I work as a scientific programmer and I used to have that reflex; even deriding others who couldn't or wouldn't automate such things for wasting time. To me, 'data' was just a blob, to be looked at as little as possible.
I've done a 180 on that. The other day I started work on making a plant database, by hand; from designing the schema (columns and sheets in Excel really - blasphemy!) to typing in the values from encyclopedias, wikipedia and books by hand (ok, the latin names I copy/paste). Yes I could just use one of the several large, well-known databases; or one of the hundreds of specific-purpose ones. But making this database has taught me so much already, things I never would have learnt if I'd spend that time on writing import scripts.
Nowadays I let my students/analysts first do extensive eda, which is usually lots of tedious work that seems a waste of time to the programmer instinct. But it's not.
That makes a lot of sense, and is why retyping code snippets from SO or wherever is more helpful than a straight copy-paste. There's something about manually typing information that makes you instantly more familiar with it.
When I was in college I noticed that when professors allowed you to make "cheat sheets" I rarely had to use said cheat sheet - the act of actually writing out the cheat sheet put the material in my head. It ended up being the most effective study method for me.
Data is mostly spatial so sometime with ArcGIS or QGIS, or R depending on what exactly it's about; R for statistical properties of data sets, scatter plots for variable relationships etc. Or Matlab for people with engineering backgrounds. I don't generally care what tools people use and everybody has their own experiences and background. Never need nosql databases (or RDBMS for that matter) for our type of work.
I'm experimenting with physical notebooks myself since a few weeks actually. No electronic note taking has ever really worked well for me, although I've been getting by for 15 years. I'm not sure I'll ever find a system I'll really like - it always feels that as long as I find a way that forces me to get intimately familiar with data sets (to the point where I'm re-doing or at least re-thinking the ways the data was made to begin with), the insights bubble up by themselves. In other words I've come to the (regrettable) conclusion that methods and tools don't matter that much, it's the elbow grease that does. (of course I'm not claiming that I could analyze 15gb of data spread over 50 tables with Notepad...)
The best part about paper notebooks is being able to page through them years later. I experimented briefly with a computational notebook (tiddlywiki) in grad school, since my work was primarily computational, and unfortunately those were lost when my laptop died.
It's entirely intentional. The point isn't just to transpose the numbers from one medium to another; it is to understand what the numbers mean. The best way to do that is the way Martin does it in that video. That's how anyone who does this professionally does it.
Absolutely. But the OPs comment is spot on. Do it manually when you start out and you'll quickly learn both the importance of the numbers you're typing in and common issues you can run into with either manually inputting data or in structuring the model (or that one time you fat fingered a number and blew out everything driven by it)
I assumed it's because he's teaching a lesson. Doing it by hand shows the video viewers how to go find these numbers in the public record, and along the way, what each number means and why it matters.
Just pulling them all in at once from an API would be like a "magic" step to most people who are trying to learn about the basics of analyzing a stock.
