One of the most frustating aspects of this course is that it effectively requires learning an entirely new programming language spread across multiple platforms at the same time, which creates a steep and unnecessary cognitive load. The lectures are structured in a way that leaves the learner largely on their own unless they resort to copying and pasting the provided exercises which I did in earlier lession but —an approach that defeats the educational purpose of AI prompting learning. A major conceptual sticking point is the use of data frames within database notebooks. A data frame is introduced as an object that appears identical to a Python DataFrame, yet in practice it is often a database-managed or database-backed object with different execution semantics. The frame is somewhere in the mist of your imagination. The course never clearly or concisely explains this distinction—what runs in Python, what runs in the database engine, and how the object is represented across contexts—which leads to confusion at precisely the point where conceptual clarity is most needed. For example the Chat bot did not understand how to query the current envirnoment but after multiple onerous querys it finally gave the the correct code to find the most regions that ordered the ski products ………..grouped_df.size().reset_index(name=‘counts’) but bot gave multiple wrong code looking for a .cvs file not understanding that the df was in the loaded with the info?