Executive Data Science: Managing Teams and Projects

To successfully lead in today's technology-driven market, managers must bridge the gap between business strategy and complex data science. This course prepares you to guide data teams and align technical outcomes with organizational goals. You will transition from a traditional manager to a confident data science leader. Through written case studies and strategic decision-making exercises, you will learn how to structure data teams, evaluate project feasibility, and navigate the lifecycle of a modern data initiative from conception to deployment. What you'll learn: - Understand foundational data science terminology, machine learning workflows, and modern cloud architectures - Structure and manage high-performing data teams using modern agile methodologies - Evaluate project feasibility, data quality, and business alignment before allocating resources - Direct the lifecycle of a data product, incorporating basic MLOps and governance frameworks - Navigate strategic decisions regarding AI opportunities, including large language models and modern data workflows - Formulate data-driven solutions to common organizational bottlenecks and communication gaps The course begins with foundational concepts of data architectures and machine learning lifecycles, establishing a clear vocabulary. You will then explore real-world management scenarios, analyzing written case studies to practice making high-impact decisions under realistic business constraints. This course is designed for aspiring managers, product owners, and business leaders who want to direct data initiatives without needing to write code themselves. No prior technical or programming experience is required. Start reading today to confidently lead your next data science project.