Applied Data, AI, and Robotics

This unit advances Python programming by teaching robust file and data handling, modular code organization, basic machine learning workflows, data visualization, and physical computing with sensors and actuators. Students will combine Jupyter-driven development, TensorFlow models, Matplotlib visualizations, and Raspberry Pi/Grove hardware control to build and test integrated projects.

Learning Objectives

• Analyze and resolve runtime errors by identifying specific exceptions, implementing targeted try/except handling, and applying defensive coding practices to keep applications stable
• Apply file and data-management techniques to read and iterate through files, load and dump JSON, and manipulate dictionary-based data for downstream analysis
• Demonstrate modular programming and reproducible development by creating/importing Python modules, authoring and running code in Jupyter Notebook, and writing functions that return and document values
• Evaluate and integrate basic machine learning and physical-computing components by building simple TensorFlow models, producing Matplotlib visualizations, exploring cloud/GPU deployment options, and controlling sensors and motors on Raspberry Pi hardware