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Open ended questions are usually difficult to answer, for instance where one can find data sources for economic indicators. Perhaps, the following list may suggest good places to start your quest for data.
Data Sources: A Compendium by the Econbrowser website
Data Sources at the American Economic Association website
Economic Data freely available online by the Economics Network
IMF Data by the International Monetary Fund
Public Use Data Archive by the NBER
Good luck in your data hunting!
A very important question for those concerned with job precarization is whether and how this phenomenon is impacted by globalization and more specifically by international trade. A key hurdle has been the absence of a well organized dataset of the labor provisions included in trade agreements. This would allow not only for a cleaner identification of the effects of trade but also for an estimation of the impact of the labor provisions. Fortunately, the International Labour Organization recently made available a dataset of this type. It covers more than 100 trade agreements and 140 countries! You can find more about the typology used here, about the dataset here, and in the video below.
Learning a new software or even a new programming language is always an interesting journey. Most of the times the tutorials and books we find are never exactly what we need. A useful resource that I found is the Big Book of R. It is one of the most comprehensive repositories of tutorials and general information about R. You can find suggestions according to your needs or even according to your background, for instance for Journalism, Social Sciences, or Life Sciences. It has very good sections on Machine Learning and on R programming.
I came across this very interesting Github repository by Qiuping X., in which she posted the codes she prepared in Python for the book “An Introduction to Statistical Learning with Applications in R” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. This is very useful for those that are learning Python and certainly facilitates the migration from R to Python too.
Some students asked me about a tutorial to learn the basics of Tableau.
Although there are many tutorials available, I found the following videos to be the most straightforward ones.
More specifically, for those wanting to learn how to create a dashboard in Tableau, I recommend the following tutorial: