "The more you know, the more you realise that you don't know." And since I work in bioinformatics, a multidisciplinary field composed of at least biology, maths, stats, and computer science, the problem is magnified because I realised all the other stuff I didn't (and still don't) know in the other fields! Thus my quixotic quest to know everything, because I have to conquer all my doubts!

Obviously this is extremely unrealistic and unnecessary but yet I would still love to, at the very least, "know enough to be dangerous" in all the fields that bioinformatics intertwines with. In the past, I made many futile attempts because I tried to learn advanced topics in the respective fields without first understanding the fundamentals. This wasted a lot of time and was extremely frustrating. Then I tried to learn from the very basics first but the topics were far removed from what I really wanted to learn. And because I was trying to juggle between learning so many different topics and work, my learning did not progress as much as I had wanted.

Philipp Bayer's Twitter bio used to say something along the lines of "trying to learn too many things for my own good" and that really resonated with me, so much so that I have included it in my blog's About page. Since his bio no longer says that, I guess he has realised the folly in such an endeavour. I too shall become a bit more strategic in my quest to know it all. It began by asking myself what's the thing that I want to learn the most AND is the most relevant to my work. It was statistics.

So for now, I'm going to forget about learning the plethora of computer languages (C, Scala, Python, Rust), linear algebra, data structures and algorithms, etc. and focus on statistical learning/modelling. I have tried to read The Elements of Statistical Learning (ESL) at different points of my life (and I recently bought a hard copy too) but sadly, I still have a lot of knowledge gaps. By chance, I found An Introduction to Statistical Learning (ISLR) while scrolling on Amazon and it is supposed to be easier to digest than ESL since it's written for people who want to learn statistical learning but come from a different academic background (like myself). You can actually download ISLR from their website if you are interested and I did read a little bit; from what I've read so far, it seems like the book that I have wanted to read. I'm patiently waiting for my hard copy to arrive later this month so I can really dig into it. (I learned that there is currently a global paper shortage [most likely due to COVID-19], which is the book has been delayed.)

Finally, the last part of making learning work is to stick to it. There's a quote I like that goes: "Have enough courage to start and enough heart to finish." No more trying to learn too many things. No more half read books. Time to focus and finish.

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