In Data Science for Non-STEM Majors, Is Learning-by-Watching Live Calculating Possible? Likely? Reasonable to Expect?
We commit as much intellectual malpractice when we let our students graduate from the university these days without command of the basic data science tools as any of the professors of the late...
We commit as much intellectual malpractice when we let our students graduate from the university these days without command of the basic Data Science tools as any of the professors of the late-mediæval university would have committed should they have allowed their students to graduate without command of a fine chancery hand…
However, whenever I have tried to act on this and tried to teach my students the basic data science tools as part of some exploratory data analysis modules of my courses, I have for the most part failed and have quickly retreated back to my standard pedagogy. The only cases in which it has worked have been those when I have been teaching Economics 101B – macroeconomics for stem majors. They know the tools, and so use of them for data analysis, and also for model simulation serves as a reminder of what they learned, a skill-booster for what they ought to know, and an aid to their comprehension of the meat of the class. In my other classes, however, the bimodal distribution of students’ previous experience with data science means that my attempt to demonstrate use of the tools is either horrifyingly opaque and bewildering or too boring in elementary, depending on which of the barbells in the distribution a particular student falls into. So, then, why am I trying this again? Because we need to find a way to do this if we are to do our jobs properly. And because I am a glutton for punishment…
2025-01-28 :: American Economic History :: Very Long Run Growth
J. Bradford DeLong
<https://datahub.berkeley.edu/hub/user-redirect/lab/tree/2025-01-28-econ-113/2025-01-28-econ-113-very-long-run-growth.ipynb>
This is the very first Python Jupyter notebook I am creating for the spring 2025 instantiation of UC Berkeley Econ 113. Its purpose to illustrate how one can use Python Jupyter Notebooks to do calculations and manipulate data, in such a way that afterwards you can figure out what you have done.
The right approach to take to this task is to think always that you are writing for the greatest idiot imaginable—for there is nobody so idiotic as yourself a year from now, trying to figure out why past-you wrote down all of the numbers that you did back then.
Human Population (in millions)
We guess that, for 95% of us alive today, more than 90% of our genes come from about 100 groups of 100 Large East-African Plains Apes wandering around near the Horn of Africa some 75000 years ago. Those 10,000 are our ancestors.
There were, back, then lots of other groups of Large East-African Plains Apes back then—a total worldwide population of perhaps 1 million? And we can see their existence in a (small component of) our genes, as we spread out across the world and "interacted" and then replaced them. But it seems not unreasonable to take those 10,000 as us, for either they were phenomenally lucky or they behaved significantly differently from other Large East-African Plains Apes in the process that made them the overwhelming sources of our genome, and not other groups.
(Parenthetically, that means that we are all very close cousins—less genetic diversity in the entire human race than in your standard baboon troop of 100. The "selfish gene" point of view says that sexually reproducing animals tend to evolve group solidarity: that you ought to be willing, from your genes' point of view in the sense that those are the genes that will tend to spread, to lay down your life so that more than 2 siblings or more than 4 first cousins can live. But our roots in those long-ago 10,000 mean that we are so inbred that, effectively, our genes "want" us to be massively other-regarding and self-sacrificing, and to act on the principle that: "the needs of the many outweigh the needs of the few":
2025-01-28 :: American Economic History :: Very Long Run Growth :: J. Bradford DeLong :: <https://datahub.berkeley.edu/hub/user-redirect/lab/tree/2025-01-28-econ-113/2025-01-28-econ-113-very-long-run-growth.ipynb>
MOAR observations below the fold:
Keep reading with a 7-day free trial
Subscribe to Brad DeLong's Grasping Reality to keep reading this post and get 7 days of free access to the full post archives.