wk-7
1
Analytics
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Analytics
Descriptive analysis is a specific kind of research that interprets data in terms of statistical evidence. The study can give five a business a clear view of where they are financially and in the industry. This interpretation can also clearly be understood by stakeholders easily. Representation of data can be done through graphs, dashboards, charts, and reports (Kannan et al., 2019). For instance, when several patients are being admitted for a similar condition, and the numbers have increased in the last one month, descriptive analytics will give detailed statistical information on the situation and what a physician understands what they are working with.
Predictive analytics considers historical data of an event and puts them into a machine learning model that can learn the trend and patterns. The model and the trends observed can then be used to evaluate the possible outcome of an event (Putra & Khodra, 2018). This model is essential because it helps one understand the past and predict the potential consequences of an event. For instance, a doctor treating a patient with a chronic condition is likely to delve into the patient's past medical reports to understand the genesis and the ways to treat the disease.
On the other hand, prescriptive analytic is related to the predictive analytic. Now that one has an idea of what will happen in the future, they can clearly give out the probable outcome of the end. For instance, when a patient has a history of a specific condition, and the information is clear, a doctor can consider the data to predict the outcome and suggest a probable measure to alter the outcome.Predictive and prescriptive analytic use historical data to predict what is likely to happen in the future. At the same time, descriptive analytics looks at information and data that has been obtained in the past to explain what is currently happening.
References
Kannan, N., Sivasubramanian, S., Kaliappan, M., Vimal, S., & Suresh, A. (2019). Predictive big data analytic on demonetization data using support vector machine. Cluster Computing, 22(6), 14709-14720.
https://link.springer.com/article/10.1007/s10586-018-2384-8
Putra, H. Y., & Khodra, M. L. (2018, May). Descriptive And Predictive Analysis Of Mail Order Pharmacy. In 2018 International Workshop on Big Data and Information Security (IWBIS) (pp. 31-36). IEEE.