The Software Engineering Bootcamp prepares incoming students for success by introducing or refreshing core content and tools, including GitHub, IDEs, debugging, and cloud workflows. All learners are required to complete the bootcamp two weeks prior to the start of the semester.

This year-long, 3-credit course focuses on the application of algorithmic thinking to large data sets, providing hands-on experience. It begins by establishing a strong SQL foundation around data retrieval, manipulation, filtering, and aggregation. Once established, it focuses on algorithms for efficient handling of data, both in SQL and outside SQL at the application layer.

The goal of the two-semester Capstone project is for students to understand the challenges of building an end-to-end software application that leverages AI and big data at scale. 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 emphasizes building a system that is reliable, correct, and performant. 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.

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.

Students work with realistic codebases and modern industry tooling to build durable engineering habits that support all later modules.

This module orients learners to programming environment setup and tools such as the filesystem, command line, and notebooks. It also reviews fundamental components of the Python programming language, including data structures, installing and importing packages, control flow, loops, and functions.

This module explores essential concepts and techniques in machine learning, including linear methods such as linear regression, lasso, and ridge; tree methods such as decision trees, random forests, and boosting; and unsupervised methods such as clustering.

Learners gain proficiency fitting machine learning models, understanding regularization to prevent overfitting, and using cross-validation to evaluate model performance and tune hyperparameters.

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 the evolution of codebases.

Students grasp the benefits of building modular systems and the strategies that guide them toward greater modularity and fault isolation.

This course explores how AI and large language models are transforming software engineering practices. Students 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, but engineers who do not boost their own productivity with AI tools may struggle to compete with those who do.

This course investigates the design and evaluation of systems where humans and AI interact. Students study usability, trust, explainability, and ethical considerations in human-AI collaboration, preparing them to build interfaces that integrate machine learning responsibly.

This course builds on Data and Algorithms for Scalable Systems and goes deeper into the scalability challenges of managing large volumes of data. Topics include the evolution of NoSQL systems, the design of ETL pipelines, managing the arrival of new data through streaming and batch pipelines, and general AI/ML Ops.

In this class, students address the key question: “How do I design data structures and systems that perform under real-world load, and organize data in a coherent manner?”

This module explores ethical considerations inherent in data-driven decision-making and the deployment of algorithmic systems. It 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 machine learning, such as GDPR, the California Consumer Privacy Act, and other data privacy laws, are integral components.