The Art and Practice of Data Science Pipelines: A Comprehensive Study of Data Science Pipelines In Theory, In-The-Small, and In-The-Large

Abstract

Increasingly larger number of software systems today are including data science components for descriptive, predictive, and prescriptive analytics. The collection of data science stages from acquisition, to cleaning/curation, to modeling, and so on are referred to as data science pipelines. To facilitate research and practice on data science pipelines, it is essential to understand their nature. What are the typical stages of a data science pipeline? How are they connected? Do the pipelines differ in the theoretical representations and that in the practice? Today we do not fully understand these architectural characteristics of data science pipelines. In this work, we present a three-pronged comprehensive study to answer this for the state-of-the-art, data science in-the-small, and data science in-the-large. Our study analyzes three datasets: a collection of 71 proposals for data science pipelines and related concepts in theory, a collection of over 105 implementations of curated data science pipelines from Kaggle competitions to understand data science in-the-small, and a collection of 21 mature data science projects from GitHub to understand data science in-the-large. Our study has led to three representations of data science pipelines that capture the essence of our subjects in theory, in-the-small, and in-the-large.

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Cited by in corpus (6)

• Towards Understanding Fairness and its Composition in Ensemble Machine Learning
• Manas: Mining Software Repositories to Assist AutoML
• Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML
• Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice
• Contextualized Data-Wrangling Code Generation in Computational Notebooks
• From Release to Adoption: Challenges in Reusing Pre-trained AI Models for Downstream Developers