Through a university-wide effort, Rensselaer has defined a distinctive integrated interdisciplinary undergraduate requirement in data dexterity. The data dexterity requirement begins with the premise that all Rensselaer students will acquire basic data awareness and literacy skills as part of their core undergraduate experience. To accomplish this goal, the Institute is creating a sequence of courses that culminates in students developing capabilities in the following data dexterity objectives:
• understanding of how modeling and analysis of data can be used to contribute to the solution of real-world problems
• applying quantitative algorithms and techniques and/or qualitative approaches to diverse data and interpreting the results, including an understanding of uncertainty, probability, and statistics
• communicating effectively the results and insights gained from data analysis to diverse audiences through oral, written, and multi-media presentations
• developing an awareness of the importance of data stewardship and documentation across the data life cycle
• understanding the ethical use and impact of data on society
All Rensselaer undergraduates must complete a two-course DI sequence. The first part of the requirement, termed DI-1, is fulfilled by completing a DI-1-designated introductory course that incorporates one or more instructor-led illustrations, or modules, in which data are used to answer an important question or solve an important problem. The second part of the requirement, termed DI-2, is fulfilled by completing a more advanced project-oriented and/or hands-on course in which the students delve more deeply into one or more of the data-intensive learning outcomes listed below that are appropriate and relevant to their major discipline.
DI-1 - A DI-1 course should have one or more modules or units (at least 4 – 8 contact hours) incorporated into an existing or new introductory course (in any field) that addresses the use of data in that field – how they are collected, managed, manipulated, analyzed, interpreted, and used to answer questions. For example, a DI-1 course instructor might pose a question or problem whose answer or solution might be provided by collecting and analyzing data. The instructor could describe the data collection process, explain the analytic tool(s), and show how the analysis provides an answer or solution. The instructor might also discuss ethical issues that may be associated with data collection, analysis, reporting the results, misusing the data, and/or
archiving the data. Privacy and security issues could also be discussed. Finally, the instructor could illustrate techniques typically used to organize, analyze, or visualize the data (and results) if that is appropriate. This should lead to a final interpretation of the results and how they answer the original question or lead to a solution of an important problem. All schools should plan to offer multiple DI-1 courses to ensure that every student has the opportunity to take at least one.
DI-2 - Each program will define a course or menu of courses (existing or new) within its disciplinary curriculum that develops data dexterity in a more focused discipline-specific manner. DI-2 courses are intended to be those in which students acquire the skills, knowledge, and/or understanding that are relevant to the role of data in that field. The data-focused content should be larger in scope and more in-depth than that covered in the DI-1 courses, and should involve projects, research, and/or hands-on activities. For example, a DI-2 course instructor might pose a problem and ask the students to think about what data are needed, how it can be analyzed and interpreted, whether there are any ethical issues to consider, privacy and security issues that might arise, and how best to visualize and communicate what the data are saying. The students might actually collect data, or select, curate, and validate data from existing datasets, analyze it, and work to develop an answer or solution to an original question or problem.
Students who complete data-intensive courses will be able to meet one or more of the following learning outcomes (instructors may also propose other suitable DI course-specific learning outcomes):
• Identify different types of data, information, and evidence within the relevant discipline, and be able to discuss issues of data quality, curation, validation, and uncertainty.
• Identify appropriate problems to which data can be applied, and discuss limitations, biases, assumptions, and interpretations.
• Determine appropriate analytical tools and effectively use them with relevant data types to formulate, analyze, interpret, and/or solve real-world problems.
• Effectively communicate about problems/issues in this field in which data is a relevant tool, including writing about, presenting on, and visualizing data.
• Discuss the ethical issues surrounding data in this field, including, but not limited to, responsible conduct of research, privacy, provenance, privatization, monetization, and social implications.
Faculty who want to have a course designated data intensive should submit the Submission Form for Data Intensive (DI) Courses and the course syllabus as an attachment to the DI Review Committee at: DI_Review@rpi.edu.
