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
- 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)
Foundation Courses (2 units)
Complete 2 units from the following:
Complete the following seminars:
10 hours of Interview Skills
10 hours of required interview skills training to be completed outside of class time. Trainings to be provided by the Data Science program and may include but are not limited to: workshops, mock interviews, resume editing and guest lecturers.