2020 Collaboratory @ Columbia Grant Recipients Embed Data Science into Traditional Disciplines
The Collaboratory Fellows Fund, part of The Collaboratory at Columbia, which was co-founded in 2016 by the Data Science Institute at Columbia University and Columbia Entrepreneurship, supports pairs of instructors–one with data science or computational expertise and the other with domain expertise–to develop and co-teach new educational offerings.
The faculty seeks to help fulfill the data literacy requirements of a discipline, specific cohort of students, or domain and to modernize and enhance Columbia’s curricula by embedding data science into more traditional disciplines.
To date, the Collaboratory has awarded 20 grants totaling $3.2M to pairs of professors. These grants have led to the creation of many courses at Columbia, including Data: Past, Present, Future, which now fulfills a science requirement for Columbia’s core curriculum, What is a book for the 21st century?, Analytics in Python, Quantitative Pricing and Revenue Analytics, and Digital Literacy.
The following five proposed courses were funded this year.
Integrating Data Science into Environmental Health Sciences’ Curricula
Andrea Baccarelli, Leon Hess Professor and Chair of Environmental Health Sciences, Mailman School of Public Health
Jeff Goldsmith, Associate Professor of Biostatistics, Mailman School of Public Health
Through three complementary initiatives, this team plans to bring graduate students in public health to a higher level of quantitative fluency and engagement with data science. First, working with biostatistics faculty members, they will develop an intensive course, Introduction to Data Science for Environmental Health. The course will be team-taught by data scientists and environmental health scientists to give students a stronger theoretical foundation in data science and provide them with a technical toolkit to take into their summer practicums. Second, with the guidance of biostatisticians, the team will evaluate how the department may more effectively implement data science throughout its curricula to synergize how data science is taught in upper-level courses. Finally, they will develop a master’s level state-certified program in environmental health data sciences.
Introduction to NYC Health Disparities Using Data Science
Mary Beth Terry, Professor of Epidemiology in Environmental Health Sciences, Mailman School of Public Health
Abigail Greenleaf, Associate Research Scientist, Mailman School of Public Health
This semester-long course will be accessible to students who do not have foundational data science skills, such as coding and analyses, in the hopes that groups underrepresented in STEM will participate in the course. The course will have no prerequisites. We will teach the data science process to students using the tenets of authentic learning and learning by applying knowledge to real-life problems. Students will simulate public health data scientists’ work by applying the data science steps to a dataset of their choice. The course will use publicly available NYC health data and introduce students to R, a freely available statistical and data visualization software, and will culminate with presentations to NYC Department of Health officials from the Center for Health Equity.
Building Next Gen of Cognitive Neuroscientists: A Suite of Interdisciplinary Human Brain Imaging Courses
Alfredo Spagna, Lecturer in the Discipline of Psychology and Director of Undergraduate Studies in Neuroscience and Behavior, Department of Psychology
Xiaofu He, Assistant Professor of Clinical Neurobiology, Columbia University Medical Center
This team proposed three different courses, starting with an undergraduate-level seminar, Fundamentals of Human Brain Imaging, followed by two graduate-level courses, Tools for Reproducible and Collaborative Neuroscience and Human Neuroimaging: Data Acquisition, Analysis, and Sharing. Students will learn techniques for measuring brain activity in humans, to deal with the challenge of handling big data sets, and to ensure that analysis pipelines can be verified, reproduced, and shared. They will incorporate a variety of active learning techniques to promote engagement and critical thinking and foster an inclusive environment open to all students, including those who have limited previous data science knowledge.
Data Science for Better Health Outcomes: A Nursing Perspective
Maxim Topaz, Elizabeth Standish Gill Associate Professor of Nursing, Columbia University Medical Center
Kathleen Mullen, Assistant Professor of Nursing, Columbia University Medical Center
This proposed pioneering course will introduce nursing students to the fascinating world of data science. Tailored to nursing, course topics will employ interactive flipped classroom learning of fundamental data science technologies (e.g., machine learning and text mining), discussion of ethical aspects of data science, and a hands-on data science project in collaboration with masters’ students from the Data Science Institute.
Programming, Analytics and Technology Curriculum for GSAPP Real Estate Development Program
Patrice Derrington, Holliday Associate Professor and Director of the Real Estate Development Program, Graduate School of Architecture, Planning and Preservation
Hardeep Johar, Senior Lecturer of Industrial Engineering and Operations Research, School of Engineering and Applied Science
This team plans to substantially advance Proptech learning by creating a curriculum that will expose real estate development graduate students to technology and programming foundations and offer them a set of electives that explore the use of these technologies. The courses will proceed in tandem with detailed instruction in the related domain content, initially with key courses and then followed by lab courses around specific applications. Additionally, to complete the process of integration, a Proptech Capstone project will be offered in the final semester, establishing important archives of the continuum of student progress.