Mapping Census Data with Python
A tutorial on how to make maps by pulling Census data into Python
Think before adding more variables to that analysis
An introduction to thinking about causal models for data analysis. The purpose is to demonstrate that the popular approach of simply gathering as much data as you can and controlling for it via regression or other methods is not a good one, and is actively misleading in many cases. We should instead carefully think about plausible causal models using tools like diagrams (directed acyclic graphs, or DAGs) and then do data analysis in accordance with those models.
How to Use Census Microdata to Analyze High Speed Internet in Kentucky
This post is a start to finish descriptive analysis of high speed internet access in Kentucky, including tables, graphs, and maps. All of the detail of cleaning the data and iterating while exploring the data is included. This makes for a rather lengthy post, but it also makes it relatively unique in including all of those steps. We go through five attempts at making a table of high speed internet before finally getting it right! There’s quite a bit of cleaning work and then also a detour into calculating standard errors via bootstrap so we can correctly display uncertainty in our visuals.
Meritocracy is Unjust
Meritocracy tends to confuse a very practical sense of merit with a more abstract and moral one. An individual may deserve a high-paying job or admission to a selective college because they are productive or qualified. However, in a moral sense, individuals do not merit the skills and abilities they are born with, nor do they merit the environments they were born into that allowed them to develop those skills.
What is Education for?
STEM often (at the undergraduate level) teaches a certain type of thinking, which is a very effective and practical way to solve problems. STEM fields seek answers, while the humanities focus first on training students to ask the correct questions, and to take an extremely broad view of any problem. A lot of damage has been done by narrow, practical solutions. The technology we have is an engineering marvel, and the economic abundance we possess is a tribute to the efficiency of solving practical problems. And yet for all our abundance we still have massive poverty and environmental degradation, as well as a society that is becoming increasingly polarized, distrustful, and distant.
