Data Makes Better Data Scientists

Jinjin Zhao, Avigdor Gal, Sanjay Krishnan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationHILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023
ISBN (Electronic)9798400702167
DOIs
StatePublished - 18 Jun 2023
Event2023 Workshop on Human-In-the-Loop Data Analytics, HILDA 2023 - Co-located with SIGMOD 2023 - Seattle, United States
Duration: 18 Jun 2023 → …

Publication series

NameHILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023

Conference

Conference2023 Workshop on Human-In-the-Loop Data Analytics, HILDA 2023 - Co-located with SIGMOD 2023
Country/TerritoryUnited States
CitySeattle
Period18/06/23 → …

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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