| Title & Conference |
Year
|
| Data science ethical considerations: a systematic literature review and proposed project framework Ethics and Information Technology 21 (3), 197-208 |
2019
|
| Integrating ethics within machine learning courses ACM Transactions on Computing Education (TOCE) 19 (4), 1-26 |
2019
|
| Towards an integrated process model for new product development with data-driven features (NPD3) Research in Engineering Design 30 (2), 271-289 |
2019
|
| A predictive model to identify Kanban teams at risk Model Assisted Statistics and Applications 14 (4), 321-335 |
2019
|
| Exploring pair programming beyond computer science: a case study in its use in data science/data engineering International Journal of Higher Education and Sustainability 2 (4), 265-278 |
2019
|
| Ethics In Data Science Projects: Current Practices and Perceptions Proceedings of the 27th European Conference on Information Systems (ECIS) |
2019
|
| Visualizing Kanban Work: Towards an Individual Contributor View Proceedings of the 25th Americas Conference on Information Systems (AMCIS) |
2019
|
| Using a coach to improve team performance when the team uses a Kanban process methodology International Journal of Information Systems and Project Management 7 (2), 61-77 |
2019
|
| Socio-technical Affordances for Stigmergic Coordination Implemented in MIDST, a Tool for Data-Science Teams Proc. ACM Hum.-Comput. Interactions |
2019
|
| Helping Data Science Students Develop Task Modularity. Proceedings of the 52nd Hawaii International Conference on System Sciences, 1-10 |
2019
|
| Will Deep Learning Change How Teams Execute Big Data Projects? 2018 IEEE International Conference on Big Data (Big Data), 2813-2817 |
2018
|
| Improving Data Science Projects by Enriching Analytical Models with Domain Knowledge 2018 IEEE International Conference on Big Data (Big Data), 2828-2837 |
2018
|
| A Framework to Explore Ethical Issues When Using Big Data Analytics on the Future Networked Internet of Things International Conference on Future Network Systems and Security, 49-60 |
2018
|
| Key concepts for a data science ethics curriculum Proceedings of the 49th ACM technical symposium on computer science … |
2018
|
| Thoughts on current and future research on agile and lean: ensuring relevance and rigor Proceedings of the 51st Hawaii International Conference on System Sciences |
2018
|
| Data Science Roles and the Types of Data Science Programs Communications of the Association for Information Systems 43 (1), 33 |
2018
|
| Identifying the Key Drivers for Teams to Use a Data Science Process Methodology Proceedings of the 26th European Conference on Information Systems (ECIS), 58 |
2018
|
| Exploring Project Management Methodologies Used Within Data Science Teams Proceedings of the 24th Americas Conference on Information Systems (AMCIS) |
2018
|
| Does pair programming work in a data science context? An initial case study 2017 IEEE International Conference on Big Data (Big Data), 2348-2354 |
2017
|
| The ambiguity of data science team roles and the need for a data science workforce framework 2017 IEEE International Conference on Big Data (Big Data), 2355-2361 |
2017
|
| Predicting data science sociotechnical execution challenges by categorizing data science projects Journal of the Association for Information Science and Technology 68 (12 … |
2017
|
| Modular design of data-driven analytics models in smart-product development ASME 2017 International Mechanical Engineering Congress and Exposition |
2017
|
| Exploring How Different Project Management Methodologies Impact Data Science Students Proceedings of the 25th European Conference on Information Systems (ECIS), 2939 |
2017
|
| Acceptance Factors for Using a Big Data Capability and Maturity Model In Proceedings of the 25th European Conference on Information Systems (ECIS … |
2017
|
| Comparing data science project management methodologies via a controlled experiment Proceedings of the 50th Hawaii International Conference on System Sciences |
2017
|
| Big data team process methodologies: A literature review and the identification of key factors for a project’s success 2016 IEEE International Conference on Big Data (Big Data), 2872-2879 |
2016
|
| Not all software engineers can become good data engineers 2016 IEEE International Conference on Big Data (Big Data), 2896-2901 |
2016
|
| A framework for describing big data projects International Conference on Business Information Systems, 183-195 |
2016
|
| Exploring the process of doing data science via an ethnographic study of a media advertising company 2015 IEEE International Conference on Big Data (Big Data), 2098-2105 |
2015
|
| The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness 2015 IEEE International Conference on Big Data (Big Data), 2066-2071 |
2015
|