James Baldwin, Senior Lecturer at Sheffield Hallam University

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Dr James Baldwin BSc MSc PhD FHEA

Senior Lecturer


Summary

James is the course leader for the MSc Computing within the College of Business, Technology, and Engineering, Department of Computing at Sheffield Hallam University. He is a proactive lecturer that teaches on the foundation year, undergraduate and postgraduate modules. His main teaching focuses on Databases, Cloud Computing and Big Data Analytics. He has successfully supervised dissertations both at undergraduate and postgraduate level. James is an active researcher based on areas of Text Mining, Sentiment Analysis, Social Media, Big Data Analytics and Machine Learning.

 

Teaching

School of Computing and Digital Technologies

College of Business, Technology and Engineering

Subject Area:
Smart Computing

 

Courses Taught:
BSc (Honours) Computing
BSc (Honours) Computing with Foundation Year
MSc Computing
MSc Big Data Analytics

 

Research

BALDWIN, James, BRUNSDON, Teresa, GAUDOIN, Jotham and HIRSCH, Laurence (2018). Towards a social media research methodology: Defining approaches and ethical concerns. International journal on advances in life sciences, 10. https://shura.shu.ac.uk/id/eprint/23420

Publications

Conference papers

Baldwin, J., Brunsdon, T., Gaudoin, J., & Hirsch, L. (2018). Towards a social media research methodology: Defining approaches and ethical concerns. International journal on advances in life sciences, 10. http://www.iariajournals.org/life_sciences/

Theses / Dissertations

Baldwin, J. (2021). Analysing social media data using sentiment analysis in relation to public order. (Doctoral thesis). Supervised by Gaudoin, J. http://doi.org/10.7190/shu-thesis-00439

Presentations

Fernandes, C., Zasada-James, J., Adhikari,, K., Ul Hasan, N., Baldwin, J., & Hirsch, L. (2024). Fog Computing Assisted Anaesthesia Monitoring to Enhance Realtime Surgical Efficiency. Presented at: 4th International Symposium on Sensing and Instrumentation in IoT Era, Azores, Portugal, 2024 http://doi.org/10.1109/ISSI63632.2024.10720491

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