Data Science TAE Course/Module Development Grant Program
Introduction
Data science education at 黑料视频 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 offer a course/module development grant program that will provide support for current faculty to develop data science courses/modules at all levels.
Program description
The program has two tracks this year. In Track 1, we aim to develop courses/modules in which the core data science components are highly transferable. In Track 2, we aim to promote data science education in a single discipline, without a need to transfer to a different discipline.
Track 1
Many disciplines use a shared data science language and tool kit for research and education. The educational materials developed in one discipline should, ideally, be easily transferred to a different discipline. With this in mind, we encourage the development of courses or modules on the following transferable data science skills:
- Ability to prepare data for effective analysis: gather, arrange, process, and model
data, analyze large volumes of structured or unstructured data, and prepare and present
data in the best forms for decision-making and problem-solving.
Ability to write efficient and maintainable code: use programs to analyze, process, and visualize data, create programs or algorithms to parse data, and collect and prepare data through APIs. - Ability to apply math and statistics appropriately: conduct experimental design to maximize the information in the data collected, perform exploratory data analysis and identify important patterns and relationships, apply rigorous statistical thinking to extract knowledge from data, and understand the strengths and limitations of various tests/models and why they fit a given problem.
- Ability to leverage machine learning and artificial intelligence (AI): Understand how and when machine learning and AI is appropriate for the study; train and deploy models to implement productive AI solutions; explain models and predictions in terms useful to the area of study.
- Acumen to the ethics issues and societal impacts of data science: analyze the privacy, bias, and explainability aspects of data and algorithms, identify tensions between the ways data may both threaten and support different values, and engage in a broader discussion of ethical and societal issues.
Faculty members who are interested in developing a course or module in these topics
should email and seek feedback from the chair of the Data Science TAE before submitting
a proposal. This ensures that our best expertise and time are placed on efforts that
people can leverage in the future.
Successful proposals in Track 1 should make a case for why the proposed contents are
transferable and generalizable and can have broader impacts on other disciplines and
on the data science community at Binghamton as a whole. We require that each proposal,
- is either jointly submitted from faculty from multiple disciplines, or
- names a content reviewer from a different discipline in the case of a single PI proposal. The content reviewer is expected to provide professional opinions during both the proposal review process and the course development process. The Data Science TAE may help identify a content reviewer if needed.
Note that we recognize and appreciate that a course developed in a particular discipline may still have its unique identity and contexts associated with the said discipline. The effort to make courses transferable and generalizable does not imply the intent to deprive the discipline-specific contents from a course.
Track 2
We recognize that in some disciplines the use of data science in research and education
has yet to be promoted. In Track 2, we aim to fund efforts to develop groundbreaking
new developments within the framework of a discipline curriculum to promote data science.
Proposals in this track are not required to show transferability but should demonstrate
how the proposed contents will lead to more data science research activities and workforce
training in the corresponding disciplines. Successful proposals in this track typically
involve a deep integration of the data science techniques and principles with the
discipline contexts that are not meant to be untwined.
Although cross-departmental submission or naming content reviewers is not required
in Track 2, faculty who are interested in this program should consider the benefit
of consulting someone outside of his/her department to help develop the course/module.
In both tracks, courses/modules that allow instructors to venture into new interdisciplinary
areas and gain exposure to data science methods outside their own fields will be ranked
favorably. Courses/modules that are regularly offered, rather than topics courses
that are offered occasionally, are more likely to receive support.
All developed materials in both tracks, such as syllabi, lesson plans, slides, presentations,
notes, videos, programming code/lab materials and datasets, will be added to a repository
to be shared with other faculty members who wish to teach the course or module in
the future.
Faculty members are free to propose courses in either track. In each track, faculty
members can propose to develop a full-semester course (3-4 credits), a short course
(2 credits) or a course module*.
* A module is a self-contained component of a full course, which can be viewed as a
mini-course or course within a course. 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 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 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.
Use of funds
Grants of up to $5,000 for full courses, $3,000 for short courses and $2,000 for course
modules will be offered based on submitted budgets, commensurate to the effort required,
pending funds availability. The funds could be used in various ways, such as to:
- hire graduate students as assistants to help develop course materials and aid in course instruction;
- acquire off-campus training for the faculty (such as short courses, workshops, conferences) in the proposed subject field;
- partially cover course remission during the semester that the faculty member is planning and developing the course;
- 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;
- purchase specialized software or equipment necessary for running the course.
Who may submit proposals
Full-time and part-time faculty members (including adjunct faculty)
Funding availability and requirements
- We plan to award 3-4 proposals in both tracks combined, pending funding availability.
- The awarded faculty should plan to offer the proposed new course or the module by Spring 2022.
- To ensure accountability, the awarded faculty should report periodically about the course development progress to the Education Committee of the Data Science TAE. The Education Committee will provide continuous support and feedback to the awardees.
- A short report should be submitted to the Data Science TAE Steering Committee at the conclusion of the course.
Proposal Preparation
Track 1: A short letter (email) of intent should be sent to the chair of the Data
Science TAE before submitting a formal proposal. This will initiate a conversation
between the Data Science TAE and the faculty who are interested. Proposal ideas that
serve the interest for both the department and the Data Science TAE will be invited
for submission. Instruction for a formal proposal will be given at that point.
Track 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, the proportion of the module within
the course (in the case of module development, it should be at least 20%), the role
of the course in the disciplinary curriculum, how the proposed course will affect
the research and education in the discipline, the anticipated number of students taking
the course each year and the frequency that the course is typically offered.
A brief (no more than one additional page) budget and budget justification.
A support note/email from the applicant's department chair. A CV or resume of the
applicant should also be included.
All proposals will be reviewed by the Education Committee of the Data Science TAE.
Application deadline
We collect proposals on a rolling basis. Faculty applicants should submit applications
to Xingye Qiao (qiao@math.binghamton.edu). For Track 1 applications, it is highly recommended to send a letter of intent as
early as possible to allow enough time for conversations.
2020 grant recipients
- Nancy Um, Department of Art History; Proposed Course: Art/History in the Digital Age (ARTH 484B/584B) - cross listed with ANTH 570H, COLI 574X, HIST 501T
- Wei Xiao, Department of Economics; Proposed Course: Python for Economics and Finance
2019 grant recipients
- 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
2018 grant recipients
- , Department of Mathematical Sciences; Proposed course: Foundational Statistics in Bioinformatics Using R (Math 459)
- , Department of Mathematical Sciences, and Kenneth Chiu, School of Computing; Proposed course: Introduction to Data Science (Math 247/CS 207)
- Weiyi Meng, School of Computing, and Ali Alper Yayla, School of Management; Proposed course: Database and Large Data Repositories (Data 504)