Exploratory Data Analysis (EDA) is an approach used by analysts to analyze data sets to summarize their main characteristics, often employing visual methods. The primary goal of EDA is to discover patterns, spot anomalies, test hypotheses, and check assumptions through statistical graphics and other data visualization tools.
Option A:Performance analysis
Rationale:Performance analysis focuses on assessing the efficiency and effectiveness of a system, process, or individual. It involves measuring key performance indicators (KPIs) to determine how well objectives are being met. This type of analysis is not primarily concerned with identifying patterns or anomalies in data sets.
Option B:Exploratory analysis
Rationale:Exploratory analysis involves examining data sets to uncover underlying structures, extract important variables, detect outliers, and test underlying assumptions. By using statistical tools and visualization techniques, analysts can identify patterns andanomalies, making it a crucial first step in data analysis.
[Reference:The CompTIA Data+ Certification Exam Objectives emphasize the importance of exploratory data analysis in identifying patterns and anomalies within data sets., comptia.org, Option C:Link analysis, Rationale:Link analysis is a data analysis technique used to evaluate relationships or connections between nodes in a network. It's commonly used in fields like criminology, fraud detection, and network analysis to identify relationships between entities. While valuable, it is more specialized and not typically used for general pattern and anomaly detection in data sets., Option D:Trend analysis, Rationale:Trend analysis involves analyzing data to identify patterns or trends over a specific period. It is used to predict future movements based on historical data. While it focuses on patterns, it is specifically concerned with temporal changes rather than a broad examination of data for various patterns and anomalies., ]
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