Data Science, MS

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The mission of our program is to produce graduates who possess a theoretical and practical understanding of many classical and modern statistical modeling and machine learning techniques; who use contemporary programming languages to scrape, clean, organize, query, summarize, visualize, and model large volumes and varieties of data; and who use their knowledge and skills to successfully solve real-world data-driven business problems and to communicate those solutions effectively.

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)

Linear Algebra Exam

All students must pass a linear algebra exam by the beginning of the Fall semester in order to demonstrate competency in this subject. Students have two attempts to pass this exam. Students are provided with ten hours of video resources as well as practice questions to aid them in their attempts.

Complete the following seminars:

10 hours of Interview Skills