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On the Appropriate Methodologies for Data Science Projects

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Authors

Data science is an emerging discipline with a particular research focus on improving the available techniques for data analysis. While the number of data science projects is growing, unfortunately, there is a slight consideration of how a team performs a data science project. Although the existence of a repeatable well-defined process could deal with many challenges of data science projects, researches conducted in recent years indicate a convergence of the results to agile methodologies as the appropriate ones for the projects. In this paper, first, the tasks and roles of individuals in data science projects are addressed; then, some research conducted for the methodologies used in the projects are studied. The study shows that agile methodologies could resolve many issues of data science projects by increasing the communications and cooperation of the team members and investors.

Original languageEnglish
Title of host publication2021 International Conference on Information Technology, ICIT 2021 - Proceedings
EditorsKhalid Mohammad Jaber
Number of pages7
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication date14.07.2021
Pages667-673
Article number9491712
ISBN (print)978-1-6654-2871-2
ISBN (electronic)978-1-6654-2870-5
DOIs
Publication statusPublished - 14.07.2021
EventInternational Conference on Information Technology - ICIT 2021 - Amman, Jordan
Duration: 14.07.2021 → 15.07.2021
Conference number: 10
http://icit.zuj.edu.jo/icit2021/Index.html

    Research areas

  • agile software methodologies, big data, business intelligence, data science
  • Engineering

Other publications by the same author(s)

Employing a Novel Metaheuristic Algorithm to Optimize an LSTM Model: A Case Study of Stock Market Prediction

Dastgerdi, A. K. & Mercorelli, P., 01.01.2023, Advances in Computing and Data Sciences: 7th International Conference, ICACDS 2023, Kolkata, India, April 27–28, 2023, Revised Selected Papers. Singh, M., Tyagi, V., Gupta, P. K., Flusser, J. & Ören, T. (eds.). Cham: Springer International Publishing, p. 478-489 12 p. (Communications in Computer and Information Science; vol. 1848).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Faulty Process Detection Using Machine Learning Techniques

Dastgerdi, A. K., Lange, D. & Mercorelli, P., 11.07.2023, Congress on Smart Computing Technologies : Proceedings of CSCT 2022. Bansal, J. C., Sharma, H. & Chakravorty, A. (eds.). Springer Singapore, p. 321-333 13 p. (Smart Innovation, Systems and Technologies).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Investigating the Effect of Noise Elimination on LSTM Models for Financial Markets Prediction Using Kalman Filter and Wavelet Transform

Dastgerdi, A. K. & Mercorelli, P., 2022, In: WSEAS Transactions on Business and Economics. 19, p. 432-441 10 p., 39.

Research output: Journal contributions › Journal articles › Research › peer-review

DOI

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