Jupyter is a web-based, interactive computing environment that supports many commonly-used programming languages. It has been widely adopted in the CS education community and is now rapidly expanding to other STEM disciplines due to the growing integration of programming in STEM education. However, unlike other educational platforms, there is currently no integrated way to capture, analyze, and visualize student interaction data in Jupyter notebooks. This means that teachers have limited to no visibility into student activity, preventing them from drawing insights from these data and providing timely interventions on the fly. In this paper, we present Jupyter Analytics, an end-to-end solution for teachers to collect, analyze, and visualize both synchronous and asynchronous learning activities in Jupyter. The Jupyter Analytics system consists of two JupyterLab extensions connected via a cloud-based backend. On the student side, we introduce the Jupyter Analytics Telemetry extension to anonymously capture students’ interaction activity with more structure and higher granularity than log data. On the teacher side, we introduce the Jupyter Analytics Dashboard extension, which visualizes real-time student data directly in the notebook interface. The Jupyter Analytics system was developed through an iterative co-design process with university instructors and teaching assistants, and has been implemented and tested in several university STEM courses. We report two use cases where Jupyter Analytics impacted teaching and learning in the context of exercise sessions, and discuss the potential value of our tools for CS education.