Facilitating Team-Based Data Science: Lessons Learned from the DSC-WAV Project

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have limited exposure to team-based data science and the principles and tools of collaboration that are encountered outside of school. In this paper, we describe the DSC-WAV program, an NSF-funded data science workforce development project in which teams of undergraduate sophomores and juniors work with a local non-profit organization on a data-focused problem. To help students develop a sense of agency and improve confidence in their technical and non-technical data science skills, the project promoted a team-based approach to data science, adopting several processes and tools intended to facilitate this collaboration. Evidence from the project evaluation, including participant survey and interview data, is presented to document the degree to which the project was successful in engaging students in team-based data science, and how the project changed the students\u27 perceptions of their technical and non-technical skills. We also examine opportunities for improvement and offer insight to other data science educators who may want to implement a similar team-based approach to data science projects at their own institutions

The relation between media promotions and service volume for a statewide tobacco quitline and a web-based cessation program

<p>Abstract</p> <p>Background</p> <p>This observational study assessed the relation between mass media campaigns and service volume for a statewide tobacco cessation quitline and stand-alone web-based cessation program.</p> <p>Methods</p> <p>Multivariate regression analysis was used to identify how weekly calls to a cessation quitline and weekly registrations to a web-based cessation program are related to levels of broadcast media, media campaigns, and media types, controlling for the impact of external and earned media events.</p> <p>Results</p> <p>There was a positive relation between weekly broadcast targeted rating points and the number of weekly calls to a cessation quitline and the number of weekly registrations to a web-based cessation program. Additionally, print secondhand smoke ads and online cessation ads were positively related to weekly quitline calls. Television and radio cessation ads and radio smoke-free law ads were positively related to web program registration levels. There was a positive relation between the number of web registrations and the number of calls to the cessation quitline, with increases in registrations to the web in 1 week corresponding to increases in calls to the quitline in the subsequent week. Web program registration levels were more highly influenced by earned media and other external events than were quitline call volumes.</p> <p>Conclusion</p> <p>Overall, broadcast advertising had a greater impact on registrations for the web program than calls to the quitline. Furthermore, registrations for the web program influenced calls to the quitline. These two findings suggest the evolving roles of web-based cessation programs and Internet-use practices should be considered when creating cessation programs and media campaigns to promote them. Additionally, because different types of media and campaigns were positively associated with calls to the quitline and web registrations, developing mass media campaigns that offer a variety of messages and communicate through different types of media to motivate tobacco users to seek services appears important to reach tobacco users. Further research is needed to better understand the complexities and opportunities involved in simultaneous promotion of quitline and web-based cessation services.</p

Comparing groups: randomization and bootstrap methods using R

A hands-on guide to using R to carry out key statistical practices in educational and behavioral sciences research Computing has become an essential part of the day-to-day practice of statistical work, broadening the types of questions that can now be addressed by research scientists applying newly derived data analytic techniques. Comparing Groups: Randomization and Bootstrap Methods Using R emphasizes the direct link between scientific research questions and data analysis. Rather than relying on mathematical calculations, this book focus on conceptual explanations and the use of statistical computing in an effort to guide readers through the integration of design, statistical methodology, and computation to answer specific research questions regarding group differences. Utilizing the widely-used, freely accessible R software, the authors introduce a modern approach to promote methods that provide a more complete understanding of statistical concepts. Following an introduction to R, each chapter is driven by a research question, and empirical data analysis is used to provide answers to that question. These examples are data-driven inquiries that promote interaction between statistical methods and ideas and computer application. Computer code and output are interwoven in the book to illustrate exactly how each analysis is carried out and how output is interpreted. Additional topical coverage includes: Data exploration of one variable and multivariate data Comparing two groups and many groups Permutation tests, randomization tests, and the independent samples t-Test Bootstrap tests and bootstrap intervals Interval estimates and effect sizes Throughout the book, the authors incorporate data from real-world research studies as well aschapter problems that provide a platform to perform data analyses. A related Web site features a complete collectionof the book's datasets along with the accompanying codebooks and the R script files and commands, allowing readers to reproduce the presented output and plots. Comparing Groups: Randomization and Bootstrap Methods Using R is an excellent book for upper-undergraduate and graduate level courses on statistical methods, particularlyin the educational and behavioral sciences. The book also serves as a valuable resource for researchers who need a practical guide to modern data analytic and computational methods