Statistics is the backbone of data science. It's the foundation on which predictive modeling and machine learning are built. Without a strong understanding of statistics, it would be difficult to do any sort of data analysis. In this blog post, we will discuss the importance of statistics in data science, and what every data scientist needs to know!

## What is Statistics and why is it important in data science

Statistics is the science of collecting, analyzing and interpreting data. It is an important tool for understanding and making decisions about the world around us.

Statisticians use statistical methods to help answer questions such as:

- How many people are affected by a particular disease?
- Are certain groups of people more likely to develop certain diseases?
- What are the chances of a particular event occurring?

Statistics plays a vital role in data science by providing a way to analyze and interpret data. Data scientists use statistics to understand patterns and trends in data, which can then be used to make predictions about future events. Without statistics, data science would not be possible.

## 1. Descriptive Statistics

When working with data, it's important to be able to understand and summarize the information that you're dealing with. This is where statistics come in. Descriptive statistics are a set of tools that allow you to do just that. They help you to organize, understand, and summarize data. For example, let's say you have a statistics homework with a dataset consisting of the ages of 100 people. You could use descriptive statistics to calculate the mean, median, and mode of the data. This would give you a better understanding of the distribution of ages in the dataset. You could also use descriptive statistics to create a histogram of the data, which would visually show you how the ages are distributed. As you can see, descriptive statistics are a valuable tool for data

## 2. Inferential Statistics

Statistics are important in data science because they allow us to make inferences about a population based on a sample. In other words, they help us to understand how likely it is that a result occurred by chance. For example, imagine that we want to know whether men or women are more likely to use Facebook. We could survey a random sample of people and ask them whether they use Facebook. If the proportion of men who use Facebook is higher than the proportion of women who do, we might conclude that men are more likely to use Facebook. However, there is always a chance that this difference occurred by chance. Statistics allow us to quantify this risk and, as a result, make more informed conclusions. In this case, statistics would tell us whether the difference between the proportions of men and women who use Facebook is likely to have occurred by chance or not.

## 3. Sampling Methods

Statistical methods are used in data science to collect, analyze, and make predictions from data. One of the most important aspects of statistical methods is sampling. Sampling is the process of selecting a representative subset of data from a larger dataset. It is important to use sampling methods when studying data because it is often impractical or impossible to collect all of the data. For example, if you wanted to study the effects of a new drug, it would be unethical to test it on every person in the world. Instead, you would need to use a sampling method to select a group of people to test the drug on.

## 4. Estimation Methods

As a data scientist, one of the most important skills you can have is the ability to effectively use and interpret statistics. Statistics are a fundamental tool that allows you to make informed decisions based on data. They can help you understand trends, identify relationships, and make predictions. In addition, statistics can be used to estimate quantities that are difficult to measure directly. For example, if you want to know the average income of people in a certain area, you can use statistical methods to estimate this number from a sample of people. Estimation methods are essential for data science because they allow you to make inferences about a population based on a limited amount of data.

## 6. Hypothesis Testing

In any scientific research process, data is collected to be analyzed and conclude from. However, before any valid conclusions can be drawn, that data must first go through a process known as hypothesis testing. In hypothesis testing, a researcher comes up with a theory, or hypothesis, and then tests how likely it is that the data they have collected supports that hypothesis. If the data collected does not support the hypothesis, the researcher can then either modify the hypothesis or scrap it entirely and start over. Consequently, statistics play a vital role in data science, as they are essential for hypothesis testing. Without statistics, it would be very difficult to draw any valid conclusions from data.

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