Existing data science tools, such as Jupyter Notebook, require users to be familiar with coding, making them unsuitable for introductory data-science courses that need concrete ways to illustrate core concepts, such as tables, schemas, transformations, and ML models, before students are comfortable with programming. Other education-related challenges, like collaboration and computing resource requirements, further complicate this problem. This demonstration shows how Texera, an open-source, browser-based visual workflow system supporting collaborative data science and AI/ML, is designed to overcome these challenges in the classroom setting and presents a scenario in which two students use Texera to learn data science by constructing and executing a data-science pipeline on a real dataset. This demonstration further highlights how Texera’s stepwise, visual execution can be aligned with explicit learning objectives to support hands-on teaching of data-science concepts in the classroom. Bursty heterogeneous workloads. Classroom settings pose challenges that conventional data science tools are not designed to address. Aligning execution environments at scale is difficult in education. Local setups force students to manage their own environments, leading to fragmented configurations and inconsistent behavior that divert class time to troubleshooting. Other existing cloud-based alternatives centralize environments but introduce friction through provisioning, authentication, access control, quota enforcement, and cost governance. These mechanisms assume stable, individualized usage patterns, which do not hold in classrooms, where activity frequently alternates between long idle periods and short, highly synchronized bursts during lectures, labs, or assignment deadlines. These challenges motivate the design of Apache Texera1 [13]— an open-source system that supports collaborative data science and AI/ML using GUI-based workflows (as shown in Figure 1). We organized a series of hands-on data science and AI/ML programs called Data Science For All (DS4ALL) using Texera [12] (see Table 1) to teach undergraduate and graduate courses, community college workshops, and summer programs, where dozens of students learn data science using workflows. In this paper, we demonstrate how Texera is used to teach introductory data science and describe the system design decisions that support classroom-teaching activities.