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Data Science Methodologies: Current Challenges and Future Approaches

  • Published:2021-05 Issue: Volume:24 Page:100183
  • ISSN:2214-5796
  • Container-title:Big Data Research
  • language:en
  • Short-container-title:Big Data Research

Author:

Martinez IñigoORCID,Viles ElisabethORCID,G. Olaizola IgorORCID

Publisher

Elsevier BV

Subject

Information Systems and Management,Computer Science Applications,Information Systems,Management Information Systems

Reference85 articles.

1. State of AI;Benaich,2019

2. VentureBeat, Why do 87% of data science projects never make it into production? 2019.

3. New Vantage, NewVantage Partners 2019 Big Data and AI Executive Survey (2019).

4. Gartner, Taking a First Step to Advanced Analytics.

5. Statistical modeling: the two cultures (with comments and a rejoinder by the author);Breiman;Stat. Sci.,2001

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1. MLOps in Data Science Projects: A Review;2023 IEEE International Conference on Big Data (BigData);2023-12-15

2. Data-Push Projects and their Unique Feature: Managing with Anomalies;2023 IEEE International Conference on Big Data (BigData);2023-12-15

3. The era of business analytics: identifying and ranking the differences between business intelligence and data science from practitioners’ perspective using the Delphi method;Journal of Business Analytics;2023-11-21

4. BDAS-EPM: An Integrated Evolution Process Model for Big Data Analytics Systems;Transactions on Computational Science and Computational Intelligence;2023-11-04

5. A Selective Conceptual Review of CRISP-DM and DDSL Development Methodologies for Big Data Analytics Systems;Transactions on Computational Science and Computational Intelligence;2023-11-04
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