Here is a handy introduction to the basic principles of machine learning: Supervised vs Unsupervised Learning – What’s the Difference?. While reading it I paused to wonder whether there is any way to use machine learning for analyzing causal relationships. Machine learning automates the creation and evaluation of models but it doesn’t identify confounding variables, to run robustness checks, etc. I imagine that the predictions based on ML results tend to be rather conservative since they extrapolate the present to the future, rather than use a series of past events to identify root causes. Please feel free to correct me if there are already ML tools that have causal research functionality!
Tag: quantitative
There are a lot of data literacy and data science courses available online. I am sure you have your own favourites! I was impressed today to learn that the courses on CoginitiveClass.ai are both free and grant users Credly badges on completion. Academic students of data science typically don’t worry about certifying their programming skills. However, hiring managers don’t have the time to read through a paper to find out if the author used sophisticated quantitative skills or not. So get certified! And ask probing questions about applicants’ skills profile (if you are hiring)! It takes a lot of work just to find the appropriate online learning resource. Just because applicants don’t have a sticker on their CV doesn’t mean they haven’t taken training in using a program.
Lucas Jerusalimiec 