Data science: applications and case studies
Abstract
Data science is the activity of analysing vast amounts of unorganised and organised raw data to find patterns and draw conclusions that can be put to use. Data science is an interdisciplinary field, and the foundations include, inference, computer science, predictive analytics, the creation of machine learning techniques, and new tools for extracting information from large data sets. Data science is still in its beginning stages of development, but it is already producing professionals with specific and relevant skills that set them apart from those in the computing, digital, and statistical sciences.
Keywords:
Data science, statistics.
References
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2. Wing, Jeannette M. "The data life cycle." Harvard Data Science Review 1.1 (2019): 6.
3. Baesens, Bart. Analytics in a big data world: The essential guide to data science and its applications. John Wiley & Sons, 2014.
4. Hox, Joop J., and Hennie R. Boeije. "Data collection, primary versus secondary." (2005): 593-599.
5. Idreos, Stratos, Olga Papaemmanouil, and Surajit Chaudhuri. "Overview of data exploration techniques." Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 2015.
6. Wang, Yichuan, et al. "An integrated big data analytics-enabled transformation model: Application to health care." Information & Management 55.1 (2018): 64-79.
7. Bates, David W., et al. "Big data in health care: using analytics to identify and manage high-risk and high-cost patients." Health affairs 33.7 (2014): 1123-1131.
8. Murdoch, Travis B., and Allan S. Detsky. "The inevitable application of big data to health care." Jama 309.13 (2013): 1351-1352.
9. Manyika, James, et al. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, 2011.
10. Hripcsak, George, et al. "Observational Health Data Sciences and Informatics (OHDSI): opportunities for observational researchers." MEDINFO 2015: eHealth-enabled Health. IOS Press, 2015. 574-578.
11. Hripcsak, George, et al. "Drawing reproducible conclusions from observational clinical data with OHDSI." Yearbook of medical informatics 30.01 (2021): 283-289.
12. Neubert, Sebastian, et al. "Multi-sensor-fusion approach for a data-science-oriented preventive health management system: concept and development of a decentralized data collection approach for heterogeneous data sources." International Journal of Telemedicine and Applications 2019 (2019).
13. Kaur, Parneet, Manpreet Singh, and Gurpreet Singh Josan. "Classification and prediction based data mining algorithms to predict slow learners in education sector." Procedia Computer Science 57 (2015): 500-508.
14. Ali, Mohd Maqsood. "Role of data mining in education sector." International Journal of Computer Science and Mobile Computing 2.4 (2013): 374-383.
15. Hassani, Hossein, et al. "Deep learning and implementations in banking." Annals of Data Science 7.3 (2020): 433-446.
16. Kou, Gang, et al. "Machine learning methods for systemic risk analysis in financial sectors." Technological and Economic Development of Economy 25.5 (2019): 716-742.
17. Kou, Gang, et al. "Machine learning methods for systemic risk analysis in financial sectors." Technological and Economic Development of Economy 25.5 (2019): 716-742.
18. Bangert, Patrick, ed. Machine learning and data science in the oil and gas industry: Best practices, tools, and case studies. Gulf Professional Publishing, 2021.
19. Sayers, Eric W., et al. "Database resources of the national center for biotechnology information." Nucleic acids research 49.D1 (2021): D10.
20. Saputro, Pujo Hari, and Herlino Nanang. "Exploratory data analysis & booking cancelation prediction on hotel booking demands datasets." Journal of Applied Data Sciences 2.1 (2021): 40-56.
Published
31/08/2022
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How to Cite
M, R., & K, V. M. V. K. (2022). Data science: applications and case studies. The Journal of Multidisciplinary Research, 2(2), 24-29. https://doi.org/10.37022/tjmdr.v2i2.396
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