Data Science, MS

Program Learning Outcomes

Students will:

• possess a solid theoretical understanding of - as well as ability to apply - classical statistical models (generalized linear models, linear time series models, etc.), machine learning techniques (boosting, random forests, neural networks, etc.), and classification techniques (naive Bayes, k-means, spectral clustering, etc.)
• effectively use modern programming languages (R, Python, SQL, etc.) to scrape, clean, organize, query, summarize, visualize, and model large varieties and volumes of data
• be prepared for careers as analytics professionals by integrating with the Bay Area data science community, solving real-world data-driven business problems with members of this community, developing professional presentation and interviewing skills, and obtaining some domain expertise

Major Requirements (35 units)