A seminar-style course with lectures on a range of computational topics important for modern data-intensive science, jointly supported by the Statistics department and Stanford Data Science, and suitable for advanced undergraduate/graduate students engaged in either research on data science techniques (statistical or computational, for example) or research in scientific fields relying on advanced data science to achieve its goals. Seminars will alternate a presentation of a topic, usually by an expert on that topic, typically leading to exercises applying the techniques, with a followup lecture to further discuss the topic and the exercises.
Prerequisites: Understanding of basic modern data science and competence in related programming, e.g., in R or Python.
We have an exciting line up of topics and speakers: see Schedule for more information.
If STATS/BIODS 352 interests you, enroll using Axess.