Project Management & Leadership for Data Scientists

Getting data science done!

Data scientists are T-shaped professionals. They have deep expertise in analytics and data engineering, but they also master boundary-crossing skills such as leadership, problem solving and project management.

This course teaches the skills needed to get things done and be a successful, entrepreneurial data-science leader. How to win support in the organization, navigate through organizational politics and prevent that your project is swamped by countless distractions? How to structure complex and messy business problems into meaningful questions that you can answer by predictive or explanatory analytics? How to help the organization to move forward in its development towards a data-driven organization? The course offers conceptual models and techniques that capture the essence of complex skills, and we discuss realistic cases in order to translate theory to practical insights.

What it could bring to you?

• Helps you to grow professionally, from programmer, IT engineer or analyst, to senior professional, senior data scientist, or leader.
• Helps you to turn data-science initiatives and groups into effective teams that deliver results and get things in motion.
• Helps you to lead your organization in the transition process towards a data-driven way of working.

What will you learn?

• Turning data-analytic thinking into effective projects: The CRISP-DM model to structure data-science projects. Getting flow in data-science projects: agile project management, heartbeat progress reviews, the project portfolio and backlog. Translating business problems into data-analytic problems, and into the data-analytic workflow.
• Personal leadership in data science: becoming an entrepreneurial data scientist, learning to recognize and understand business value. Growing as a leader, from reactive to proactive to being an influencer in a political forcefield. Coaching a team, but above all: coaching your environment and the organization’s management. Committed leadership, motivation, and achieving results by letting go.
• Managing a data-science project: translating goals into plans into action. Effective structures for professionals in action: stakeholder management, work breakdown structure, value-proposition canvas and data-mining canvas. Planning under uncertainty and the importance of cadence.
• Managing data science in the organization: The supportive infrastructure for data-science projects. Creating a business strategy, designing a data-science innovation roadmap on multiple horizons, translating the roadmap into a project backlog. The roles of the data engineer, data analyst, data scientist, domain expert and sponsor. Leading the transformation to a data-driven organization: organization development as a learning process, identifying and dealing with organizational barriers, the punctuated equilibrium model.

Audience group

• Data scientists with project responsibilities, obligations for results, or who do data-science projects for commercial customers
• Senior data scientists who have a leading role in developing a data-science initiative in the organization
• Executives who are shaping a data-science or digitization initiative
• Project managers, group leaders and department leaders who are managing data-science projects within their groups