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.