This course welcomes students into the four-term, online program and introduces them to the faculty who will be teaching the courses in the program, the technologies used to deliver the on-line experience, the structure used throughout all of the courses in the program, as well as the common expectations across the courses.

This module acknowledges that students enter the program with diverse programming and engineering backgrounds. It provides an intentional, structured on-ramp to level foundational skills before EX 601: Software Engineering Fundamentals. Through hands-on work with professional tools—Git, IDEs, debugging, testing, build systems, and the fundamentals of data analytics — students gain the practical fluency required for success in the Software Engineering curriculum.

This course introduces foundational principles of modern software engineering, preparing students to design, build, and maintain reliable software systems. Emphasis is on disciplined development practices, automated quality assurance, and managing the software lifecycle from conception through deployment. Working in teams, students will build a software project across the term, applying object-oriented design, refactoring, version control and collaboration, and testing, while learning to judge where AI assistants genuinely help and where they introduce error, building durable engineering habits that support all later modules.

This course orients learners to navigate a programming environment setup and tools like the filesystem, command line, and notebooks. Also includes a review of fundamental components of the Python programming language, including data structures (scalars, vectors, arrays, dictionaries, etc.), installing and importing packages, control flow, loops, and functions.

This 2-term, 3-unit module, will focus on the application of algorithmic thinking to large data sets. It will include a lab component which will focus on hands-on experience with manipulation of large data sets. It will begin by establishing a strong SQL foundation around data retrieval, manipulation, filtering, and aggregation. Once established, it will focus on algorithms for efficient handling of data – both in SQL and outside, at the application layer.

This course explores essential concepts and techniques in machine learning, including key topics such as linear methods (e.g., linear regression, lasso, ridge), tree methods (decision trees, random forests, boosting), and unsupervised methods like clustering. Learners will gain proficiency in fitting various machine learning models, understanding regularization to prevent overfitting, and cross-validation to evaluate model performance and tune hyper-parameters.

This course explores the challenges of building and scaling large-scale software systems. Students learn how architectural choices, concurrency models, and deployment strategies affect reliability, scalability, and evolution of code bases. Students will grasp the benefits of building modular systems and the strategies that will guide them towards the most modularity and fault isolation.

This course explores how AI and large language models (LLMs) are transforming software engineering practices. Students will learn not only how to use AI tools to boost productivity, but also how to critically assess the reliability, security, and maintainability of AI-generated code. The course blends hands-on experimentation with theoretical grounding in the evolving role of human engineers in an AI-augmented development workflow.

Emphasis is placed on understanding how AI fits into testing, code review, deployment, and observability practices across both greenfield and legacy systems. This class embraces the idea that AI will not eliminate software engineering jobs, however engineers who do not boost their own productivity with AI tools may struggle to compete with those who do.

It is crucial that students have internalized the concepts from EX 601: Software Engineering Fundamentals prior to taking this class. This will allow them to navigate AI/LLM generated feedback critically and ensure that while parts of the code itself might be AI generated, fundamental software engineering principles are still being adhered to.

This course examines how to design, build, and evaluate systems where humans and AI work together. Students apply user-centered design principles to build interfaces that incorporate AI, and learn to reason about the human factors that shape those interactions—trust, mental models, and how users interpret uncertainty. Alongside design practice, the course takes a critical view of responsibility: studying usability, explainability, and the UX and architectural choices that keep AI systems responsive, while learning to anticipate failures like overreliance, opacity, and algorithmic bias. Graduates are prepared to build usable, transparent interfaces that integrate machine learning responsibly, designed to keep humans meaningfully in control.

Data Design & Distribution at Scale, AI/ML OPS, but this course will go much deeper into scalability challenges of managing large volumes of data, including the evolution of NoSQL systems, design of ETL pipelines, managing arrival of new data via streaming and batch pipelines, and general AI/ML Ops.

This course explores ethical considerations inherent in data-driven decision-making and the deployment of algorithmic systems. Emphasizes the socially constructed nature of science, shedding light on biases and inequalities. Learners explore the societal and cultural implications of AI and machine learning technologies, analyzing potential biases and disparities in data and algorithms. Practical skills are developed through the application of fairness metrics to evaluate and mitigate bias in algorithmic decision-making. Privacy, ethical considerations surrounding personal data, and navigating key regulatory frameworks related to AI and ML, such as GDPR, California Consumer Privacy Act, and other data privacy laws, are integral components.

The Capstone project spans the last two terms. The goal of the project is to expose students to the challenges of building an end-to-end software application that leverages AI and big data at scale, while exposing its functionality to users in an intuitive way.

A central focus of this project is to ensure that students appreciate the seams between utilizing AI models, traditional software engineering, and Cloud DevOps. The capstone project will emphasize building a system that is reliable, correct, and performant. Engineers capable of navigating this seam are lacking in industry as there is a bit of a divide between AI/ML experts who are focused on tuning models and software system engineers who are focused on the stability and correctness of the architecture. The ability to integrate and utilize models, reliably and correctly, without sacrificing system stability is a valuable skill that the capstone will hone in on. By the end of the project, students will have built and deployed an AI-enabled application that exemplifies the integration of scalable software architecture, responsible AI, and human-centered design.