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.
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.
Poisson distribution
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.
Bernoulli distribution
Bernoulli distribution
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:
Bayes' theorem
Bayes’ theorem
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.
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