In data analysis, we make use of a lot of theory, whether we like to admit it or not. In a traditional statistical training, things like the central limit theorem and the law of large numbers (and their many variations) are deeply baked into our heads. I probably use the central limit theorem everyday in my work, sometimes for the better, and sometimes for the worse. Even if I’m not directly applying a Normal approximation, knowledge of the central limit theorem will often guide my thinking and help me to decide what to do in a given data analytic situation.
I was recently asked to moderate an academic panel on the role of universities in training the data science workforce. I preceded each question with opinionated introductions which I have fused into this blog post. These are weakly held opinions so please consider commenting if you disagree with anything. To discuss data science education we first need to clearly state what it means. The panel organizers defined data science as “an emerging discipline that draws upon knowledge in statistical methodology and computer science to create impactful predictions and insights for a wide range of traditional scholarly fields.
Editor’s Note: I attended an ASA Chair’s meeting and spoke about ways we could support junior faculty in data science. After giving my talk Galin Jones, Professor and Director of Statistics at University of Minnesota, and I had an interesting conversation about how they had changed their promotion criteria in response to a faculty candidate being unique. I asked him to write about his experience and he kindly contributed the following post.
tl;dr check out our new paper on the relationship between MOOC completion and economic outcomes! Last Monday we launched our Chromebook Data Science Program so that anyone with an internet connection, a web browser, and the ability to read and follow instructions could become a data scientist. Why did we launch another MOOC program? Aren’t MOOCs dead? Well we didn’t think so :). We have been pretty excited about MOOCs for a while now and now run five different MOOC programs through the Johns Hopkins Data Science Lab.
The Johns Hopkins Data Science Lab has been teaching massive online open courses for more than 5 years now. During that time we’ve reached more than 5 million learners who want to break into the number one rated job in America. While we have been incredibly excited about the results of these training programs, we’ve also learned over the last 5+ years that there are still significant barriers to getting into data science.
Rolando Acosta and I recently posted a manuscript on bioRxiv describing the effects of Hurricane María, based on an analysis of mortality data provided by the Demographic Registry. I was also an author on a paper published in May based on a survey of 3,000 households. These are very different datasets. Assuming it is complete, the Demographic Registry dataset provides much more precise quantitative information. However, this dataset was not made publicly available until June 1, 2018, three days after the paper based on the survey data was released.
There are often discussions within the data science community about which tools are best for doing data science. The most recent iteration of this discussion is the so-called “First Notebook War”, which is well-summarized by Yihui Xie in his blog post (it is a great read). One thing that I have found missing from many discussions about tooling in data analysis is an acknowledgment that data analysis tends to advance through different phases and that different tools can be more or less useful in each of those phases.
Hilary Parker and I just released part 2 of our book club discussion of Nigel Cross’s book Design Thinking and it centers around a profile of designer Gordan Murray, who spent his career designing Formula One race cars. One of the aspects of his job as a designer is taking a “systems approach” to solving problems. Coupled with that approach is his role in balancing the various priorities of members of his team.
This week Hilary Parker and I have started our “Book Club” on Not So Standard Deviations where we will be discussing Nigel Cross’s book Design Thinking: Understanding How Designers Think and Work. We will be talking about how the work of designers parallels the work of data scientists and how many of the principles developed in design port over so well to data analysis. While data visualization has always taken cues from design, I think much broader aspects of data analysis could benefit from the work studying design.
One conversation I’ve had a few times revolves around the question, “What’s the difference between science and data science?” If I were to come up with a simple distinction, I might say that Science starts with a question; data science starts with the data. What makes data science so difficult is that it starts in the wrong place. As a result, a certain amount of extra work must be done to understand the context surrounding a dataset before we can do anything useful.