In this course, students will take their coding skills and understanding of AI and machine learning model building into real-world contexts. While other courses focus on technical skills in data science methods, tools, and programming, this course builds industry-level readiness through an engineering mindset. Students will apply professional standards of reliability and reproducibility using modern tools and workflows common in real-world team environments.
Through guided, step-by-step labs, students will learn and apply automated engineering workflows using Python packages and tools to implement unit testing, validate data transformations, and test model pipelines—core practices for ensuring quality in ML systems. Students will leverage the command-line interface, develop practices for working with virtual environments, and apply modern software development techniques to write maintainable, production-ready code.
Number of Units:1.0 graduate-level extension credit(s) in semester hours
Who Should Attend: This course is tailored for individuals who have a baseline of technical knowledge but want to bridge the gap between academic or basic coding and professional engineering. This includes aspiring ML engineers, data scientists, software engineers transitioning to AI/ML, computer science or data science students, and self-taught programmers.
Technical Requirements
Professional development courses offered by the University of San Diego’s Division of Professional & Continuing Education are taught by faculty that possess a depth and breadth of academic and real-world professional experience.
The Professional and Continuing Education program nurtures key partnerships on the local, national, and international level. The goal is to better serve working professionals who seek to enhance or build their careers and help achieve their highest value and potential. Contact us today to learn more.
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Courses offer a convenient, yet rigorous style of learning that allows you to structure your education to suit your schedule while keeping you on pace toward achieving your educational goals.
Our online self-paced courses are designed to provide flexibility and autonomy for learners. Unlike traditional classroom methods, which require attendance at scheduled times, self-paced courses allow you to progress through the material at your own speed within a six-month period from the time of enrollment. Your instructor will provide feedback via written responses on your assignments and exams. Grades are based on completed projects, assignments and exams.
Key differences from traditional classroom methods:
Structure of Online Self-Paced Courses:
The content is divided into learning modules, each covering one or more topics. Within each module, you can expect the following components:
Typically, there is a final project, paper, or exam due in the last module that synthesizes all the topics covered throughout the course. The design of the learning modules follows a consistent rhythm to help you manage your time effectively. Grades will be available within two weeks of final submission, transcripts will be automatically mailed out following grades being posted and approved. Please allow 3-4 weeks from when final grades are posted to receive a transcript.
The Guide to Choosing the Right AI Business Certificate walks through the different types of AI programs, common mistakes to avoid, and how to choose based on outcomes, not hype.