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Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
It is a main task of exploratory data analysis, and a common technique for statistical data analysis, ... Repeat steps 2,3 and 4 till all the cells are traversed.
The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and natural and social sciences. Carl Friedrich Gauss made major contributions to probabilistic methods leading to statistics.
Data processing may involve various processes, including: Validation – Ensuring that supplied data is correct and relevant. Sorting – "arranging items in some sequence and/or in different sets." Summarization (statistical) or – reducing detailed data to its main points. Aggregation – combining multiple pieces of data.
The primary aim of data-flow diagrams in the context of structured design was to build complex modular systems, rationalizing the interdependencies across different modules. [3] Data-flow diagrams (DFD) quickly became a popular way to visualize the major steps and data involved in software-system processes.
The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Vitaly Friedman (2008) the "main goal of data visualization is to communicate information clearly and effectively through graphical means.