DBA-831-O500 - Topic 2 DQ 1

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Grand Canyon University *

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831

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Business

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Jan 9, 2024

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docx

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Uploaded by dallasmwilliamsiii

It is very critical for companies to be aligned with Exploratory Data Analysis (EDA) in the objectives for their business. EDA will assist with the understanding of characteristics of data. This will include the distribution, variance, and where there is correlation. EDA will be an important part of how predictions are made as well as informed decisions (Wang et al., 2022). Patterns and relationships are also identified through EDA, which will lead to better strategies being put in place that will produce good outcomes (Xie et al., 2023). The quality of the data will also be verified by EDA. This will reduce the risk of inconsistencies in the data that could potentially skew results of the analysis. When there is a good model selected, the structure of the data is understood, and businesses can ensure that they choose the most appropriate machine learning models for the prediction that is needed (Xie et al., 2023). EDA also helps with mitigating risks early in the analysis process. EDA will be a vital step in providing a good source of guidance for all companies that utilize the analysis. Wang, C., Ma, H., Chen, G., & Hartmann, S. (2022). Memetic EDA-Based Approaches to QoS-Aware Fully Automated Semantic Web Service Composition. IEEE Transactions on Evolutionary Computation, Evolutionary Computation, IEEE Transactions on, IEEE Trans. Evol. Computat, 26 (3), 570–584. https://doi-org.lopes.idm.oclc.org/10.1109/TEVC.2021.3127633 Xie, Z., Pan, J., Chang, C., Hu, J., & Chen, Y. (2023). The Dark Side: Security and Reliability Concerns in Machine Learning for EDA. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on, IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst, 42 (4), 1171–1184. https://doi-org.lopes.idm.oclc.org/10.1109/TCAD.2022.3199172

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DBA-831-O500 - Topic 2 DQ 1

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