Organizers: Nicole Panza, Francis Marion University (npanza@fmarion.edu); Amanda Mangum, Converse University (amanda.mangum@converse.edu)
Data is now everywhere, in every field. In order for students to be competitive in the ever-data-dependent job market, they need to have basic skills associated with collecting and interpreting data. This can take many different forms at different institutions, from Data Science courses to Statistics to Computer Science and Computational Chemistry. Mathematics Departments are often tasked with building new courses to bolster both evolving general education programs and major-level courses supporting the modern interests of applied students. This session is focused on how different departments have faced this challenge to give faculty ideas of how they might also join the effort to equip students with data literacy skills.
With a data focused 2020-2030 Strategic Plan, Elon University committed to a Data Competency QEP that harnesses the data-centric interdisciplinary work done across campus. This talk will highlight this work and a variety of ideas to achieve disciple specific advanced data competency for all.
View Submission
As Mathematics Departments have been adjusting over the last decade due to the ubiquity of data and the demand for data scientists, ML and AI have taken the world by storm. Fortunately, these fields’ reliance on data has made a data-centric shift more strategic and has even given a possible new heading. We have been slowly testing a conservative, curriculum restructuring approach in hopes of a proof-of-concept, at least for Math Departments with similar conditions which continue the data-centric focus but in a new form. The restructuring is based around the principle that mathematics plays a central role in ML and AI, and the mathematics in ML and AI touches every math class in a standard college math curriculum. The restructuring is conservative in the sense that the curriculum needs little formal change since most is a shift in material presentation and a continuation of these presentations externally through research projects. There are a few minor formal changes, but we will show how we have navigated those changes at least during the proof-of-concept period. In our talk, we will discuss the details of our approach to-date including motivation and anecdotes. We believe that the most enduring benefit of this approach (besides reinvigorating math departments a second time) is helping reduce a major limitation of future development of ML and AI: understanding the mathematics of ML and AI.
View Submission
Over the past few years, mathematics faculty at Winthrop University have made efforts to infuse data science into the curriculum, culminating in the recent launch of an undergraduate degree program in data science. The program has been built with an applied focus to maximize student employability upon graduation, including features such as the use of real-world data sets throughout the curriculum, partnerships with industry and research groups, and an emphasis on domain knowledge in a student’s chosen field of interest. We will discuss how we built the program around our current curricular structure and challenges we’ve faced along the way. We will also explore several ongoing goals related to supporting the program, including the acquisition of biomedical datasets in collaboration with biology and chemistry faculty, expanding data science instruction across STEM disciplines, and mentoring undergraduate research projects in data science.
View Submission
In this session, I will describe how I teach the concepts of good and bad statistical practices. I will include lecture materials that have been created in collaboration with my students in an open resourced learning environment, as well as in-class and out-of-class exercises to allow for group work and interactive learning. I will describe the process we use for collecting, organizing and analyzing our own data set. Finally, I will describe how we work in groups to create research projects - oral presentations, utilizing our subject librarian’s expertise, and the rubric that promotes excellence, all under the umbrella of incorporating learning-centered inquiry methods to support each individual’s growth in their knowledge base.
View Submission
Converse University recognizes the increasingly important role of data literacy in educating tomorrow's workforce. With this in mind, I have created and taught a new general education course, Data Literacy. Converse has also recently approved a new general education program that includes a data component. This talk will address efforts to promote data literacy across campus and to create new courses that support this focus.
View Submission
Small steps -- a single workshop, a single course, a single tutor -- can create a launchpad for giant leaps in a data science program. We describe how data science courses, curricula and programming form a web of support for students across the disciplines at Davidson College.
View Submission
The MAA’s NSF-funded StatPREP Project had, as its mission to empower statistics instructors to create data-driven learning opportunities so that students can gain the skills and knowledge needed to work and live in a data-centric world. The forthcoming MAA Notes volume on the StatPREP Project showcases the tools and resources developed by the project, dives into examples of how to transform the introductory stats classroom (including lessons on common intro stat topics that use tools like the Little Apps to explain and explore topics using R online via a web browser), as well as other topics and considerations for improving and expanding data science education. We will explore these tools and ways they can be used in your classroom.
View Submission
In an increasingly data-driven world, fostering data literacy has become essential for students across disciplines. This presentation describes the steps the School of Mathematical and Statistical Sciences has taken to promoting data literacy through the development of an undergraduate major, specialized courses, and an online Master’s program. By integrating data literacy into the curriculum, students will not only gain essential analytical skills but will also learn to apply data-driven decision-making in diverse fields. The general education undergraduate course focuses on foundational data concepts, while the major offers an in-depth exploration of data analysis, programming, and ethical implications in a wide variety of disciplines. The online Master's program was designed for working professionals, providing flexible access to advanced data literacy training. This session will explore the rationale behind these initiatives, the curriculum design, and the impact on both academic and professional communities.
View Submission
Employers are demanding more statistical and data analysis skills from our graduates. In an effort to meet that demand, our Mathematics Department at Francis Marion University implemented a minor in statistics. This talk will discuss how we came to that decision, how we implemented the minor and how it has worked out so far.
View Submission