| Title & Conference | Year |
|
Current approaches for executing big data science projects—a systematic literature review
PeerJ Computer Science Journal
| 2022 |
|
Achieving Lean Data Science Agility Via Data Driven Scrum
Proceedings of the 55th Hawaii International Conference on System Sciences
| 2022 |
|
The Risk Management Process for Data Science: Gaps in Current Practices
Proceedings of the 55th Hawaii International Conference on System Sciences
| 2022 |
|
Identifying and Addressing 6 Key Questions when Using Data Driven Scrum IEEE International Conference on Big Data (Big Data)
| 2021 |
|
CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps IEEE International Conference on Big Data (Big Data)
| 2021 |
|
Evaluating MIDST, A System to Support Stigmergic Team Coordination
Proceedings of the ACM on Human-Computer Interaction
| 2021 |
|
Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study
Proceedings of the 54th Hawaii International Conference on System Sciences
| 2021 |
|
Identifying the most Common Frameworks Data Science Teams Use to Structure and Coordinate their Projects
2020 IEEE International Conference on Big Data (Big Data)
| 2020 |
|
Exploring which agile principles students internalize when using a kanban process methodology
Journal of Information Systems Education
| 2020 |
|
The Need for an Enterprise Risk Management Framework for Big Data Science Projects.
DATA
| 2020 |
|
MIDST: an enhanced development environment that improves the maintainability of a data science analysis
International Journal of Information Systems and Project Management
| 2020 |
|
Achieving Agile Big Data Science: The Evolution of a Team’s Agile Process Methodology IEEE International Conference on Big Data (Big Data)
| 2019 |
|
SKI: An Agile Framework for Data Science IEEE International Conference on Big Data (Big Data)
| 2019 |
| 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 |