One the big things I wanted to pick up from the MITx course was how to determine O notation (time complexity). The course did a fairly good job of explaining it and I passed most of the quizzes on it, though my n log n understanding still needs work.
The last weeks of the course covered decision trees. I'm going to agree with commenters that this section feels rushed and was quite confusing for me to understand what it is suppose to do to writing code for it.
I did skip a few things like classes and object oriented programming since I did most of it during the bootcamp I attended.
Overall: This is a solid overview of python and how to develop time efficient algorithms. I think this covers a lot more detail than just the Codeacademy course on Python.
I think going through the bootcamp was a great way to figure out if I wanted to do software development as a career, to a lesser extent I think this course helps with that decision as well. For me, these types of courses helps connect the dots that traditional school wasn't able to do. I learned skills from the courses, then was made to complete assignments that can be applied to real world situations right away.
The next courses I'm looking to take are in Machine Learning and Data Science. Since most investments/decisions are data driven these days, having knowledge of stats is super important. I enjoyed statistics when in school because it seemed so practical, especially in reading studies that we're bombarded with everyday and knowing what the conclusions actually mean and questioning methods that a study uses.
The bright side for me is, a lot of Machine Learning and Data Science in companies require programming skills as well. To develop the tools to analyze information, storing and pulling data from databases, and parsing documents.