Statistics is a subject and a branch of mathematics that is related to all the collection, analysis, interpretation, and visualization of empirical data, and there are two major areas of statistics are descriptive statistics and inferential statistics. If we talk about, descriptive statistics are used to describe the characteristics of sample and population data (what has happened). These properties are used by inferential statistics to test hypotheses, reach conclusions, and make predictions (what can you expect).

# Tag: Statistics

## Bayes’ Theorem with Example for Data Science Professionals

Bayes Theorem is the extension of Conditional probability. Conditional probability helps us to determine the probability of A given B, denoted by P(A|B). So Bayes’ theorem says if we know P(A|B) then we can determine P(B|A), given that P(A) and P(B) are known to us.

## Probability Basics for Data Science

Probability is used to predict the likelihood of the future event.

Statistics is used to analyse the past events

Also,

Probability tells us what will happen in a given ideal world?

While Statistics tells about how ideal is the world?

Probability is the basics of Inferential Statistics.

## Variance, Standard Deviation and Other Measures of Variability and Spread

Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point.

## Basic Statistics for Data Science – Part 1

Types of Statistics: Descriptive vs Inferential

Basic terminology like Population vs Sample

Types of Variables: Numerical vs Categorical

Measures of central tendencies: Mean, Median and Mode and their specific use cases

Measures of dispersion/spread: Variance, standard deviation etc.