PYTHON FOR APPLICATIONS IN ENGINEERING
Introduction to Python's concepts of objects and reference; classes and nested objects. Elements of object-oriented programming in Python. Container types: lists, dictionaries, and tuples. Installing Python packages and managing environments. Scientific computing with Numpy and SciPy. Applications of Python to Data Analysis. Applications of Python to Machine Learning. Applications of Python to embedded systems/robotics.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Perform computations typically encountered in Engineering (data reading/writing, solving linear systems, perform regressions, etc.) using Python scripts with hundreds to thousands of lines of code Audience: Graduate 2. Produce a software design solution that can be subsequently implemented in Python to solve a task/practical problem that draws on a computing component Audience: Graduate 3. Increase their productivity in tasks that require Python programming by taking advantages of 3rd party Python packages Audience: Graduate 4. Justify the rationale behind using Python in Engineering; explain how this programming language provides support of numerical analysis, statistical analysis, robotics, machine learning, data visualization, computer vision, and AI Audience: Graduate Learning Outcomes: 1. Perform computations typically encountered in Engineering (data reading/writing, solving linear systems, perform regressions, etc.) using Python scripts with hundreds to thousands of lines of code Audience: Graduate 2. Produce a software design solution that can be subsequently implemented in Python to solve a task/practical problem that draws on a computing component Audience: Graduate 3. Increase their productivity in tasks that require Python programming by taking advantages of 3rd party Python packages Audience: Graduate 4. Justify the rationale behind using Python in Engineering; explain how this programming language provides support of numerical analysis, statistical analysis, robotics, machine learning, data visualization, computer vision, and AI Audience: Graduate
Sources
- Course catalogue ↗2026-2027