Different types of Data Analysis

Descriptive Analysis: This is the most basic form of data analysis. It involves the calculation of simple measures such as averages, percentages, and counts. The goal is to describe and summarize the data.

Exploratory Data Analysis (EDA): This type of analysis is used to find patterns, relationships, or anomalies to inform future studies. EDA often involves visual methods such as scatter plots and histograms.

Inferential Analysis: This type of analysis makes inferences about a population based on a sample of data. It often involves hypothesis testing and the calculation of confidence intervals.

Predictive Analysis: This type of analysis uses statistical models to predict future outcomes based on historical data. It often involves machine learning techniques.

Prescriptive Analysis: This type of analysis uses optimization and simulation algorithms to advise on possible outcomes. It not only predicts but also suggests a range of prescribed actions and associated outcomes.

Causal Analysis: This type of analysis is concerned with finding cause-and-effect relationships between variables. It often involves experimental or quasi-experimental research designs.

Mechanistic Analysis: This type of analysis is used to understand the exact changes in variables that lead to changes in other variables. It is often used in scientific research to understand the mechanisms behind observed relationships.