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.
A parameter (P) describes the enter population Eg: Conducting a quiz to question random (voluntary) people
Skewness: it indicates data is concentrated on one side. Skewness shows where the data is situated.
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.
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.
In case the mean, median, mode all are equal then…
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.
∘ History of python
∘ Integrated Development Environment :
∘ Installation :
∘ Python installed or not
∘ What is a Comment?
∘ Run python
∘ Data types in python
∘ working with string
∘ Escape character
A python is an object-oriented programming language that was created by Guido van Rossum…
Representing not a part of a set:-
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
This is the continuation of the Statistics you can check out the first article here
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…
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…
The continuous Distribution follows
Student T 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
Usually all outcomes have equal probability or same probability, which are followed by ranges is referred as discrete uniform 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.