Greater data science at baccalaureate institutions

Donoho's JCGS (in press) paper is a spirited call to action for statisticians, who he points out are losing ground in the field of data science by refusing to accept that data science is its own domain. (Or, at least, a domain that is becoming distinctly defined.) He calls on writings by John Tukey, Bill Cleveland, and Leo Breiman, among others, to remind us that statisticians have been dealing with data science for years, and encourages acceptance of the direction of the field while also ensuring that statistics is tightly integrated. As faculty at baccalaureate institutions (where the growth of undergraduate statistics programs has been dramatic), we are keen to ensure statistics has a place in data science and data science education. In his paper, Donoho is primarily focused on graduate education. At our undergraduate institutions, we are considering many of the same questions.Comment: in press response to Donoho paper in Journal of Computational Graphics and Statistic

Integrating computing in the statistics and data science curriculum: Creative structures, novel skills and habits, and ways to teach computational thinking

Nolan and Temple Lang (2010) argued for the fundamental role of computing in the statistics curriculum. In the intervening decade the statistics education community has acknowledged that computational skills are as important to statistics and data science practice as mathematics. There remains a notable gap, however, between our intentions and our actions. In this special issue of the *Journal of Statistics and Data Science Education* we have assembled a collection of papers that (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. We believe that it is critical for the community to redouble our efforts to embrace sophisticated computing in the statistics and data science curriculum. We hope that these papers provide useful guidance for the community to move these efforts forward.Comment: In press, Journal of Statistics and Data Science Educatio

The importance of good coding practices for data scientists

Many data science students and practitioners are reluctant to adopt good coding practices as long as the code "works". However, code standards are an important part of modern data science practice, and they play an essential role in the development of "data acumen". Good coding practices lead to more reliable code and often save more time than they cost, making them important even for beginners. We believe that principled coding practices are vital for statistics and data science. To install these practices within academic programs, it is important for instructors and programs to begin establishing these practices early, to reinforce them often, and to hold themselves to a higher standard while guiding students. We describe key aspects of coding practices (both good and bad), focusing primarily on the R language, though similar standards are applicable to other software environments. The lessons are organized into a top ten list

Effects of Middle-Ear Disorders on Power Reflectance Measured in Cadaveric Ear Canals

Objective: Reflectance measured in the ear canal offers a noninvasive method to monitor the acoustic properties of the middle ear, and few systematic measurements exist on the effects of various middleear disorders on the reflectance. This work uses a human cadaver-ear preparation and a mathematical middle-ear model to both measure and predict how power reflectance R is affected by the middle-ear disorders of static middle-ear pressures, middle-ear fluid, fixed stapes, disarticulated incudostapedial joint, and tympanic-membrane perforations. Design: R was calculated from ear-canal pressure measurements made on human-cadaver ears in the normal condition and five states: (1) positive and negative pressure in the middle-ear cavity, (2) fluidfilled middle ear, (3) stapes fixed with dental cement, (4) incudostapedial joint disarticulated, and (5) tympanic-membrane perforations. The middle-ear model of Kringlebotn (1988) was modified to represent the middle-ear disorders. Model predictions are compared with measurements. Results: For a given disorder, the general trends of the measurements and model were similar. The changes from normal in R, induced by the simulated disorder, generally depend on frequency and the extent of the disorder (except for the disarticulation). Systematic changes in middle-ear static pressure (up to ±300 daPa) resulted in systematic increases in R. These affects were most pronounced for frequencies up to 1000 to 2000 Hz. Above about 2000 Hz there were some asymmetries in behavior between negative and positive pressures. Results with fluid in the middle-ear air space were highly dependent on the percentage of the air space that was filled. Changes in R were minimal when a smaller fraction of the air space was filled with fluid, and as the air space was filled with more saline, R increased at most frequencies. Fixation of the stapes generally resulted in a relatively small low-frequency increase in R. Disarticulation of the incus with the stapes led to a consistent lowfrequency decrease in R with a distinctive minimum below 1000 Hz. Perforations of the tympanic membrane resulted in a decrease in R for frequencies up to about 2000 Hz; at these lower frequencies, smaller perforations led to larger changes from normal when compared with larger perforations. Conclusions: These preliminary measurements help assess the utility of power reflectance as a diagnostic tool for middle-ear disorders. In particular, the measurements document (1) the frequency ranges for which the changes are largest and (2) the extent of the changes from normal for a spectrum of middle-ear disorders