The development of data science curricula has gained attention in academia and industry.
Yet, less is known about the pedagogical practices and tools employed in data science …

In this work, we explain the setup for a technical, graduate-level course on Fairness,
Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the …

Contribution: This article presents evidence that electrical engineering, computer science,
and data science students, participating in introduction to machine learning (ML) courses …

The information schools, also referred to as iField schools, are leaders in data science
education. This study aims to develop a data science graduate curriculum model from an …

An internship promotes the idea of learning through experiencing and has been an
important way for data science students to gain practical knowledge and skills for solving …

Contribution: This article discusses a research–practice partnership (RPP) where instructors
from six undergraduate courses in three universities developed data science modules …

Environmental data science is a multi‐disciplinary and mature field of research at the
interface of statistics, machine learning, information technology, climate and environmental …

Abstract The CC2020 Task Force was established to provide a useful framework for
universities and undergraduate computing majors to conceptualize computing education [1] …

Model explainability has become an important problem in machine learning (ML) due to the
increased effect that algorithmic predictions have on humans. Explanations can help users …

This paper describes a computational learning resource on Reproducible Data Science with
Python. The resource provides an accessible, hands-on introduction to data science …