Jupyter notebooks have become a staple in data science and machine learning workflows, offering an interactive environment where code, visualizations, and narrative text coexist in a single document. Their ability to execute code cell by cell allows users to experiment rapidly, tweak parameters, and observe results in real time, which is especially valuable when exploring new libraries or debugging data pipelines. Because notebooks can embed Markdown, LaTeX, and even interactive widgets, they serve as a convenient medium for documenting projects and sharing reproducible analyses with collaborators. The web‑based interface and open‑source nature of Jupyter also make it accessible to learners who may not yet be comfortable with command‑line tools.
However, when it comes to teaching application programming, the very features that make notebooks appealing can also become obstacles. The cell‑based structure encourages a fragmented view of code that does not reflect the linear flow of a typical software project. Moreover, many tutorial authors exploit the flexibility of notebooks to intermix multiple code paths and explanatory text, which can obscure the logical progression of the lesson. These practices can lead to a learning experience that feels more like watching a demonstration than actively building a working application.
First, the division of code into discrete cells means that the notebook does not model the architecture of a real application, where modules, classes, and functions are organized into files and packages. Students may therefore miss the discipline of structuring code for maintainability and scalability.
Second, the temptation to embed alternative solutions or optional code snippets within the same notebook can create a maze of branches that is hard to navigate. When a learner tries to trace the execution path, the interleaved instructions can become confusing, especially if the notebook is long or poorly labeled.
Third, the interactive nature of notebooks can make it difficult to follow the overall flow of the program, because cells can be executed out of order or repeatedly, leaving the state of variables ambiguous. This non‑deterministic execution model contrasts with the linear, top‑to‑bottom execution of a script, which is the norm in production code.
Fourth, notebooks are not a deployable artifact; they lack the packaging, dependency management, and build steps that are essential for delivering software to users. Consequently, learners may finish a notebook without understanding how to translate their code into a runnable application or service.
Fifth, the environment in which a notebook runs can impose constraints on resource access. When hosted on a cloud platform or a shared server, the notebook may have limited file system permissions, restricted network access, or capped compute resources, which can hinder experimentation with databases or external APIs. These limitations can prevent students from fully exploring the end‑to‑end workflow they are studying.
Sixth, many learners prefer to build the application incrementally as they progress through a course, rather than simply watching a pre‑written notebook. Actively writing code reinforces concepts and helps students internalize the logic behind each step.
Seventh, having a working proof‑of‑concept that the learner has constructed themselves serves as a valuable reference for future projects. It also provides a tangible artifact that can be revisited, modified, or extended, which is harder to achieve when the code is only presented in a notebook.
Eighth, some courses present a polished notebook and then assess students with a test that requires recalling code they never wrote themselves. Without hands‑on practice, students may struggle to reproduce the logic from memory, leading to frustration and a superficial grasp of the material.
Historically, effective learning in programming involved walking through tutorials, writing code alongside the instructor, and then keeping the resulting scripts as a reference. This approach ensured that learners engaged with the full development cycle from design to implementation to debugging rather than merely observing.
Therefore, educators and content creators should reconsider the reliance on notebooks for teaching application development. Either revert to the traditional script‑based tutorials that emphasize code structure and deployment or devise new formats that combine the interactivity of notebooks with the discipline of a full application workflow. Such a shift would encourage deeper learning, better retention, and a stronger foundation for building real‑world software.
