# Learn Statistics for data science

## Fundamentals of statistics you need to know to start your data science career

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: 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.

• Statistics deals with population and sample.

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

Population :

• A

# Elementary of statistics you need to know

Skewness: it indicates data is concentrated on one side. Skewness shows where the data is situated.

• There are three different skews

1. Right Skew

2. Left Skew

3. No Skew

We will learn about the types of skewness briefly.

If the mean is greater than the median then it is considered as a right skew.

• Mean > Median

In this type left skew is the opposite of the right skew, yes if the mean is lower than the median, we can say it is the left skew.

• Mean < Median

In case the mean, median, mode all are equal then…

# 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 is for everyone. I would like to thank freeCodeCamp and GUVI are helped me to understand the complexity of python.

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

# Sets and events you need to know for data science

• Sets are used to eliminate duplication
• Event : Set of outcomes, If a set is empty it is called an empty set or null set denoted by Ø .
• A non-empty set can be finite or infinite

Example :

• X A (X in A)
• The above tells that X is the element of set A
• Where A -is set, X -is an element

(OR)

• A X (A contain x)
• Where A -is set, X -is an element.

Representing not a part of a set:-

• X A ( which means X is an element set not in A…

# How Combinatorics used in data science

Combinatorics deals with combinations of objects from a finite set. To perform combinatorics, they have some restrictions (conditions) like avoiding repetition and order to get several favourable outcomes.

Combinatorics has three parts, Namely

1. Permutation
2. Variations
3. Combinations
• Permutation represents several different possible ways we can arrange a set of elements. Eg: a jigsaw puzzle.
• These elements can be in numbers, letters, etc.,
• Example: Consider three racers(drivers) namely Max, Bob, Ram if max won 1st place then Bob will be 2nd place and Ram will be 3rd place otherwise Bob won 3rd place and ram won 2nd price like this we have…

# Data Visualization Concepts in Statistics

## Learn about how the data are visualized in different ways

This is the continuation of the Statistics you can check out the first article here

# 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…`
• A variable can take many values like Age is a variable. Remember a variable contains a value.
• for example Gender — male. …

# 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

• The normal distribution can be denoted by N(μ,σ²) The normal distribution occurs in nature as well as in various shape and forms.

# 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 the various types of Discrete Distribution.

The types of Discrete Distributions are

1. ✈ Discrete Uniform Distribution.
2. ✈ Bernoulli Distribution.
3. ✈ Binomial Distribution.
4. ✈ Poisson Distribution.

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