Data Science and Artificial Intelligence, MS

Program Learning Outcomes

Students will:

• Develop a theoretical understanding of statistical models and methods, including probability theory, hypothesis testing, uncertainty quantification, generalized linear models, experimental design techniques (e.g., A/B testing), etc., to analyze and apply them to draw valid inferences and support evidence-based decision-making in complex data contexts.
• Demonstrate an understanding of core machine learning, deep learning, and AI methods, including supervised and unsupervised learning, neural networks, and generative models. Apply knowledge of theoretical foundations, practical applications, and limitations to analyze structured and unstructured data problems. 
• Apply modern programming languages and computing technologies to design, implement, and evaluate data science and AI workflows that scrape, clean, organize, query, visualize, and model large, complex, and heterogeneous datasets at scale. Develop expertise in computing technologies, including, but not limited to, Python, SQL, NoSQL, Spark, Airflow, Docker, PyTorch, MLFlow, and cloud platforms such as GCP and AWS.
• Demonstrate the ability to work effectively in collaborative, project-based settings to address practical data science and AI problems, incorporating ethical reasoning and consideration of legal, societal, and security issues into problem-solving, model design, and deployment. 
• Demonstrate workforce readiness by communicating complex data science and AI concepts clearly and effectively to diverse audiences through professional writing, presentations, and other forms of technical and non-technical communication. Build a professional presence by engaging with industry and academic communities in the Bay Area and beyond.

Major Requirements (35 units)