Panel with Dragan Djuric and Kathi Fisler
summary
Course centers on data and ties tables to metadata, privacy, and inference, making intro computing feel current and broadly popular across campus. Curriculum argument: teach fundamentals and multiple languages rather than chasing a seven‑year hype cycle, because students go to many different careers. Data engineering gets overlooked because traditional topic sequences (variables then loops) don't align with it. Practical path: build a deep learning library from scratch with a companion numerical linear algebra reference, integrate with Intel/NVIDIA tensor backends, and iterate on real code.
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chapters (10)
- 0:00Introduction
- 0:26Data-centric computing curriculum
- 2:57University vs industry education
- 5:47Data engineering overlooked
- 7:53Functional programming for data science
- 8:35Cuban salsa dancing
- 11:03Sustainability of data science
- 14:08Machine learning learning path
- 22:56Programming as life skill
- 25:26Closing
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