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In this, IT world, enormous of data is being produced a day –to –day. People usually confused on how do these data is being produced? From where the data is being produced? Moreover, they also think about how to analyze this data? How do stats take part in data science? Do you think these stats play a major role in data Science? if your answer is NO, you are absolutely wrong. Because stats play a major role in analyzing the data. They are useful in analyzing how much data is being analyzed day –to day? Moreover, this stats are useful in analyzing in which day, the data produced is high. Read the complete article to know How do stats take part in data science

Today, we will be getting data from various sources. From this various sources, we need to collect and combine these multiple sources of data. So this combined data is being analyzed in two ways. So let me explain you each way of data briefly.

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descriptive statistics :

This stats is useful for describing (or) presenting the data either numerically (or) pictorially. This is used for day to day analysis of data. But the drawback is, we could not able to estimate the future through this statistics. To overcome this, we have Inferential analysis.

Inferential statistics:

These statistics were produced by the more complex mathematical calculations. It allows us to make assumptions and predictions. These predictions were based on the populations, and the sample taken from it.
Along with this statistics, we have several measures to take into consideration. so let me explain to you all those topics in How do stats take part in data science.

Normal distribution :

It is the most commonly used analysis by the statisticians. Moreover, it is the basic distribution seen by the statisticians. It usually describes how the graph looks like when plotted. it is sometimes called as BELL CURVE (or) Gaussian curve.

This is usually required at the time of the Inferential statistics. Moreover, the data needs to be distributed carefully. If there is any wrong in this distribution, it leads to the wrong conclusions. In a perfectly normal distribution, each side is exactly a mirror of other.

Central tendency :

The one more topic, that we need to discuss is mean median and mode. This is simply called as a central tendency. This is the basic calculation that would be taken for each sample of data. Moreover, no stats can be taken without this. So now let me explain to you the central tendency in how do stats take part in data science.

Central tendency :

Mean: It is the most considerable assumption from a single sample. It can be calculated by taking the average of all the samples in the population. In other words, it can be calculated as the sum of all the values to the total number of sample in the population

Mode: It is considered as the most frequently occurred sample in the population. In other words, it is the sample that has occurred max number of times in the population. The mode value can be anything. And this mode can vary from zero to many. i.e some populations have zero modes, some have one whereas some have many. It is the only measure of central tendency for categorical variable . since we cannot get the average for certain things like Gender.

Median: It the Mid value of the all the samples in the population. In other words, it is the midpoint of your data. It is also called the 50th percentile. It is a less affected component in the population.

In a normal distribution, all these three will be same. i.e mean, median and mode would be same in a normal distribution.

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