Johns Hopkins Data Science Specialization Review

It’s been a couple of weeks since Johns Hopkins issued final certificates for their Data Science Specialization on Coursera. I’m glad to say that I am now among the first crop of “alums” of the program. According to the last email we students received from our Johns Hopkins professors, about 2.3 million students have attempted at least one of the courses in the Data Science Specialization. Of those, 68,000 verified certificates were issued for completing a single course. 800 students made it through all 9 courses and enrolled in the capstone, and 500 of us successfully completed the capstone.

The JHU Data Science Specialization has been a very interesting, intellectually stimulating experience. I’d never even taken a single MOOC (Massive Online Open Course) before I enrolled in the first course for the specialization. The courses were a combination of video on demand, computer graded quizzes, and peer graded projects. Given the huge enrollment numbers, there’s obviously a lot of interest in the program, and I continue to get asked questions about it on LinkedIn. I’ve written about my experiences with the specialization before, including a review of every class, but now that I’ve completely finished it and started working in industry, I’d like to give a high level review of the data science specialization.

Strengths of the Data Science Specialization

There’s plenty to talk about here, as the program offers a lot to those that throw themselves into the challenge.

• It’s possible to start with very little data science knowledge. You will be at a noticeable disadvantage if you don’t have at least a beginner’s knowledge of computer programming or statistics/undergraduate mathematics. However, there is no need to know a random forest from a random uniform distribution. The Johns Hopkins professors don’t assume any data science background of any type.
• The Data Science Specialization uses R, a very powerful and popular language for statistics/data science. I don’t see how someone could make it through the JHU program gaining at least an intermediate working understanding of R. R is free and open source, and as a result there is a huge community supporting it with packages and tools. Those tools are emphasized throughout the program, and you’ll have the opportunity to share your work on GitHub.
• A broad variety of data science topics are covered. While the specialization starts slowly with courses like R Programming, Machine Learning doesn’t come until the 8th course. That’s because there are earlier courses emphasize statistical inference, reproducible research, exploratory data analysis, etc. Even within each course, there are a wide variety of topics covered.
• Every course had at least one programming based project. This is where the real substance of learning happens in the Data Science Specialization. I found that there was no way to work through these projects without gaining significant understanding as they were challenging and open ended.
• The program is affordable. The entire specialization can be completed for $500. 2018 update, Coursera’s pricing structure has changed a couple of times and is currently monthly, but this number is still right on par if you take the classes at a rate of 1 per month. A 1 year master’s degree program could cost $50,000 at many universities. It’s hard to claim the the John’s Hopkins specialization has the value of a master’s degree, but I think it has far more than 1% the value of a master’s degree.

Weaknesses of the Data Science Specialization

For everything I did like about the specialization, it is not without its weaknesses.

• Because very little prerequisite data science knowledge is required for the program, the instructors can’t go as deep into content as they could with if they had required students to have a deep knowledge of statistics or computer science up front, like many on campus programs probably do. I don’t think I gained mastery or depth of certain topics that now seem important to me, particularly with topics that occurred later in the sequence like machine learning. That said, I was one of the students who benefited from the slower pace at the beginning of the sequence, when I was struggling to keep up, so it’s hard to complain about this very much.
• Although I really appreciated going into great depth with an industry standard tool like R, I do wish there had been at least minimal exposure to other tools. In my opinion, it does weaken the Data Science Specialization somewhat to offer no exposure at all to Python.  I’ve been left to try to figure out my own introduction to doing data science in Python with Pandas, as it is a valued skill in industry, but with fewer options for online training than R. There was also a no real introduction to “big data” with Hadoop tool sets, or even a discussion of how a Hadoop cluster works. Even a guest lecture on this topic would have been beneficial.
• Finally, the JHU Data Science Specialization has something in common with Coursera and all MOOCs–learning on your own is a challenge. Yes, there are forums where you can discuss problems with other students and community teaching assistants. But while they are of significant valuable in the learning process, course forums are not a full replacement for having an in-person conversation with a classmate, colleague, or professor about a sophisticated topic.

My reviews for the individual courses of the Data Science Specialization follow:

• Course 1: The Data Scientist’s Toolbox
• Course 2: R Programming
• Course 3: Getting and Cleaning Data
• Course 4: Exploratory Data Analysis
• Course 5: Reproducible Research
• Course 6: Statistical Inference
• Course 7: Regression Models
• Course 8: Practical Machine Learning
• Course 9: Developing Data Products
• Thoughts on Completing the 9 Johns Hopkins Data Science Courses

Bottom Line Data Science Specialization Review

The Data Science Specialization from Johns Hopkins is a terrific learning experience if expectations are set properly. If you are coming from a field that is only tangentially related to data science as I was, this program can expose you to exciting new ways of thinking about solving problems with data. I am certain I would not have been able to embark on my new career path so quickly without having gone through this sequence of courses. My Johns Hopkins professors got me up to speed on many data science topics in a very short amount of time, and I’m incredibly grateful. On the other hand, I think there is a ceiling as to how much one can learn with this type of program without practical experience in the field, and the ceiling is lower here than with a full blown degree.

2018 Specialization Review Update and Epilogue

So it’s been nearly three years since I first posted this review. What I didn’t know then was that this the JHU Data Science Specialization would set me on such an incredible career adventure. Shortly after I completed the DS Specialization, I started hearing from recruiters and getting interview opportunities. I landed my first job as a data scientist at a Midwest Fortune 50 insurance company, where I worked for almost 3 years. In mid-2017, I made another career move and went to an east coast company in the Carolinas, where I’m now working for a “tech-first” company with a Silicon Valley style campus.

These changes have had an obvious impact on my confidence, my career trajectory, my income, and the kinds of problems I get to work on every day. The Coursera Data Science Specialization was a first, and not final, step in learning towards this new career. That said, it was such an important first step that I’m honestly not sure I would have made this career change without it.

The other great update has been to see the team at Johns Hopkins innovate the Specialization. Packages, tools, and techniques have changed greatly in the four years that I’ve been a data scientist. I’ve been keeping tabs on the work that the team is doing, and they been continuously iterating the program to integrate tools like the Tidyverse and Plotly, tools that were in their infancy four years ago, but shape the way data scientists work today. Given my experience with Roger Peng, Brian Caffo, and Jeff Leek, I’d expect nothing less than continual iteration and improvement for the students that go through it.

Click here to register for the Johns Hopkins Data Science Specialization on Coursera. (Affiliate link, thanks for your support!)