# Learn Statistics for data science

## What is Statistics? 🤔

Generally, statistics are applied for collecting, organizing and analyzing the data(a piece of information). In other words, analyzing the numerical information and extracting information from data.

A Statistic is a measure that tells only a Sample of the population. Eg: Conducting a Quiz to Question random (voluntary) people.

## Descriptive Statistics And Inferential Statistics:

Descriptive Statistics: It involves the method of picturing information observed from samples and population

Inferential statistics: Method of using information from a sample to conclude the population.

## Parameter:

A parameter (P) describes the enter population Eg: Conducting a quiz to question random (voluntary) people

Population :

# Python for everyone

## How to become an expert in python programming

In this article, we will discuss python from beginner to expert. To understand python there are no prerequisites is needed as I already told python if for everyone. I would like to thank freeCodeCamp and GUVI are helped me to understand complexity in python.

## History of python :

A python is an object-oriented programming language that was created by Guido van Rossum and…

# How probability distributions are related to data science

## Notation :

P(Y=y) or P(y)

A distribution can be carried out by the following characteristics

Mean…

# Sets and events you need to know for data science

## Non-Empty Set :

Example :

(OR)

Representing not a part of set :-

# How Combinatorics used in data science

Combinatorics deals with combinations of objects from a finite set. In order to perform combinatorics, they have some restrictions (condition) like avoiding repetition and order to get a number of favorable outcomes.

## Types of Combinatorics :

Combinatorics has three parts, Namely

# what are the fundamentals of statistics?

## Fundamentals of statistics

Hi, if you are started to learn data science? then this article is for you I have already shared my experience of what I Learned in Statistics to kickstart my Data Science career, this is the continuation of the “Learn Statistics for data science” if you haven’t read I would suggest you go for it to have clear knowledge about statistics😃.

`Let us see the fundamental of statistics…`

# Cyber Security

Generally, cybersecurity is used to prevent data exploitation from network attacks or unauthorized entry. The NIST — National Institute of Standards and Technology provides a set of rules to prevent and protect data from illegal entry.

The principle of CIA stands for Confidentiality, Integrity, Availability, which is used to protect information from unauthorized access, modification.

Confidentiality: Prevents data from the illegal approach.

Integrity: integrity will secure information like data encryption. This uses hash values for data integrity verification. Eg: downloading os from the internet later verification is made to install the operating system.

Availability: This verifies that hardware components, software…

# Probability Distribution

## Continuous Distribution 🚙

The continuous Distribution follows

Normal Distribution

Student T Distribution

Logistic Distribution

Exponential Distribution

# Discrete Distribution 🌱

## Learn about the fundamentals of discrete distribution

⚡ The probability distribution is base for the statistics without the knowledge of probability you will be in trouble to learn statistics 👻. In this article we will discuss about the various types in Discrete Distribution.

The types of Discrete Distributions are

## Discrete Uniform Distribution :

Usually all outcomes have equal probability or same probability, which are followed by ranges is referred as discrete uniform distribution.

## What is Bernoulli Distribution ?

✌️ The bernoulli distribution is denoted by “Bern” followed by probability of referred outcomes ‘Bern(P)’. This distribution is the special privilege of binomial distribution.

# Understating Probability for inferential statistics 