How I picked which online Statistics course to study [Personal PhD update #5.2]

The prep week of the first Personal PhD sprint is done. I have chosen my curriculum. I have received few questions on how I pick courses among sea of so many options, so I thought I would share my process in this case.

In general my philosophy is, if there are many different course options, it doesn’t really matter which specific course you pick to go through content wise. From any of them, you will learn more than if you are being paralyzed from too many choices and don’t make a decision at all and don’t study. This is one of the reasons I have an explicit prep time built into my structure so that 1) I give myself time to play around with different options 2) have a deadline by which I have to pick a material. I feel that gives a balance of having somewhat informed decision, but not get bogged down in endless research.

Previously I took Udacity’s Intro to Descriptive Statistics course, now it’s time to learn about Inferential Statistics.

I did some brief Googling on online statistics courses, incorporated coworker recommendations, and looked at next course in sequence for Udacity and picked three options to check out in detail. They were the following:

  1. edX Introduction to Statistics by University of California Berkeley.
  2. Probability and Statistics on Stanford Online platform.
  3. Udacity’s Intro to Inferential Statistics course.

I spent roughly an hour on each and went through the first few lessons.

For me personally, what ended up being the main criteria in this case for picking one course over another was how mobile-friendly it was.

I spend a lot of time with my butt in a seat, be it plane, train or shuttle. I want to use that time productively, and one the ways how I spend it is studying. So bite-sized lessons being available on the phone is important.

If you always study at certain time at a desk with a computer then this totally doesn’t apply to you.

Here are my thoughts on each course.

EdX Berkeley course:

  • Consists mostly of video lessons with the professor speaking quite slowly. On the desktop it is possible to increase the speed of the video, but not on the mobile site.
  • The video screen is small by default on mobile and on each video needs to be expanded bigger; in the expanded mode there is no way to advance to the next video. So unzoom, advance, zoom.. too clumsy for a mobile interface.
  • There is additional reading and exercises on a different webpage that is not really integrated with the course. It is not always clear which exact section need to be read and in which order compared to the videos. Too many micro-decisions cause decision fatigue and therefore there is less willpower left for the actual studying. Also it is very clumsy on a smartphone to jump back and forth between different webpages.

Stanford Online Platform also uses EdX to power its courses, however their Statistics and Probability course is actually very heavy on text materials, not just video so it wasn’t that painful to not be able to speed up the occasional video on the phone. It had an excellent introduction on meta-cognition to be a more effective learner in MOOC setting, which I thought was a very nice touch.

Udacity has a dedicated mobile app which is quite good. It is all video based, but it is so much more than a professor lecturing with slides, that it is so busy with graphs and pointers and stuff happening on the screen with quite a fast speaker, that there is no need to speed up the video.


So for me it really boils down to ease of use on the go on a smartphone.  The EdX Berkeley one is out, but it really is a toss-up between the Stanford and Udacity courses.

In the end I picked the Udacity’s Intro to Inferential Statistics as I had already done its Intro to Descriptive statistics. Perhaps some day as a review and to cement in the basics even more, I will come back to the Stanford’s course.

To round-up theoretical material with some lighter material for this sprint I also plan to read the book The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy by Sharon Bertsch McGrayne and listen to podcast about Machine Learning. The book has been on my list to read from beginning of year so I will start with that first, but I have received some other great recommendations, so keep them coming!

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