Data Makes Better Data Scientists

Jinjin Zhao; Avigdor Gal; Sanjay Krishnan

doi:10.1145/3597465.3605228

Data Makes Better Data Scientists

Jinjin Zhao, Avigdor Gal, Sanjay Krishnan

Data and Decision Sciences

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023
ISBN (Electronic)	9798400702167
DOIs	https://doi.org/10.1145/3597465.3605228
State	Published - 18 Jun 2023
Event	2023 Workshop on Human-In-the-Loop Data Analytics, HILDA 2023 - Co-located with SIGMOD 2023 - Seattle, United States Duration: 18 Jun 2023 → …

Publication series

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

Conference

Conference	2023 Workshop on Human-In-the-Loop Data Analytics, HILDA 2023 - Co-located with SIGMOD 2023
Country/Territory	United States
City	Seattle
Period	18/06/23 → …

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Access to Document

10.1145/3597465.3605228

Cite this

@inproceedings{2248668fcc5c4993988464236e34740c,

title = "Data Makes Better Data Scientists",

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.",

author = "Jinjin Zhao and Avigdor Gal and Sanjay Krishnan",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 2023 Workshop on Human-In-the-Loop Data Analytics, HILDA 2023 - Co-located with SIGMOD 2023 ; Conference date: 18-06-2023",

year = "2023",

month = jun,

day = "18",

doi = "10.1145/3597465.3605228",

language = "الإنجليزيّة",

series = "HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023",

booktitle = "HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023",

}

Zhao, J, Gal, A & Krishnan, S 2023, Data Makes Better Data Scientists. in HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023., 12, HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023, 2023 Workshop on Human-In-the-Loop Data Analytics, HILDA 2023 - Co-located with SIGMOD 2023, Seattle, United States, 18/06/23. https://doi.org/10.1145/3597465.3605228

Data Makes Better Data Scientists. / Zhao, Jinjin; Gal, Avigdor; Krishnan, Sanjay.
HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023. 2023. 12 (HILDA 2023 - Workshop on Human-In-the-Loop Data Analytics - Co-located with SIGMOD 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Zhao, Jinjin

AU - Gal, Avigdor

AU - Krishnan, Sanjay

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.

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DO - 10.1145/3597465.3605228

M3 - منشور من مؤتمر

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 -

Data Makes Better Data Scientists

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