Descriptive and Predictive Analytics
Explore how descriptive analytics helps analyze past data to summarize events and identify causes. Understand predictive analytics techniques that forecast future outcomes, enabling data-driven decision making and optimization.
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Descriptive analysis
Descriptive analytics focuses on analyzing past data to understand what happened and summarize events and patterns. Expanding on this, diagnostic analytics goes a step further to explain why something happened by identifying relationships and possible causes. It is an important part of a data scientist’s work, as it helps uncover cause-and-effect patterns in historical data.
Examples
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Which areas in the world did a particular book sell the most based on sales data?
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Which country’s economy grew the most in the past year?
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Why do cars in traffic follow a certain route the most?
Predictive analytics
Predictive analytics is about forecasting what may happen in the future based on past and present data. Prescriptive analytics helps determine which actions to take to alter the outcomes of future events. This is also an important part of a data scientist’s work, as it focuses on using data-driven insights to guide future choices and optimize outcomes for the company.
Examples
What will the weather be next week based on previous weather data?
Where will a wildfire occur before it actually happens?
Where is a crime about to happen?