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Data Analytics


Data analytics is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analytics has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, and social science domains. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analytics that relies heavily on aggregation, focusing mainly on business information.[1] In statistical applications, data analytics can be divided into descriptive statistics, exploratory data analytics (EDA), and confirmatory data analytics (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analytics. Data integration is a precursor to data analytics,[according to whom?] and data analytics is closely linked[how?] to data visualization and data dissemination. The term data analytics is sometimes used as a synonym for data modeling.

->    Writing well designed, testable, efficient code by using best software development practices

->    Integrating data from various back-end services and databases

->    Creating software layout/user interfaces by using standard cooding practices