Menguak potensi data science

Transformasi di dunia teknik informatika

  • Muhammad Ali Nur Ardhi Program Studi Teknik Informatika Universitas Islam Negeri Maulana Malik Ibrahim Malang
Keywords: Data science, informatics, engineering data analysis, big data, machine learning, higher education

Abstract

Data Science has become an essential component in the field of Informatics Engineering, significantly contributing to big data analysis and data-driven decision-making. By utilizing mathematical, statistical, and programming techniques, Data Science enables the processing of complex data to uncover valuable patterns and trends. This article discusses the importance of Data Science in Informatics Engineering, its benefits, and the challenges faced in its implementation. Additionally, the article explores various practical applications of Data Science relevant to the needs of the industry and education in Informatics Engineering.

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References

Berman, F. (2018). Data science and the digital humanities. Communications of the ACM, 61(8), 66-75.

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.

Davenport, T. H., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70-76.

Dean, J. (2014). Big data, data mining, and machine learning: Value creation for business leaders and practitioners. John Wiley & Sons.

Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

Gros, B. (2016). The design of smart educational environments. Smart Learning Environments, 3(1), 1-11.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.

Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep Patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6, 26094.

Piatetsky-Shapiro, G., & Frawley, W. J. (1991). Knowledge discovery in databases. AAAI/MIT Press.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.

Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3.

Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach. Malaysia; Pearson Education Limited,.

Vogelstein, B. (2013). Cancer genomics: New discoveries and opportunities. N Engl J Med, 368(17), 1561-1571.

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Published
2024-06-30
How to Cite
Ardhi, M. (2024). Menguak potensi data science. Maliki Interdisciplinary Journal, 2(6), 1084-1088. Retrieved from https://urj.uin-malang.ac.id/index.php/mij/article/view/8758
Section
Articles