TY - GEN
T1 - Data Makes Better Data Scientists
AU - Zhao, Jinjin
AU - Gal, Avigdor
AU - Krishnan, Sanjay
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/6/18
Y1 - 2023/6/18
N2 - With the goal of identifying common practices in data science projects, this paper proposes a framework for logging and understanding incremental code executions in Jupyter notebooks. This framework aims to allow reasoning about how insights are generated in data science and extract key observations into best data science practices in the wild. In this paper, we show an early prototype of this framework and ran an experiment to log a machine learning project for 25 undergraduate students.
AB - With the goal of identifying common practices in data science projects, this paper proposes a framework for logging and understanding incremental code executions in Jupyter notebooks. This framework aims to allow reasoning about how insights are generated in data science and extract key observations into best data science practices in the wild. In this paper, we show an early prototype of this framework and ran an experiment to log a machine learning project for 25 undergraduate students.
UR - http://www.scopus.com/inward/record.url?scp=85167947466&partnerID=8YFLogxK
U2 - 10.1145/3597465.3605228
DO - 10.1145/3597465.3605228
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AN - SCOPUS:85167947466
T3 - HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023
BT - HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023
T2 - 2023 Workshop on Human-In-the-Loop Data Analytics, HILDA 2023 - Co-located with SIGMOD 2023
Y2 - 18 June 2023
ER -