A month with Coursera
Two years ago I took my first Coursera class, Introduction to Data Science. That summer I got hooked. I was only working part time before starting my masters in Journalism at Medill, and needed something to focus on.
Once graduate school started my free time plummeted. I forced myself to take Understanding Media by Understanding Google because it was offered by a Medill professor, then took a hiatus from online learning.
Last week I wrapped up an internship at the Washington Post and decided to return to freelancing. I have some prospective projects in the works, but once again have a lot of free time. There are a lot more courses offered on Coursera now, and I signed up for as many as I thought I could handle.
Then I added a couple more.
I don’t think I can handle this many courses, but they all seem so interesting. I figured by writing about them I would increase the likelihood of finishing them, or at least most of them. I’m going to devote 15 hours a week to classwork, and drop out as needed.
The courses, in order of the likelihood that I will finish:
R Programming: Part two (out of nine) of the Data Science Specialization from Johns Hopkins. Part one is simply setting up Git. I’m cheating with this course. I already took the first half, but didn’t have time to finish. (It’s offered monthly.) The entire course is posted from the beginning, so I’m going to do this one first.
My time estimate: 8 hours total.
Getting and Cleaning Data: Part three of the Data Science Specialization. The R class is a prerequisite, so as soon as I’m done I will check this one out. This course also posts everything upfront. I’m good with data, and similar to the R course I will power through all the material as soon as possible.
My time estimate: 15 hours total.
Algorithmic Thinking (Part 1): This is offered by Rice University. Two years ago I took their Python course as I was learning the language. The instructors were great, but I dropped it once I had some practice with the syntax. This course is also in Python, which is why I picked it over other algorithms courses. I took algorithms as an undergrad, but that was almost 15 years ago now. I’m looking forward to refreshing my knowledge. It is released week-by-week, so that is how I will take it.
My time estimate: 5 hours per week.
Networks Illustrated: Principles without Calculus: I think the course sounds very interesting, but if I loose interest I’ll quickly drop it. It actually runs until July 11th, so I may ignore it at the end of the month to focus on classes that end in June. Then cram, because cramming is the essence of learning. Material released weekly, videos first then homework on Friday.
My time estimate: 5 hours per week.
Introduction to Marketing: Part one of the University of Pennsylvania’s Business Foundations Specialization. I’m definitely lacking in business knowledge. This should help.
My time estimate: 4 hours per week.
Introduction to Finance: I started this course two years ago. Then got behind and dropped it. The instructor was hilarious, and I’m looking forward to taking it again. This class goes until September 19th and material is released on three week cycles. Two weeks of material, with a “free” week for doing the assignments. So, the first set (of 2) assignments aren’t due until June 22nd and the second set will be due roughly when Networks Illustrated wraps up. I remember the first set is really easy. I will power through it once I’m done with Cleaning Data.
My time estimate: 4 hours for the first set, 8 hours for the second set.
Introduction to Statistics for the Social Sciences: This class actually started on April 28th and runs until July 17th. However, I have not started it. I thought about dropping it to focus on everything above, but I do want to refresh my statistical knowledge. I also thought stats was easy as an undergrad, and am hoping it will feel easy again. But since I’m already behind I may drop it.
My time estimate: I don’t have one.
