University of Virginia Data Visualization Discussion Question

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Part 1:When thinking about data visualization, it is important to understand regular expressions in data analytics.  Therefore, note the importance of data visualizations and choose two types of expressions (* - wildcards for example) and discuss the difference between the two types of expressions.

Your response should be 250-300 words.

There must be at least one APA formatted reference (and APA in-text citation) to support the thoughts in the post. Do not use direct quotes, rather rephrase the author's words and continue to use in-text citations.

Part 2: we need to respond to 2 of the class mates posts.

Required Text

Title: Data Visualisation

ISBN: 9781526468925

Authors: Andy Kirk

Publisher: SAGE Publications Limited

Publication Date: 2019-10-07

Edition: 2nd ED.

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Regular Expressions in Data Visualization Regular expressions are character sequences that match patterns in your Analytics data widely or narrowly. They enable data analysts to detect and replace inexact patterns as well as special characters in text such as tabs and line breaks. Not all data files, however, are neatly structured. Although dealing with disorganized data is time-consuming and, at times, a nightmare (Kun, 2014). Wildcard Dot (.) can be used to match any single character that can be any letter, number, or symbol. Use the dollar sign ($) can be used to match the earlier or neighboring characters at the end of a string (About Regular Expressions (Regex) - Analytics Help, 2013). Reference About regular expressions (regex)—Analytics Help. (2013). https://support.google.com/analytics/answer/1034324?hl=en&ref_topic=1034375 Kun, R. (2014, February 20). Extract information from texts with regular expressions in R Kun Ren’s Blog Posts. https://renkun.me/2014/02/20/extract-information-from-texts-withregular-expressions-in-r/ the regular expression or is a particular text string that describes a search query pattern. Regular terms on steroids can be considered wildcards. Wildcard notes like.txt are likely to be known to find any texts in a directory. The counterpart of regex is.*\.txt (- et al., 2020). The fundamental principle of regular expressions is to determine patterns that we like to fit in a string of text, and scan in a string to produce matches. A few of those patterns appear very odd as both the material we wish to match and certain letters are contained, which affect why the pattern is read. Regular expressions occur whenever string data is scanned but are a crucial tool at a minimum only at the fundamental level to grasp (Yang, 2020). A specific data element of a data string was its primary application of regular expressions. The webpage and app programmers usually employ regular expressions in order to verify if the client entries correspond to the needed data element structure. An illustration of how we find an e-mail address virtually every day. For an instance, the email address requires our username and the @ symbol, accompanied by the domain. Unless the client entry within the data element/field is appropriately typed as well as the error notice is displayed when it does not meet the necessary format, then we may utilize REGEXP MATCH. There may be 3 different parts in a regular phrase. Note: Not all terms are necessary, depending on the necessity (Flerlage, 2021). • Ordinary expression Classes of metacharacter – A Metacharacter is a character with a particular meaning for a software program, including a shells translator, or a regular expression engine for our instance REGEX. • Operators/quantifiers for regular expression - utilized to enhance a pattern. For instance, how often the patterns must be replayed. • Set Expressions – utilized to set certain letters to matching. Regular expression to begin at a certain place inside the string in our instance, from either the comma and space we must look ahead. Let's see into its components. • , – It's only a string literal. We look for just a comma. We seek. The first dot (.) corresponds to almost any single character but not the Break lines Whatever single character is matched 0 or more occurrences. It's being used to fit any amount of characters in a data file. In regular expressions, the dot has a specific significance. To locate a dot inside a string, the escape symbol backslash() is being utilized, which fits a single dot. txt is indeed a string literal that fits the actual txt phrase just after the dot. • \s - This metacaracter signifies the space. So we're looking for a room. We're currently scanning for just a comma and spaces in combination also with a comma. • + – It is called after the gap and commands matching we will record a capture group word. The wild Cards Notations and differences between each Use case are as follows . The first dot (.) corresponds to almost any single character but not the Break lines * Whatever single character is matched 0 or more occurrences. It's being used to fit any amount of characters in a data file. \. In regular expressions, the dot has a specific significance. To locate a dot inside a string, the escape symbol backslash() is being utilized, which fits a single dot. Txt txt is indeed a string literal that fits the actual txt phrase just after the dot. References: Flerlage, K. (2021). An Introduction To Tableau Regular Expressions (REGEX). The Flerlage Twins: Analytics, Data Visualization, and Tableau. https://www.flerlagetwins.com/2021/05/regex1.html. Yang, A. (2020, April 21). Tutorial: Python Regex (Regular Expressions) for Data Scientists. Dataquest. https://www.dataquest.io/blog/regular-expressions-data-scientists/. -, O. R., By, -, & Rathee, O. (2020, July 13). Detailed Guide to Regular Expressions: vizartpandey expressions/. regex. Welcome to Vizartpandey. https://vizartpandey.com/regular-
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Data Visualization

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Data Visualization
As the internet of things continues to expand, the notion of cloud computing has
dominated. There is big data in the cloud. For the human mind, this information cannot be easily
understood due to bulkiness. The mind has a small processing capability of such an enormous
amount of data. It might...


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