After an extremely long wait, today was the day that the fifth course in Coursera’s Machine Learning Specialization was set to begin. I’ve been with this specialization since it launched in the fall of 2015. Students were initially promised an ambitious slate of six courses, including a capstone that would wrap up by early summer of 2016. With noted husband and wife couple Carlos Guestrin and Emily Fox, previously of Carnegie Mellon and now of the University of Washington, this sounded like a great option.
I’ve spent the last couple of months working through course three in the University of Washington’s Machine Learning Specialization on Coursera. Course two was regression (review); the topic of the third course is classification. As has been the case with previous courses, this specialization continues to be taught by Carlos Guestrin and Emily Fox. For the classification course, Dr. Guestrin took the lead. The time requirements did increase a bit with this third course, not excessively, but it felt like I was working an extra hour or so a week on it.
I’ve recently completed the second course in the University of Washington Machine Learning Specialization on Coursera, “Machine Learning: Regression.” This comes on the heels of completing course 1, Machine Learning Foundations: A Case Study Approach. This course debuted right at the end of November and wrapped up 6 weeks later (my impression is that these courses are slipping a bit behind the timeline that was originally announced). I’d encourage you to read my review of the first course above, as I was left satisfied with the learning experience I received in the first class, but wondering if some of the concerns that students raised would be addressed.
Because I just couldn’t get enough of the new Machine Learning Specialization from the University of Washington, I decided to fill fill my schedule to the brim with another Coursera class, Social and Economic Networks: Models and Analysis, from the University of Stanford. I took a graph theory course at the University of Illinois while getting my master’s degree around the dawn of the new millennium, which among many other topics, covered things like Euler circuits, Hamiltonian paths, coloring, and the like.
After completing the Data Science Specialization from Johns Hopkins in 2014, my MOOC studies in 2015 have been fairly sporadic, partly as a result of starting a new job, and partly as a result of not seeing something that seemed like the right fit. That’s no longer the case, as I’ve recently jumped into a new specialization, the Machine Learning Specialization from the University of Washington. As great an experience as I had with the JHU specialization, this new specialization checks a couple of continuing education boxes for me that I felt the JHU specialization left lacking.
I just received my certificate from Stanford’s Statistical Learning course, taught by the legendary Trevor Hastie and Rob Tribshirani. This was the first MOOC I’ve completed since making the jump from education to the corporate world, and I did find it challenging to keep up with the material despite the fact that this class required quite a bit less on a per week basis than most of the Johns Hopkins Data Science Specialization on Coursera.
If you’ve looked into MOOCs (Massive Online Open Courses) at all, you have probably wondered how successful students are at completing them compared to traditional courses. The short answer? Not very. I’ve seen various numbers floating around in a variety of studies, citing completion rates as low as 4% and as high as 8%, but never have I seen an aggregate number over 10%. People take MOOCs for a variety of reasons.
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.
Overview of the Data Science Capstone Project and Approach The Johns Hopkins Data Science Capstone project concluded around Christmas last month. It was an interesting experience, and very different than the other classes. The project, a partnership with smartphone app maker SwiftKey, required students to create a predictive text web app that worked much like a smartphone keyboard. I spent much of the almost 2 months of the project getting up to speed on the basic terminology and approaches of Natural Language Processing, a field dedicated to the interaction between computers and human languages.
A process that began 4 months ago, the sequence of 9 Johns Hopkins Data Science Specialization courses on Coursera, wrapped up for me late last week with my last quiz in course 9, Developing Data Products. While I haven’t truly finished the specialization yet (the first ever capstone project doesn’t launch until late October), I still feel a sense of accomplishment. According to our JHU professors, as of early August, over 800,000 students have attempted at least one course in the sequence.