Course/Module Development Grant

2019 Data Science TAE Course/Module Development Grant Program


Data science education at Binghamton University must be based upon a robust and diverse interdisciplinary curriculum, yet directed at the shared purpose of developing student expertise in data science. The Data Science TAE recognizes that many faculty members across campus are interested in developing curriculum in data science, but lack the resources or flexibility to do so. We propose a course/module development grant program, which will provide support for current faculty wishing to establish permanent courses and modules in data science at the undergraduate and graduate levels. These courses and modules will contribute to degree and non-degree programs in data science, while also allowing for enhancement of existing programs in various disciplines.

2019 grant recipients are:

  • Ali Alper Yayla, School of Management; Proposed Module: Data Visualization
  • Congrui Jin, Department of Mechanical Engineering; Proposed Module: Big Data Science in Mechanics
  • Jeffrey T. Pietras, Molly Patterson and Timothy de Smet, Department of Geological Sciences and Environmental Studies; Proposed Module: Data Science Course Modules for Geosystems
  • Loretta Mason-Williams, Department of Teaching, Learning and Educational Leadership; Proposed Course: Education and Data Analytic: Improving School Outcomes through Data Literacy and a Framework for Problem Solving

Proposal process

This program invites proposals from full-time faculty members across campus each year. Applicants will propose development of a course in data science, or a module to incorporate a data science component into an existing course, at the undergraduate or graduate level.

Grants of up to $3,000 will be offered based on submitted budgets, commensurate to the effort required. The funds could be used in various ways, such as to:

  1. Hire a graduate student as a specialized teaching assistant to help develop course materials and aid in course instruction.
  2. Acquire off-campus training for the faculty (such as short courses, workshops, conferences) in the proposed subject field.
  3. Partially cover course remission during the semester that the faculty member is planning and developing the course, which would be offered in a following semester.
  4. In the case of co-teaching, partially buy out a course in one of their departments during the semester in which the course is offered.
  5. Purchase specialized software or equipment necessary for running the course.

Selection criteria

A priority of this year's program is module development. A module is a self-contained component of a full course, which can be viewed as a mini-course or course within a course. Preferences are given to modules that can be added to an existing disciplinary course to significantly enrich the data science component and provide data science exposure to the students. The combination of a data science component within a disciplinary course is commonly referred to as the "X + Data" framework, in which X stands for a given domain discipline. Examples of a module of this kind include a statistical analysis module developed in the context of a biology course or a web-scraping module in the context of a social science course. We are especially interested in funding modules in disciplines that are under-represented in data science. Modules that allow instructors to venture into new interdisciplinary areas and gain exposure to data science methods outside of their own fields will be ranked favorably. Modules developed for regularly offered courses, rather than topics courses that are offered occasionally, are more likely to receive support. We only fund modules that are at least 20 percent of a full course, that is, roughly 2.8 weeks of coursework (or more) in a 14-week semester. 

Full course developments may also be funded. For full course developments, preferences are placed on high-demand courses in data science, such as introduction to data analysis using python, introduction to statistical analysis using R, a machine-learning or data-mining course intended for all majors, mathematics for data science, general-purpose data visualization, social media and web analytics, etc.

A selection committee will be comprised of faculty members with expertise in data science, with representation from various fields.

All awarded faculty members must:

  1. Offer the proposed new course or the data-enhanced course with the proposed module by fall 2020.
  2. Produce curricular materials, such as syllabi, lesson plans, slides, presentations, notes, programming code/lab materials and datasets, which will be added to a repository to be shared with other faculty members who wish to teach the course or module in the future.
  3. Submit a short report to the Data Science TAE Steering Committee at the conclusion of the course.


Faculty applicants should submit the following materials to Xingye Qiao ( by May 31, 2019:

  1. A current CV.
  2. A proposal of no more than two pages that includes (but is not limited to): a course/module description including the role of data in the course, the target audience for the course, when the course will be offered, and in the case of module development, the proportion of the module within the course (it should be at least 20 percent), the role of the course in the disciplinary curriculum, the typical number of students taking the course each year and the frequency that the course is typically offered.
  3. A brief (no more than one additional page) budget and budget justification.
  4. A support note/email from the applicant's department chair.

This proposal is modeled after a similar program at UNC

2018 funded course proposals:

  • Sanjeena Dang, Department of Mathematical Sciences; Proposed course: Foundational Statistics in Bioinformatics Using R (Math 459)
  • Xingye Qiao, Department of Mathematical Sciences, and Kenneth Chiu, Department of Computer Science; Proposed course: Introduction to Data Science (Math 247/CS 207)
  • Weiyi Meng, Department of Computer Science, and Ali Alper Yayla, School of Management; Proposed course: Database and Large Data Repositories (Data 504)