2021/02/13 by Ani Adhikari, Adhikari, Ani, John DeNero +3
Computer Science · Decision Sciences · Engineering · Mathematics · Psychology · #Computational Physics and Python Applications #Computational thinking #Computer science #Computers and Society (cs.CY) #Curriculum #Engineering #Engineering ethics #FOS: Computer and information sciences #Interleaving #Mathematics education #Medical education #Pedagogy #Psychology #Scale (ratio) #Scientific Computing and Data Management #Sociology #Statistics Education and Methodologies #Undergraduate education #Undergraduate research #cs.CY
paper · pdf · doi:10.48550/arxiv.2102.09391
openalex publication_date 2021/02/13 · arxiv created 2021/03/17 · arxiv updated 2021/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and working on real-world problems. We believe that interleaving these elements within our core courses is essential to preparing students to engage in data-driven inquiry at the scale that contemporary scientific and industrial applications demand. This new curriculum is already reshaping the undergraduate experience at Berkeley, where these courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.