Design Thinking: Understanding How Designers Think and Work

"The five books that I chose are probably at quite a high level of abstraction in terms of data science. I could have chosen a bunch of books about detailed statistics, R programming, etc. but I purposely chose to go a bit higher up, and this book might be the epitome of this choice. Being a person who’s done a lot of data analysis, one thing that I’ve found frustrating is the lack of proper mental model for what is going on when you analyse data. Most universities have a class called ‘data analysis’, and typically it presents various useful tools, but rarely discusses what actually happens when you do the analysis itself. Design Thinking gives a mental model to describe what happens in this process. “Even the best data analysts I’ve worked with have trouble saying what they’re doing—they just do it.” Yes, it is about design in general, not specifically about data analysis, but there’s a lot that we can borrow from that world to adapt those concepts to our needs. They provide a way of thinking and a vocabulary. Even the best data analysts I’ve worked with have trouble saying what they’re doing—they just do it. Every data analysis feels unique, so it’s very hard to generalize across different experiences. Exactly; a colleague of mine often says that there’s a reason it’s called re search: you’re rarely inventing something completely new on your own, but rather you’re always borrowing and improving from what someone else conceptualized. I really think it’s a great book, even if you have no interest in design per se . It doesn’t take much of a mental leap to see how what Nigel Cross describes is relevant to what we’re doing in data science. Yes, it’s a key step. If you take the example of the students pursuing a PhD in biostatistics here at Johns Hopkins, they spend three years with an academic advisor, essentially learning these types of skills. It’s hard to scale as a model, because it takes a long time and really requires focused learning and deliberate practice to master efficient data analysis across different kinds of data. Of course it’s very important to know the tools, but the tools change and are more susceptible to automation. On the other hand, things like design thinking are key parts of advancing up the ladder, and to becoming a leader in this area, who is able to coordinate the activities of a group of people. Get the weekly Five Books newsletter Actually, a lot of people who took our courses on Coursera already had advanced degrees, usually Masters but also PhDs. What they didn’t have was knowledge of these tools, and they needed to fill a gap, which online courses are perfect for. I think that any person who’s gone through a scientific program has really learned the same higher-level skills; maybe they just know Matlab instead of R for example. If you’re maybe younger or haven’t had that kind of education yet, then these kinds of courses are tremendously useful in getting you your first job—but the option to go further down the road and follow a good program will usually be very beneficial. The other important aspect is that some programs, including Bachelor’s degrees, can be extremely expensive. So following short programs and online courses is definitely a good way to test the waters and see if you like the subject."