English
Data analysis is: the process of extracting value and useful signals from a set of data. The aim of analysis is to understand the patterns, relationships, and internal structure of data in order to make intelligent decisions and discover new insights.
Data analysis includes many different processes and techniques, the most important of which are:
1. Descriptive Analysis: It is concerned with understanding and describing data by making use of descriptive statistics, graphs, and data collection related to measurements, distributions, and correlations between variables. This type of analysis helps in clarifying the past of data and understanding the current situation.
2. Diagnostic Analysis: It aims to analyze the causes and factors affecting the phenomena or events presented, and helps in understanding the causal and dependent relationships between different variables. It uses statistical tools such as analysis of variance and regression analysis to achieve this type of analysis.
3. Predictive Analysis: It is related to anticipating future events and analyzing upcoming trends through the use of forecasting models and machine learning techniques. This type of analysis is used for future forecasting and strategic decision making.
4. Exploratory Analysis: It aims to explore data and discover previously unknown patterns and relationships. It uses exploratory analysis tools such as data clustering, factor analysis, and hierarchical clustering to comprehensively analyze data and explore new information.
5. Prescriptive Analysis: It is concerned with providing data-based solutions and recommendations for future decisions through the use of machine learning and artificial intelligence techniques. This type of analysis aims to improve performance and make effective decisions based on the available data.
Data analysis is used in various fields such as marketing, finance, medicine, science, and others, where it can help determine the type of data you want to analyze and the analysis objectives associated with it.