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Data analysis is the systematic process of inspecting, cleansing, transforming, and modeling data to discover useful information, support conclusions, and guide decision-making. It converts raw data into meaningful information that can be used in business, science, healthcare, artificial intelligence, and social sciences. Its purpose is to make decisions more informed, accurate, and effective by identifying patterns, relationships, trends, and insights within data. Data analysis includes multiple approaches, such as descriptive statistics, exploratory data analysis, confirmatory data analysis, predictive analytics, data mining, business intelligence, and text analytics. The process is generally iterative and may involve defining data requirements, collecting and preparing data, cleaning errors, exploring patterns, applying statistical or mathematical models, visualizing findings, communicating results, and implementing decisions. John Tukey’s definition also emphasizes that data analysis includes interpreting results and planning data collection so that analysis is more precise and reliable.
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The data analysis process transforms raw data into useful information for decision-making. It typically begins by defining data requirements based on the needs of users or stakeholders, identifying the experimental units and variables, and collecting data from sources such as sensors, interviews, online resources, organizational systems, and documentation. The collected data is then processed and integrated into structured datasets, followed by data cleaning to correct errors, remove duplicates, address missing or inaccurate values, and improve overall data quality. After preparation, analysts use exploratory data analysis to summarize and visualize the data, identify patterns, detect anomalies, and determine whether additional cleaning or data collection is needed. Mathematical and statistical models may then be applied to examine relationships, test hypotheses, make predictions, or explain variation among variables. Results can be developed into data products, communicated through tables, charts, and other visualizations, and used to support decisions and implementation. These phases are iterative: feedback from exploration, modeling, communication, or decision-making can require analysts to revisit earlier requirements, collection, processing, or cleaning activities.
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