Mathematical Data Science with Applications in Business, Industry, and Medicine
Mathematical data science is a field that combines mathematical techniques with data science methods to extract insights and knowledge from data. It involves working with data at all stages of the data lifecycle, from collection and storage to cleansing and processing, the analysis and visualization...
Guardat en:
| Format: | Online |
|---|---|
| Idioma: | anglès |
| Publicat: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
|
| Matèries: | |
| Accés en línia: | ONIX_20250220_9783725827411_410 |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
| _version_ | 1869520548984258560 |
|---|---|
| collection | Directory of Open Access Books |
| description | Mathematical data science is a field that combines mathematical techniques with data science methods to extract insights and knowledge from data. It involves working with data at all stages of the data lifecycle, from collection and storage to cleansing and processing, the analysis and visualization of data, and the communication of the results and findings. Data scientists use a variety of tools and techniques to analyze data, including mathematical concepts and models, artificial intelligence techniques, machine learning algorithms, statistical analysis, and data visualization. Furthermore, data science can be used to make predictions, identify patterns, and draw conclusions from data, and it is applied in a variety of areas, including business, industry, and medicine. It is a rapidly evolving field, and data scientists are expected to stay up to date with new tools, techniques, and technologies. This Reprint is a collection of articles on a wide range of topics in the field of mathematical data science, with applications in business, industry, and medicine. The proposed methods and concepts are discussed in detail and illustrated with several real-life data examples. |
| format | Online |
| id | doab-20.500.12854ir-153046 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1530462025-02-20T13:28:41Z Mathematical Data Science with Applications in Business, Industry, and Medicine Johannssen, Arne Chukhrova, Nataliya artificial intelligence big data analytics computational statistics data science deep learning hypothesis testing machine learning probability distributions reinforcement learning statistical data analysis thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Mathematical data science is a field that combines mathematical techniques with data science methods to extract insights and knowledge from data. It involves working with data at all stages of the data lifecycle, from collection and storage to cleansing and processing, the analysis and visualization of data, and the communication of the results and findings. Data scientists use a variety of tools and techniques to analyze data, including mathematical concepts and models, artificial intelligence techniques, machine learning algorithms, statistical analysis, and data visualization. Furthermore, data science can be used to make predictions, identify patterns, and draw conclusions from data, and it is applied in a variety of areas, including business, industry, and medicine. It is a rapidly evolving field, and data scientists are expected to stay up to date with new tools, techniques, and technologies. This Reprint is a collection of articles on a wide range of topics in the field of mathematical data science, with applications in business, industry, and medicine. The proposed methods and concepts are discussed in detail and illustrated with several real-life data examples. 2025-02-20T13:28:39Z 2025-02-20T13:28:39Z 2024 book ONIX_20250220_9783725827411_410 9783725827411 9783725827428 https://directory.doabooks.org/handle/20.500.12854/153046 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/10275 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2742-8 10.3390/books978-3-7258-2742-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725827411 9783725827428 242 Basel open access |
| spellingShingle | artificial intelligence big data analytics computational statistics data science deep learning hypothesis testing machine learning probability distributions reinforcement learning statistical data analysis thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science Mathematical Data Science with Applications in Business, Industry, and Medicine |
| title | Mathematical Data Science with Applications in Business, Industry, and Medicine |
| title_full | Mathematical Data Science with Applications in Business, Industry, and Medicine |
| title_fullStr | Mathematical Data Science with Applications in Business, Industry, and Medicine |
| title_full_unstemmed | Mathematical Data Science with Applications in Business, Industry, and Medicine |
| title_short | Mathematical Data Science with Applications in Business, Industry, and Medicine |
| title_sort | mathematical data science with applications in business industry and medicine |
| topic | artificial intelligence big data analytics computational statistics data science deep learning hypothesis testing machine learning probability distributions reinforcement learning statistical data analysis thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| topic_facet | artificial intelligence big data analytics computational statistics data science deep learning hypothesis testing machine learning probability distributions reinforcement learning statistical data analysis thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science |
| url | ONIX_20250220_9783725827411_410 |