What are the different statistical techniques used in data science?

There are many statistical techniques used in data science, including –

The arithmetic mean – It is a measure of the average of a set of data

Graphic display – Includes charts and graphs to visually display, analyze, clarify, and interpret numerical data through histograms, pie charts, bars, etc.

Correlation – Establishes and measures relationships between different variables

Regression – Allows identifying if the evolution of one variable affects others

Time series – It predicts future values ​​by analyzing sequences of past values

Data mining and other Big Data techniques to process large volumes of data

Sentiment analysis – It determines the attitude of specific agents or people towards an issue, often using data from social networks

Semantic analysis – It helps to extract knowledge from large amounts of texts

A / B testing – To determine which of two variables works best with randomized experiments

Machine learning using automatic learning algorithms to ensure excellent performance in the presence of big data.