Casting a Wider (Neural) Net: Introducing Data Science and Machine Learning to a Larger Audience
A presentation introducing foundational concepts related to Data Science and Machine learning was given five times over a one year period to audiences with different backgrounds. This report summarizes the successes and failures in delivering this material. Insights about the topic and useful teaching resources are presented.
Machine Learning for Business Students: An Experiential Learning Approach
This paper reports on the design and teaching of a course on Machine Learning (ML) for business students, focusing on the interaction between content and teaching approach. We demonstrate that the nature of ML technology is amenable to experiential learning and is well suited for business students as users of the technology. The community-based tools, documentation and datasets enable non-programmers to use and adapt open, public-domain ML examples. Students learn how to select algorithms for specific data and tasks, experiment with hyper-parameters and neural-network structures, evaluate results and interpret their business implications. We conclude that business students, at least in our school, often have good abstract understanding of computing and may be ready for deeper learning of digital technology. The general-purpose nature of ML makes it suitable for real-world business problems, making it business-relevant technology that should be introduced into business schools.
A Personal Perspective on the Origin(s) and Development of 'Big Data': The Phenomenon, the Term, and the Discipline, Second Version
I investigate Big Data, the phenomenon, the term, and the discipline, with emphasis on origins of the term, in industry and academics, in computer science and statistics/econometrics. Big Data the phenomenon continues unabated, Big Data the term is now firmly entrenched, and Big Data the discipline is emerging.