Higher education institutions need to produce individuals with data science acumen to meet the demands of a 21st century data science workforce. While most organizations and agencies have access to data, many cannot capitalize and benefit from it, as they lack access to staff with interdisciplinary skillsets. Higher education institutions tend to operate in disciplinary silos and often prepare a workforce that is accustomed to sourcing solutions for specific disciplinary problems as opposed to interdisciplinary problems relevant to current technological and societal contexts. Furthermore, the best talents from data science programs often choose to work for large technology-oriented companies. Thus, it is very difficult for small, mid-sized companies and public sector organizations to gain access to graduates with data science skills. Several universities across the U.S. created Data Science for Social Good (DSSG) programs to address the data science talent needs of public sector organizations. However, the impacts of DSSG programs on student experiences and workforce demands are understudied. Florida DSSG program has been conducting studies on student experiences and how those experiences lead to skillsets that match workforce demands. During the session, we will present our study findings and conduct live reflection activities using the Riff AI tool to gather ADSA participants’ perspectives on the data science workforce, and STEM student educational and co-curricular programs.
Container-driven Reproducible Research Made Simple Ronaldas Lencevicius (University of California, Santa Barbara) Scholarship in data science should consists of a complete software development environment along with instructions for all the results and figures. However, fully specifying and reproducing an arbitrary data science workflow can often be challenging, especially with the increasing complexity of software dependencies and computational infrastructure. Furthermore, reproducibility that relies on documentation or language specific tools can involve specialized adjustments and tweaking that many researchers may not have the time or background for. To address this common deficiency, we introduce a computational research framework to the data science community that can specify complex computational environments using an OS-level virtualization technology called containers. We show that the container-driven reproducibility approach balances flexibility and ease of use through Visual Studio Code, a popular code editor. In addition, to alleviate the steep initial learning curve of containers, we introduce a code-generating template repository for further simplifying the initial setup of Python and/or R-based workflows commonly used in data science.
Scientific software is at the heart of data-intensive research projects in nearly every domain. With the increasing availability of generative AI tools like GitHub Copilot and ChatGPT, the practice of programming for research is sure to be affected. Because scientific conclusions frequently depend on code for data analysis, collection, visualization and simulation, the potential impacts of these tools on data-intensive research may be substantial. In this structured discussion, we'll together address the question: How does the use of generative AI code tools change the work involved in writing, using, and maintaining code for data-intensive science? We'll explore how the skills needed to develop, maintain and use high-quality scientific code may be changing. We'll discuss the potential impacts of increased reliance on generative AI code tools on the validity, correctness, and maintainability of scientific code. Finally, we'll also brainstorm what kind of training, practices, and tooling might help scientists best take advantage of generative AI software tools while mitigating risks.
This session aims to share best practices for upskilling students from diverse majors in using data science to address problems within their respective disciplines. A significant challenge in this endeavor is achieving proficiency in programming-based tools, which often act as a barrier to student success. Non-programming tools, while more accessible and user-friendly for beginners, have inherent limitations such as the size of the datasets they can handle, limiting their effectiveness for more complex analyses. This session will explore the best practices for creating a data analytic pathway that transitions from no-code to low-code to high-code tools. The co-chairs will share their experiences in upskilling students from non-computing disciplines, beginning with spreadsheets, CODAP, and Weka, before transitioning to provide a gentle introduction to accessible high-code tools. The session will include audience participation through table discussions and share-outs, focusing on the following prompts: 1. What tools have you used, and what limitations or obstacles have your students encountered with those tools? 2. What resources do you believe are necessary or helpful to enable your students to overcome these obstacles? (Basically, your wish list.) 3. What ideas have you implemented, or do you have, to bring a wider group of students, teachers, and others into data science? This work is supported through NSF grant #2245958.
