descriptive analysis in data science

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Descriptive Analysis In Data Science
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Table Of Content

1. What is Descriptive Analysis?
2. Why Is Descriptive Analysis Important?
3. How Does Descriptive Analysis Work?
4. Applications Of Descriptive Analysis

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What is Descriptive Analysis ?
Descriptive analysis can be used to determine
the strengths and weaknesses of an organization.
Some metrics that are used in descriptive
analysis include the year-overyear price
changes, monthly sales growth, number of users
or total revenue per subscriber.
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Why Is Descriptive Analysis Important?
It is important to use descriptive statistics
because raw data can be difficult to understand,
especially when there are a large number of
them. Descriptive statistics allows us to
present data in a meaningful manner, which
simplifies interpretation.
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How Does Descriptive Analysis Work?
A descriptive analysis is a type of data
research which helps to describe, demonstrate,
or summarise data points in order for patterns
that meet all conditions of the data to develop.
This technique identifies patterns and
connections by using recent and historic data.
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Applications Of Descriptive Analysis
When aggregation of historical data is required
to analyze the performance and results of an
organization at an aggregated level, descriptive
analytics comes into play. Real World Examples
Marketing and Social Media Professionals use
Google Analytics tools to gather web results
statistics and other types data.
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