It can analyze the potential implications of different choices and recommend the best course of action. It not only predicts what is likely to happen but also suggests an optimum response to that outcome. Prescriptive analytics takes predictive data to the next level. Having anticipated their customer’s future travel requirements, the company could start targeted advertising for those cities from February.
The computer program or algorithm may look at past data and predict booking spikes for certain destinations in May. In each of these techniques, computers are trained to reverse engineer causality connections in the data.For example, the flight service team might use data science to predict flight booking patterns for the coming year at the start of each year. It is characterized by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. Predictive analysis uses historical data to make accurate forecasts about data patterns that may occur in the future. This may lead to the discovery that many customers visit a particular city to attend a monthly sporting event. Multiple data operations and transformations may be performed on a given data set to discover unique patterns in each of these techniques.For example, the flight service might drill down on a particularly high-performing month to better understand the booking spike. It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. Diagnostic analysisĭiagnostic analysis is a deep-dive or detailed data examination to understand why something happened. Descriptive analysis will reveal booking spikes, booking slumps, and high-performing months for this service. It is characterized by data visualizations such as pie charts, bar charts, line graphs, tables, or generated narratives. For example, a flight booking service may record data like the number of tickets booked each day. Descriptive analysisĭescriptive analysis examines data to gain insights into what happened or what is happening in the data environment. Data science is used to study data in four main ways: 1.
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