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Distributional data analysis is a branch of nonparametric statistics that is related to functional data analysis.It is concerned with random objects that are probability distributions, i.e., the statistical analysis of samples of random distributions where each atom of a sample is a distribution.
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]
Software analytics is the analytics specific to the domain of software systems taking into account source code, static and dynamic characteristics (e.g., software metrics) as well as related processes of their development and evolution.
Data collection systems are an end-product of software development. Identifying and categorizing software or a software sub-system as having aspects of, or as actually being a "Data collection system" is very important. This categorization allows encyclopedic knowledge to be gathered and applied in the design and implementation of future systems.
Software development is the process of designing and implementing a software solution to satisfy a user.The process is more encompassing than programming, writing code, in that it includes conceiving the goal, evaluating feasibility, analyzing requirements, design, testing and release.
The development of these devices were motivated from the need to use random digits to perform simulations and other fundamental components in statistical analysis. One of the most well known of such devices is ERNIE, which produces random numbers that determine the winners of the Premium Bond , a lottery bond issued in the United Kingdom.
A systems development life cycle is composed of distinct work phases that are used by systems engineers and systems developers to deliver information systems.Like anything that is manufactured on an assembly line, an SDLC aims to produce high-quality systems that meet or exceed expectations, based on requirements, by delivering systems within scheduled time frames and cost estimates. [3]
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.