mlsanityData to 7 Oct 2026

Automating Data Science: Prospects and Challenges

arXiv 2105.05699 ↗First posted 12 May 2021cs.DB cs.LGcited by 17

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Abstract (arXiv)

Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights: * Automation in data science aims to facilitate and transform the work of data scientists, not to replace them. * Important parts of data science are already being automated, especially in the modeling stages, where techniques such as automated machine learning (AutoML) are gaining traction. * Other aspects are harder to automate, not only because of technological challenges, but because open-ended and context-dependent tasks require human interaction.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 10 of its 63 references that are in the map. First posted on arXiv 12 May 2021.

Builds on

10 of 63 references.

References · 10

  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  2. 2014OpenML: networked science in machine learningcited by 523
  3. 2017Progressive Neural Architecture Searchcited by 483
  4. 2016Data Programming: Creating Large Training Sets, Quicklycited by 246
  5. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  6. 2014Automatic Construction and Natural-Language Description of Nonparametric Regression Modelscited by 40
  7. 2020Data Engineering for Data Analytics: A Classification of the Issues, and Case Studiescited by 10
  8. 2019Automated Machine Learning with Monte-Carlo Tree Searchcited by 8
  9. 2018ColNet: Embedding the Semantics of Web Tables for Column Type Predictioncited by 4
  10. 2015BayesDB: A probabilistic programming system for querying the probable implications of datacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 7; 2025: 3; 2026: 3.

17 papers.

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  1. 2024Data Interpreter: An LLM Agent For Data Sciencecited by 222
  2. 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineeringcited by 125
  3. 2024DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoningcited by 119
  4. 2025DeepAnalyze: Agentic Large Language Models for Autonomous Data Sciencecited by 49
  5. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
  6. 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
  7. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  8. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  9. 2025Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agentscited by 4
  10. 2026EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Managementcited by 2
  11. 2024A Survey of Pipeline Tools for Data Engineeringcited by 1
  12. 2023Machine learning's own Industrial Revolutioncited by 0
5 more
  1. 2024Using ChatGPT for Data Science Analysescited by 0
  2. 2024Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphscited by 0
  3. 2025Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discoverycited by 0
  4. 2026Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analyticscited by 0
  5. 2026Fail Loudly: An Auditable Runtime for Agentic Data Analysiscited by 0