Data Science, MS

Program Learning Outcomes

Students will:

• Possess a theoretical understanding of classical statistical models (e.g., generalized linear models, linear time series models, etc.), as well as the ability to apply those models effectively
• Possess a theoretical understanding of machine learning techniques (e.g., random forests, neutral networks, naive Bayes, k-means, etc.), as well as the ability to apply those techniques effectively
• Effectively use modern programming languages (e.g., R, Python, SQL, etc.) and technologies (AWS, Hive, Spark, Hadoop, etc.) to scrape, clean, organize, query, summarize, visualize, and model large volumes and varieties of data
• Prepare for careers as data scientists by solving real-world, data-driven, business problems with other data scientists, and understand the social, ethical, legal, and policy issues that increasingly challenge and confront data scientists
• Develop professional communication skills (e.g., presentations, interviews, email etiquette, etc.), and begin integrating with the Bay Area data science community

Major Requirements (35 units)