Data analysis

The processes of examining, cleaning, converting, and modelling data are all part of the data analysis process. The end purpose of this process is to find usable information, inform conclusions, and provide support for decision-making. The practise of data analysis encompasses a wide range of fields, including economics, science, and the social sciences, and includes a wide number of methods that are referred to by a variety of different names. In the modern world of business, data analysis plays an important role in bringing a greater level of objectivity to decision-making and in facilitating more efficient corporate operations.

Data mining is a specific method of data analysis that focuses on statistical modelling and knowledge discovery for predictive rather than merely descriptive purposes. In contrast, business intelligence covers data analysis that heavily relies on aggregation and focuses primarily on information related to businesses. Data analysis may be broken down into descriptive statistics, exploratory data analysis (also known as EDA), and confirmatory data analysis in the context of statistical applications (CDA). Exploratory data analysis (EDA) focuses on finding previously unknown aspects of the data, while confirmatory data analysis (CDA) seeks to validate or invalidate preexisting ideas. Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, which are a species of unstructured data. On the other hand, predictive analytics is focused on the application of statistical models for predictive forecasting or classification. All of the aforementioned activities are forms of data analysis.

Integration of data comes before analysis of the data, which in turn is strongly connected to display of the data and the distribution of the data.