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.
The data revolution : big data, open data, data infrastructures & their consequences
Chapter 1: Conceptualising Data What are data? Kinds of data Data, information, knowledge, wisdom Framing data Thinking critically about databases and data infrastructures Data assemblages and the data revolution Chapter 2: Small Data, Data Infrastructures and Data Brokers Data holdings, data archives and data infrastructures Rationale for research data infrastructures The challenges of building data infrastructures The challenges of building data infrastructuresData brokers and markets Chapter 3: Open and Linked Data Open data Linked data The case for open data The economics of open data Concerns with respect to opening data Chapter 4: Big Data Volume Exhaustive Resolution and indexicality Relationality Velocity Variety Flexibility Chapter 5: Enablers and Sources of Big Data The enablers of big data Sources of big data Directed Data Automated data Volunteered data Chapter 6: Data Analytics Pre-analytics Machine learning Data mining and pattern recognition Data visualisation and visual analytics Statistical analysis Prediction, simulation and optimization Chapter 7: The Governmental and Business Rationale for Big Data Governing people Managing organisations Leveraging value and producing capital Creating better places Chapter 8: The Reframing of Science, Social Science and Humanities Research The fourth paradigm in science? The re-emergence of empiricism The fallacies of empiricism Data-driven science Computational social sciences and digital humanities Chapter 9: Technical and Organisational Issues Deserts and deluges Access Data quality, veracity and lineage Data integration and interoperability Poor analysis and ecological fallacies Skills and human resourcing Chapter 10: Ethical, Political, Social and Legal Concerns Data shadows and dataveillance Privacy Data security Profiling, social sorting and redlining Secondary uses, control creep and anticipatory governance Modes of governance and technological lock-ins Chapter 11: Making Sense of the Data Revolution Understanding data and the data revolution Researching data assemblages Final thoughts