Data science projects require the coordination of diverse skills across interdisciplinary teams. As organizations increasingly adopt automated tools for planning and staffing, decision support systems (DSS) are becoming more relevant. However, most existing DSS focus on technical workflows and neglect human competencies. This results in recommendations that may not align with actual team capabilities. This mismatch primarily impacts the success of the project. To address this issue, we propose CARE, a Competence-Aware Requirement Engineering framework that grounds the DSS design on validated user needs. Through expert interviews, we extract competence-related user stories and identify additional system requirements related to decision support functions, interfaces, and competency modeling. These results are validated through a structured survey with 195 professionals involved in data science projects. The results highlight the need for DSS features that support role-competence alignment, identify competency gaps, and provide targeted training. The respondents also prioritized system qualities such as adaptability, explainability, and integration into existing workflows. Based on this multilevel validation, we present a set of empirically grounded requirements for competence-based DSS. The CARE framework provides practical guidance for developing systems that align recommendations with user capabilities, enabling more usable and context-aware decision support in data science environments.

Automation frameworks support data science projects by automating tasks such as data preprocessing, feature engineering, model selection, and deployment. These frameworks reduce manual workload but create challenges for maintaining trustworthy, fair, and responsible analytics. Trustworthy analytics require human competencies to ensure explainability, fairness auditing, and ethical compliance, particularly where automation alone is insufficient. Existing taxonomies classify frameworks by technical scope but ignore the human competencies necessary for critical oversight. This paper addresses the gap by proposing a Competency-Aware Human-in-the-Loop (CA-HIL) Framework. The CA-HIL Framework systematically maps automation stages to intervention points, defines the associated risks, and identifies the specific human competencies required to mitigate these risks. Following the PRISMA methodology, a systematic literature review (SLR) of 2241 research articles supports the framework's development. The evaluation applies CA-HIL to leading automation systems such as TPOT, Auto-Prep, and AutoDS to identify in-dispensable human interventions. The findings demonstrate how competency-aware intervention enhances transparency, fairness, and accountability in automated workflows. This study provides a structured, evidence-based approach for designing responsible data science automation systems that bridge technical efficiency with human ethical responsibility.

Driven by technological advancements in generative AI and the shortage of data professionals in the European labour market, a growing interest in data science education has led to the development of numerous data science curricula. However, a standardized competency framework for data scientists has not yet been established. Moreover, data scientists have shifted from a generalist approach to focusing on specialised roles within the data ecosystem. As a result, data science curricula have become more specialised, often including a comprehensive introductory phase followed by in-depth studies in specific areas. However, many students struggle to combine the diverse competencies and knowledge elements a data science degree teaches. To address this challenge, this research project focuses on developing a data science framework that identifies interdependencies between competencies and knowledge elements, enabling students to choose personalized learning paths based on their individual goals and prior knowledge. This paper introduces a competency network to create personalized learning paths for an introductory data science course. It will be based on professionally logical interdependencies, which will be evaluated and optimised through the analysis of expert interviews. The goal is to positively impact students' self-efficacy, motivation, and learning outcomes by providing a structured and adaptable learning experience.

In the fast-evolving field of data science, the combination of the right team competencies has a major impact on a successful project execution. These competencies, ranging from data acquisition to model deployment, are increasingly difficult to maintain due to widespread competency shortages. This puts data science projects at risk of delays, inefficiencies, and failure, as organizations struggle to find skilled professionals. Automation frameworks - software tools designed to automate repetitive or complex tasks - offer a solution to this challenge. While these frameworks provide benefits such as reducing manual labor and improving project efficiency, they have notable limitations, particularly in covering critical phases like business understanding and deployment. Additionally, training programs also struggle to fully address the competency gap due to time, cost and scalability constraints. This paper investigates how existing automation frameworks can fill these competency gaps within data science teams more effectively. Using the CRISP-DM model as an example of a structured process, this study first identifies tasks required in each phase. Then, it matches these tasks with relevant automation frameworks to assess the extent of automation possible. Finally, these tasks are mapped to the EDISON Data Science Competence Framework to highlight which competencies automation frameworks can address. The findings suggest that automation frameworks effectively bridge competency gaps, enabling teams to complete projects more efficiently and effectively where human expertise may be lacking. In this manner, our findings serve as a reference point for data scientists and practitioners alike.