Leo Carlsson is a currently unemployed but looking for work Political Scientist and Data Scientist in Gothenburg, Sweden. My research interest include corruption, local politics, and party politics, preferably the intersection between them all.
In this blog I will mostly document my journey in improving different types of Data Science skills, as well as demonstrate things I find usefull. If you’re a political scientist, be sure to follow this blog for updates on how to use data science concepts in your own research!
Master in Political Science, 2020
BA in Global Studies, 2017
In this post, we yet again build on the former post, this time to understand parallel processing a bit more. Remember the poll from the last post, where we calculated CI:s from a poll?
In this post, we’ll build on the former post, and ad a bit of iteration programming, understanding how we can reduce code for ease of readability and efficiency. Reducing code also reduces the points where problems can happen, since if it works on one place, it should work on all other places if it’s iterated, but if it’s just copied you have to change it on many places.
Assume we have two candicates, T and D, who are the two candidates running for election in a small village. You poll 1099 inhabitants, and of them 544 say the will vote for D, and 555 say they will vote for T.