In the first post of the “Practical Analysis” blog series, I suggested that numeracy will become an increasingly essential skill set in the 21st century, and that everyone, in one way or another, will become an analyst. But what does it actually mean to be analyst? There is no one answer. The possibilities range from someone who uses a working knowledge of analysis in their day-to-day work to hotshot data scientists at companies like Amazon and Google.
Let’s say you want to begin your journey to become an analyst. Where would you start? Like with most endeavors: with the fundamentals, of course. To help outline those fundamentals, I have assembled a panel of experts.
Our Panel of Experts
Our panel of experts is a somewhat eclectic group of three people who have worked to both expand the boundaries of analysis and make it accessible to as many people as possible. All three share the same belief: you don’t need a Ph.D. in statistics or math to become a very proficient analyst. And all of them are Ph.D.’s so they know from whence they speak. Here are brief profiles of our experts.
Where do you turn when you want to learn about the essentials of just about any subject? The “. . . for Dummies” series, of course. Deborah Rumsey is the author of “Statistics for Dummies I & II” as well as “Probability for Dummies.” She has taught statistics at Ohio State and Kansas State Universities. Rumsey is passionate both about statistics and helping people, mathematically inclined or not, put them to work at a practical level. Her “. . . for Dummies” books start with the most basic statistical concepts and work through a progression of techniques, culminating with some of the more advanced statistical algorithms that are at the heart of modern predictive analytics. In addition to being a good refresher for me, Rumsey’s books also turned me on to some useful tools I hadn’t been aware of.
“Sensemaking.” Now there’s an interesting term. It describes the essence of Stephen Few’s latest book: “Signal: Understanding What Matters in a World of Noise.” Few teaches in the MBA program at the University of California at Berkeley and is the author of several books and publications on topics directly related to analysis. His pet peeve is technology vendors who discount the importance of fundamentals – because their tools “take care of it for you.” I thought that would be a useful perspective to weave into our discussion. Few’s latest work focuses on cutting through the hype around topics from big data to AI and applying the basics of analysis to derive meaning from any set of data, large or small.
Communication, as I’ve pointed out, is arguably the most important aspect of analysis as it represents the sharing of knowledge that is produced through the analytical process. Nathan Yau has dedicated his work to sharing effective techniques for communicating data visually. Yau earned his Ph.D. in Statistics at UCLA and has worked as a “data scientist,” including a stint with the prestigious New York Times Upshot team. He is the founder and curator of the Flowing Data website, which regularly features creative approaches to charting and graphing information in ways that are intuitive and comprehensible. He also has a good knack for narrative descriptions that complement the graphics.
In upcoming posts, we’ll take a more extensive look at the work of each member of the panel to better illuminate their perspectives on how a budding analyst can learn to analyze, interpret, and communicate information. Stay tuned.
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