I’ve gone through two of the four courses in Executive Data Science Specialization on Coursera and it’s really, really good. So good that a Friday night and a good chunk of the weekend were happily invested into learning. That weekly schedule that I’d carefully set up: obliterated!
I’ve been looking for an effective way to learn how to work with data science professionals in order to incorporate the growing opportunities that the practice brings to digital product design1.
Starting with a vague hunch that it would be a critical investment but knowing better than to try studying algorithms and R and all that myself2, it’s been an ongoing process this past year to pinpoint what I could be learning that would enable me to act as a multiplier.
A self-initiated data analysis project turned out to be a great start. Now, I can say with confidence that I’m learning how to 1) identify opportunities best served by utilizing quantitative data 2) recruit and empower the right people and 3) feed the insights back into a product or organization. And that it builds upon my existing skills and mindset to managing teams, projects, and products3. Phew.
And this course is a perfect supplement to that tiny experiment. I’m really excited to go through the remaining courses and create tangible opportunities at AQ.
I’ll post a recap after finishing all four courses but in the meantime, here’s the course description. Again, it’s the Executive Data Science Specialization by the Data Science Lab at John Hopkins University. Two of the three professors are the guys behind the great Simply Statistics blog.
Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects.
In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You’ll get a crash course in data science so that you’ll be conversant in the field and understand your role as a leader. You’ll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You’ll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you’ll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.
Focus-wise, four hours was the maximum amount of time that I could spend consecutively on the course, so I watched Ex-Machina to stay in the mood 🙂
By the way, it’s the first time I’ve managed to stick to an online course. I guess the trick is to chose a subject that you want to learn right now.
- Check out the Airbnb data team’s work on using machine learning to leverage host preferences. As Cennydd Bowles says in this article – “AI is becoming a cornerstone of user experience.” ↩
- A few months ago, I flirted with the idea of taking the Data Science Specialization before coming to my senses. Now that I’m a bit more knowledgable… well, never say never. It’s by the same team as the Executives course, which is a huge plus. ↩
- And Riley Newman, head of data science at Airbnb, says that “data isn’t numbers, it’s people” and explains that Airbnb characterizes data as the “the voice of our customers”. Which aligns perfectly with user research, right? Seeing data as another tool to work towards the same goal makes it much more accessible. ↩