analyzing-the-analyzers

CHAPTER 4T-Shaped Data ScientistsWe feel that a defining feature of data scientists is the breadth of theirskills — their ability to single-handedly do at least prototype-levelversions of all the steps needed to derive new insights or build dataproducts (Mason & Wiggins, 2010). We also feel that the most successfuldata scientists are those with substantial, deep expertise in atleast one aspect of data science, be it statistics, big data, or businesscommunication.In many ways, this pattern matches the “T-shaped skills” idea that hasbeen promoted since at least the early 1990s (see citations here). The“T” represents breadth of skills, across the top, with depth in one arearepresented by the vertical bar. T-shaped professionals can more easilywork in interdisciplinary teams than those with less breadth and canbe more effective than those without depth. Data science is an inherentlycollaborative and creative field, where the successful professionalcan work with database administrators, business people, and otherswith overlapping skill sets to get data projects completed in innovativeways.For data scientists, we feel this notion can help address the communicationsissues we’ve described. By clarifying your areas of depth,perhaps using our Skills terminology, others can more quickly understandwhere your expertise lies. We also suggest that our Skills terminologycan suggest areas of career development. For instance, a datascientist with an Operations Research background and deep skills inSimulation, Optimization, Algorithms, and Math might find value inlearning some of the new Bayesian/Monte Carlo Statistics methodsthat also fall under our Math/OR Skills Group. That same data scientist19