Work Faster with Practical Gen AI for Data Engineering | For all Data Professionals (Engineers, Analysts, Scientists)
InstructorHenry HabibInstruction includes live debugging and shows model outputs at the start of sections to provide immediate context.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This screen capture shows a developer environment running Python code for a Flask web application that processes database queries, illustrating the practical implementation steps.
This screen capture shows a raw employment agreement opened in a text editor, illustrating the unstructured document format that serves as input for data extraction workflows.
This Jupyter Notebook screenshot illustrates the data normalization process, showing a DataFrame with inconsistent formats that need standardization.
The curriculum explores various AI tools including ChatGPT, Claude, Custom GPTs, and platform-specific assistants like Copilot for Azure Data Factory.
Some approaches rely on direct data augmentation via LLMs, which may lead to unstable results compared to using AI to generate consistent code.
The content may feel like it only scratches the surface for experienced professionals, lacking advanced topics like security or agentic workflows.
The curriculum aligns with the target audience of data professionals, though feedback suggests it is more suitable for beginners than senior engineers.
