Related papers: Data Science for Engineers: A Teaching Ecosystem
In data science education, the importance of learning to solve real-world problems has been argued. However, there are two issues with this approach: (1) it is very costly to prepare multiple real-world problems (using real data) according…
We describe an introductory data science course, entitled Introduction to Data Science, offered at the University of Illinois at Urbana-Champaign. The course introduced general programming concepts by using the Python programming language…
Is it possible to derive organizing principles of higher education systems from the applicants' choices? Here we introduce the Higher Education Space (HES) as a way to describe the complex relationship between degree programs. The HES is…
Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the…
We introduce a newly designed undergraduate-level interdisciplinary course in scientific computing that aims to prepare students as the next generation of research-oriented computational scientists and engineers. The course offers students…
A hybrid teaching approach that relied on combining Project Based Learning with Team Based Learning was trialled in an engineering module during the past five years. Our motivation was to expose students to real-world authentic engineering…
According to a recent report from the European Commission, the world generates every minute 1.7 million of billions of data bytes, the equivalent of 360,000 DVDs, and companies that build their decision-making processes by exploiting these…
Statistics is running the risk of appearing irrelevant to today's undergraduate students. Today's undergraduate students are familiar with data science projects and they judge statistics against what they have seen. Statistics, especially…
Data Science is a complex and evolving field, but most agree that it can be defined as a combination of expertise drawn from three broad areascomputer science and technology, math and statistics, and domain knowledge -- with the purpose of…
Theoretical ecologists have long leveraged empirical data in various forms to advance ecology. Recently increased volumes and access to ecological data present an expanding set of opportunities for theoreticians to inform model development,…
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to…
Sustainability is increasingly recognized as a critical dimension of engineering education, yet its integration into Software Engineering curricula remains a challenge. This paper reports on a case study that examines how sustainability is…
Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and…
In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder.…
There are both technical and social issues regarding the design of sustainable scientific software. Scientists want continuously evolving systems that capture the most recent knowledge while developers and architects want sufficiently…
Understanding the earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array…
This article presents an educational proposal based on the computational implementation of a model of interaction between particles of different types, as a tool for the development of STEM (Science, Technology, Engineering, and…
While data science has emerged as a contentious new scientific field, enormous debates and discussions have been made on it why we need data science and what makes it as a science. In reviewing hundreds of pieces of literature which include…
The growth of the data science field requires better tools to understand such a fast-paced growing domain. Moreover, individuals from different backgrounds became interested in following a career as data scientists. Therefore, providing a…
A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growing larger and becoming distributed; and their location,…