1. Data Science: A tool to capture the interest of American students
William Yslas Vélez
Department of Mathematics
University of Arizona
Tucson, Arizona

2. Curriculum Guidelines for Undergraduate Programs in Data Science Park City Math Institute (PCMI) Undergraduate Faculty Group
Data science is necessarily highly experiential; it is a practiced art and a developed skill. Students of Data Science must encounter frequent project-based, real-world applications with real data to complement the foundational algorithms and models.
They should also include the topic of data provenance and how that informs the conclusions one can draw from data.
Effective communication is a core skill of the data scientist.

3. Opportunities for less mathematically prepared students
Less mathematically prepared does not mean low-ability. I could mean that the student did not find a reason for being quantitative. Enrolling in data science courses could motivate students to become more quantitative.
Different skills are required that could attract students into this area.
Students can be provided with a research opportunity early in their career.
Relevant issues can be addressed.

4. Implications for minority populations
There are large populations of minority students at community colleges. If community colleges do not develop/adopt these courses in data science, then these students will be left out of the pipeline.
This is particularly important for the many first-year courses that are being designed. Catching the imagination of students early is key as it gives students a chance to re-tool before they use up their financial aid.

5. Native American Students
Tribal Colleges are isolated.
It is critical to provide tribal colleges with the expertise and knowledge to offered courses in data science.
Funding agencies should require that funds directed at development of courses in data science include
A) community colleges
B) Entry level courses in data science