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Data Visualization and Regular Expressions

Data visualization is very important in daily business operations and analysis of business data. To begin with, it enables analysts to analyze their data in a better way by allowing them quickly to realize the crucial points required for the business since they are able to visualize new data patterns and concepts (Islam & Jin, 2019). This also enables the business and analysts to identify the causes of new data patterns. Besides, it ensures faster decision-making since it allows decision makers quickly to form relationships between the data patterns. Additionally, it enables analysts to reveal errors in the data thus enabling them to perform fast analysis and take timely actions.  This also assists businesses to make better predictions or forecasting. Furthermore, data visualization acts as a key to reveal big data. This enables the analysts quickly to understand the large amount of visualized data thus reducing time for report customization (Islam & Jin, 2019).  

The types of expressions I chose are anchors and escape. Anchors do not match a character but assist in matching a position at the beginning or the end of a character (Chowdhury et al., 2017). For instance, they assist in creating expressions occurring at the beginning of a word or at the end of a word. They also assist in fixing regular expressions to beginning or the end of a line. Escape enables a regular expression engine to interpret metacharacters by performing string conversion. For instance, it indicates that there should be literal interpretation of the adjacent character. Therefore, the difference between anchors and escape is that anchors only shows that a pattern matches at the beginning or the end of a character while escape converts a string to enable metacharacter interpretation (Chowdhury et al., 2017).