Abstract: Many computational models across disciplines are based on a few fundamental analogies, and exposing this truth helps students understand the fundamentals of the science, the mathematics, and the computing. In this talk I will demonstrate introductory models from ecology, medicine, and physics to show how students can better appreciate the interdisciplinary nature of computational science instead of falling into the old “silo” way of thinking and modeling.

Abstract: Infiltration of data-driven computational methods of humanities research has generated mutual interests between the two communities of computer science and humanities. Larger institutions have adopted drastic structural reforms to meet the challenges to bridge the two fields. Successful examples include the integrated major programs launched at Stanford University and the collaborative workshop at Carnegie Mellon University. These types of exploratory experiments require 1) intensive resources as well as 2) strong support of faculty and administration. At a small college, both can be luxuries. We presented an innovative model to carry out effective synchronized courses of computational humanities and digital humanities that pulls together efforts between two small programs and needs little additional support. This paper reviews the proposal, design, and delivery of a pair of interdisciplinary graduate courses in the small college setting. We discussed the details of our implementation and provided our observations and recommendations.

Abstract: This paper describes the motivation and design of a 10-week graduate course that teaches practices for developing research software; although offered by an engineering program, the content applies broadly to any field of scientific research where software may be developed. Topics taught in the course include local and remote version control, licensing and copyright, structuring Python modules, testing and test coverage, continuous integration, packaging and distribution, open science, software citation, and reproducibility basics, among others. Lectures are supplemented by in-class activities and discussions, and all course material is shared openly via GitHub. Coursework is heavily based on a single, term-long project where students individually develop a software package targeted at their own research topic; all contributions must be submitted as pull requests and reviewed/merged by other students. The course was initially offered in Spring 2018 with 17 students enrolled, and will be taught again in Spring 2019.

Abstract: The widespread of data science languages and libraries have raised new interest in teaching computational science programming that leverage the capabilities of both single-computer and cluster-based computation infrastructures. Some of the programming paradigms are converging, yet there are specialized uses and cases that require learners to switch from one to another. In this paper, we report on our experience and action research with more than ten cohorts of mixed background students in postgraduate level data science classes. We first discuss the key mental models found to be essential to understanding problems, and then review the three fundamental models that students must face when coding and their interrelation. Finally, we discuss how decision criteria for choosing frameworks can be introduced to students.