Coursera’s Executive Data Science Specialisation: A Review

As my day job has shifted toward a statistics and data science focus, I’ve been reviewing a lot of online materials to get a feel for what is available – both for my learning and to see what might be good training for others.

One course I went through was Coursera’s Executive Data Science Specialisation, created by John Hopkins University. Billed as the qualification to allow you to run a data science team, it is made up of five “one week” courses covering the basics of data science, building data science teams and managing data analysis processes.

There are some goods parts to the courses, but unlike the tagline that you will learn what you need to know “to begin assembling and leading a data science enterprise”, it’s some way short of that benchmark. For managers who have data scientists sitting under them, or who use a data science team in their organisation, it might give them a sense of what is possible and an understanding of how data scientists think. But it is not much more than that.

If I were to recommend any part of the specialisation, it would be the third and fourth courses – Managing Data Analysis and Data Science in Real Life (notes below). They offer a better crash course in data science than the first unit, A Crash Course in Data Science, and might help those unfamiliar with data science processes to understand how to think about statistical problems. That said, someone doing them with zero statistical knowledge will likely find themselves lost.

With Coursera’s subscription option you can subscribe to the specialisation for $50 or so per month, and smash through all five units in a few days (as I did, and you could do it in one day if you had nothing else on). From that perspective, it’s not bad value – although the only material change through paying versus auditing is the ability to submit the multiple choice quizzes. Otherwise, just pick videos that look interesting.

Here’s a few notes on the five courses:

  1. A Crash Course in Data Science: Not bad, but likely too shallow to give someone much feeling about data science. The later units provide a better crash course for managers as they focus on methodology and practice rather than techniques.
  1. Building a Data Science Team: Some interesting thoughts on the skills required in a team, but the material on managing teams and communication was generic.
  1. Managing Data Analysis: A good crash course in data science – better than the course with that title. Walks through the data science process.
  1. Data Science in Real Life: Another good crash course in data science, although you will likely need some statistical background to fully benefit. A reality check on how the data science process is likely to go relative to the perfect scenario.
  1. Executive Data Science Capstone: You appreciate the effort that went into producing an interactive “choose your own adventure”, but the entire effort was around half a dozen decisions in less than an hour.

5 comments

  1. Extremely useful – thank you. I am considering taking this course (as a refresher to a stats background), this helps me skip to the right place.

    Ah the risk of freeloading on your efforts, I’d be very interested in your views/notes on other resource / papers you’ve found useful in this area.

    1. Still early days yet, but some thoughts:
      – I’ve been through the first 6 courses (out of 10) of the Coursera Data Science specialisation – I’d call it more a specialisation in using R for data analysis, and the stats course is simply too much too fast. You can’t learn inference with a couple of hours of videos over four weeks.
      – I haven’t covered many other stats courses, but I’ve heard good things about Duke’s Statistics with R specialisation
      – Andrew Ng’s Machine Learning course is great – good for the intuition, although it’s a big step from that course to actually implementing machine learning models
      – Geoffrey Hinton’s Neural Networks for Machine Learning was great but pretty technical

  2. Thanks for this Jason. I’ve just completed three units of stats at uni culminating in MANOVA and factorial analysis. Mindful that I am a marketing creative with no real-world application of stats experience, would this Coursera material be too advanced for me?

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