2. Name and explain the eight hats/roles of data visualization. 4. Summarize/discuss at least 6 key things discussed in this course that influence one’s Design Choices. 5.What is the importance of RegEx or Regular Expressions in data analytics? Discuss the differences between the types of regular expressions. Choose two types of regular expressions and discuss the differences between the two. Please be sure to include two or three differences for each. Include how they help manipulate data. 6.Excel provides many features that make it useful for data visualization. It is a tool still widely used despite its limitations. Newer versions of Excel have addressed some of the limitations. What are 3 or 4 cons (limitations) to using Microsoft Excel for data visualization? 10. Three storytelling techniques are discussed in the text (pages 161-209) in which data is presented and stories are being interpreted. The techniques are described as inheritant characteristics of some charts and graphics. What is the importance and the advantages of using these techniques? Provide an example of each technique. 14. List and Describe 3 or 4 myths that are described in chapter 11 of Andy Kirk’s book. Then state what is Kirks response to these myths.

5. The importance of RegEx or regular expressions in data analytics lies in their ability to efficiently search and manipulate text strings. Regular expressions are a powerful tool for pattern matching, allowing data analysts to search, extract, and manipulate specific substrings in large sets of unstructured data. They provide a flexible and efficient way to perform complex search and replace operations, data validation, and data transformation.

There are several types of regular expressions, each with its own syntax and functionality. Two commonly used types are POSIX regular expressions and Perl regular expressions.

One key difference between POSIX and Perl regular expressions is their matching behavior. POSIX regular expressions are typically more conservative in their matching behavior. They try to find the longest match possible, meaning that they will match the maximum amount of text that fits the specified pattern. This behavior can be useful for capturing specific portions of text, but it may also lead to unexpected matches when dealing with ambiguous patterns.

On the other hand, Perl regular expressions are more greedy in their matching behavior. They try to find the shortest match possible, meaning that they will match the minimum amount of text that fits the specified pattern. This behavior can be useful when dealing with patterns that have overlapping matches or when you want to match specific occurrences of a pattern within a larger text.

Another difference between POSIX and Perl regular expressions is their support for advanced features. Perl regular expressions offer a more extensive set of features, including lookaheads, lookbehinds, and backreferences. These features provide additional functionality for complex pattern matching and data manipulation.

Both POSIX and Perl regular expressions can be utilized to manipulate data effectively. For example, if you have a dataset containing email addresses, you can use regular expressions to extract the domain names from the email addresses and categorize them. Regular expressions can also be used to validate and clean data by removing unwanted characters or formatting.

6. Despite its widespread use, Microsoft Excel has several limitations when it comes to data visualization. Some of these limitations include:

1. Limited data scalability: Excel has a restricted capacity for handling large datasets. When dealing with millions of rows or complex data structures, Excel can become slow and inefficient.

2. Limited data preprocessing capabilities: Excel lacks advanced data preprocessing functionalities, making it challenging to clean, reshape, or transform data effectively. It lacks tools for handling missing data, outlier detection, or data normalization.

3. Limited interactivity and dynamic visualization: Excel’s visualization capabilities are mainly static and do not allow for interactive exploration or dynamic updating. This limits the ability to provide real-time insights or interactive dashboards.

4. Limited customization options: Excel’s charting features are limited compared to specialized data visualization tools. It has a limited range of chart types and limited options for customization, making it difficult to create complex visualizations or customize visual elements.

Despite these limitations, newer versions of Excel have introduced some improvements, such as enhanced data connectivity options, additional chart types, and the integration of Power Query and Power Pivot for more advanced data manipulation. However, for more sophisticated data visualization requirements, it is often recommended to use specialized data visualization tools that offer more flexibility and functionality.