In this blog, Codeavail experts will explain to you about the basic concepts of statistics in detail. It is one of the important tools for making the art of Data Science (DS).

According to a high-level view, it is the mathematics branch used for performing data technical analysis. A basic visualization might provide you some data of high-level. With the help of this blog, you can perform data in a targeted way.

A basic visualization like a bar chart may give you some high-level data, but with statistics you get to work on the data in a much more informative and targetive way. Rather than just guesstimating mathematics helps us form strong. data conclusions. In this blog you will get the perfect information about basic concept of statistics.

By using statistics, we can get better and more deep knowledge of how exactly data can be formatted and on the basis of that structure how we can apply other data science methods to get even more knowledge.

Likewise, you are going to see 3 of the basic concepts of statistics that every data scientist should have the understanding and how these basic concepts of statistics can be used in the most effective ways.

**Some Basic Concepts of Statistics**

Table of Contents

**Statistics Definition**

It is one of the essential and most strong math parts. Statistics is the mathematics part which utilize to work with data organization, collection, presentation, and outline.

In other words, statistics is all about achieving some methods on the raw information to make it easier to understand.

The model of Statistics helps apply statistics scientific, industrial and social problems.

**Statistics example **

Let’s assume that you have ask to calculate 80 students’ average weight in your class. It is not easy to calculate the student’s average weight manually. This is where statistics play an essential role. To calculate the 80 students’ average weight you can use statistics functions. With the help of Many statistics functions you can calculate the student’s average weight.

**Probability Distributions**

Probability may be define as the percent probability that how many events will happen. In data science this is usually calculate the scale of 0 to 1, where 0 indicates we are sure this will not happen and 1 indicates we are sure it will happen. A probability distribution function describes all possible values probabilities in the experiment.

**Uniform distribution: **

For a better understanding of uniform distribution lets get back to the example of rolling a die where the possible results are both likely to appear than the other.

This type of probability distribution is consider to be a uniform distribution.

**Poisson Distribution:**

It is related to the Normal Distribution but with a *skewness *added factor. With a skewness, less for Poisson distribution value will have an almost uniform range in all directions just like the Normal distribution.

The skewness value is large in magnitude the range of our data will be change in several directions.

**Bernoulli distribution: **

The outcome here has only two possible directions. Two possible results are 0 and 1 respectively. This means to say that a random variable Y may be a failure if it takes the value 0 or success if it takes the value 1. Here the probability of failure and success may not be the same.

**Bayesian Statistics**

For a better understanding of Bayesian Statistics first one should know where Frequency statistics fail.

Frequency statistics are kind of statistics that individual think when “probability” word comes to their mind.

**Bayes’ Theorem formula**

Understand bayes’ theorem by formula:

**P(A/B) **prior probability

**p(B/A) **likelihood of the evidence ‘B’ if Hypothesis ‘A’ is true

**P(B/A)** posterior probability of ‘A’ given the evidence

**P(B) **prior probability that the evidence itself is true

In this equation, the probability *P(A)* is your frequency analysis. The *P(B/A)* is as likely in this equation. It is essentially the probability that your evidence is accurate, given data from your frequency analysis.

For example, if you roll the die 10,000 times, and you get 6 in the first 1000 rolls. The *P(B) *is the probability that the original evidence is correct.

**Under and over Sampling**

Under and over Sampling are methods apply for problem class. Sometimes, our data set classification maybe heavily slant to one side. For example, for class 1 we have 100 examples, but for class 2 only 20. That will throw off a lot of the ML methods we work and practice to create the data and make predictions. For example, check out the graph below.

On both sides of the image, as compared to the orange class blue class has more samples.

In that case, we have two pre-processing choices to help in the Machine Learning models training.

Under sampling means we will choose only *a *few data from the class which has more data, These choices be done to maintain class probability distribution.

On the other hand Oversampling means that you need to make minority class *copies *in order to get the equivalent number of examples as the majority class has.

The copies of the minority class will be created in such a way the distribution of the minority class is reserved.

**Conclusion**

In this blog, we have discussed essential basic concepts of statistics not just how to calculate them, but also how to evaluate them.

These basic concepts of statistics are important for every data scientist should know. From statistics you get to operate on the data in a much more information-driven and targeted way.

If you still need additional information regarding statistics then you can reach us through email, call or live chat we are available round the clock to assist you.

We have a team of professionals who have years of experience in their respective fields.

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