The D4 training program will provide an inclusive training on trustworthy data science. It will be available for students who pursue graduate degrees and concurrent BS/MS degrees. Fellowship is available for underrepresented groups of students in STEM graduate programs, such as women and minority students. The training program consists of a boot camp, course work, research experience, seminars, and industry experiential learning experience. The training program can be completed in three semesters
- Do I have the quantitative aptitude and skills?
- Will my major professor allow me to participate in an interdisciplinary program?
- Am I willing to be part of a group of diverse researchers and explore topics outside of my major?
The D4 program accepts students eligible for funding and international students.
The funding agency is the National Science Foundation, which requires residency status to participate in the program.
The application deadline is February 1st. Review of applications for the Traineeship program will begin in early February, with offers being made by March 15. The acceptance deadline will be April 15th for the first round of offers. Applications submitted after February 1st will be considered on a space-available basis.
Admitted trainees may be selected for a 12 month financial package which includes a $34,000 stipend, tuition waiver, & health insurance.
Eligibility requirements for funding are:
- BS/MS student or MS, PhD student at ISU
- Research interest related to Data Science
- At least 2 years of study remaining
- U.S. Citizen or a permanent resident
The traineeship is funded by National Science Foundation Award No. 2152117
Benefits to trainees
Trainees will be able to
- Identify risks to and measures of trustworthiness of data science lifecycles defined as the processes that plan for, acquire, manage, analyze, and infer from data collectively
- Conduct research to improve trustworthiness of data science lifecycles.
- Involve domain experts in biological science and bioengineering for data driven decisions from biological data.
- Learn about applications of data science lifecycles in practice.
- Recognize ethical issues arising during different stages of data science lifecycles and make ethical decisions.
- Communicate the knowledge, the research, and the findings to stakeholders.
- Contribute as effective members of multidisciplinary teams.