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International Journal of Data Science and Analytics

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06-10-2023 | Editorial

Theoretical and practical data science and analytics: challenges and solutions

Authors: Carson K. Leung, Gabriella Pasi, Li Wang

Published in: International Journal of Data Science and Analytics | Issue 4/2023

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Abstract

Big data have become a core technology for providing innovative solutions in numerical applications and services in many fields. Embedded in these big data is valuable information and knowledge. This calls for data science and analytics, which has emerged as an important paradigm for driving the new economy and domains (e.g., Internet of Things, social and mobile networks, cloud computing), reforming classic disciplines (e.g., telecommunications, biology, health and social science), as well as upgrading core business and economic activity. In this article, we focus on both theoretical and practical data science and analytics. We summarize and highlight some of its challenges and solutions, which are covered in the eight articles in the current Special Issue on "theoretical and practical data science and analytics."

next article Establishing FAIR (Findable, Accessible, Interoperable and Reusable) principles for estuarine organisms exposed to engineered nanomaterials

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Özsu, M.T.: Data science - a systematic treatment. Commun. ACM 66(7), 106–116 (2023)CrossRef

Cao, L.: Data science: challenges and directions. Commun. ACM 60(8), 59–68 (2017)CrossRef

Cao, L.: Data science: a comprehensive overview. ACM Comput. Surv. 50(3), 43 (2017)

Dubey, M., Palakkadavath, R., Srijith, P.K.: Bayesian neural Hawkes process for event uncertainty prediction. Int. J. Data Sci. Anal. (2023). https://doi.org/10.1007/s41060-023-00443-3CrossRef

Hernández, N., Muñoz, A., Martos, G.: Density kernel depth for outlier detection in functional data. Int. J. Data Sci. Anal. (2023). https://doi.org/10.1007/s41060-023-00420-wCrossRef

Villanueva Zacarias, A.G., Reimann, P., Weber, C., Mitschang, B.: AssistML: an approach to manage, recommend and reuse ML solutions. Int. J. Data Sci. Anal. (2023). https://doi.org/10.1007/s41060-023-00417-5CrossRef

Sorvisto, D.: Applications of the discrete-time Fourier transform to data analysis. Int. J. Data Sci. Anal. (2023). https://doi.org/10.1007/s41060-023-00409-5CrossRef

Hael, M.A.: Unbiased recursive decision tree for supervised functional data classification with applying on electrocardiogram signals. Int. J. Data Sci. Anal. (2023). https://doi.org/10.1007/s41060-023-00410-yCrossRef

10.

Ahmed, N., Schmidt-Thieme, L.: Sparse self-attention guided generative adversarial networks for time-series generation. Int. J. Data Sci. Anal. (2023). https://doi.org/10.1007/s41060-023-00416-6CrossRef

11.

Bloemheuvel, S., van den Hoogen, J., Jozinović, D., Michelini, A., Atzmueller, M.: Graph neural networks for multivariate time series regression with application to seismic data. Int. J. Data Sci. Anal. 16, 317–332 (2023)CrossRef

12.

Xiao, C., Hong, S., Huang, W.: Optimizing graph layout by t-SNE perplexity estimation. Int. J. Data Sci. Anal. 15, 159–171 (2023)CrossRef

Title: Theoretical and practical data science and analytics: challenges and solutions
Authors: Carson K. Leung
Gabriella Pasi
Li Wang
Publication date: 06-10-2023
Publisher: Springer International Publishing
Published in: International Journal of Data Science and Analytics / Issue 4/2023
Print ISSN: 2364-415X
Electronic ISSN: 2364-4168
DOI: https://doi.org/10.1007/s41060-023-00465-x

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Other articles of this Issue 4/2023

AssistML: an approach to manage, recommend and reuse ML solutions

Sparse self-attention guided generative adversarial networks for time-series generation

Unbiased recursive decision tree for supervised functional data classification with applying on electrocardiogram signals

Establishing FAIR (Findable, Accessible, Interoperable and Reusable) principles for estuarine organisms exposed to engineered nanomaterials

Applications of the discrete-time Fourier transform to data analysis

Density kernel depth for outlier detection in functional data

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