6.1: The Basics of Data Visualization Flashcards
What is the fourth step in the SOAR model, and what is its focus?
The fourth step in the SOAR model is “REPORT THE RESULTS,” which focuses on synthesizing the findings of business analytics for decision-makers.
It emphasizes the clear communication of findings in an appropriate format, summarization level, and language for the target audience.
Data visualizations play a crucial role in this step, making the data understandable through charts or graphs.
How do data visualizations, such as infographics, simplify complex information presentation?
Data visualizations, including infographics, simplify complex information presentation by breaking down difficult concepts into categories and visual components that are easy to read and understand.
They use easy-to-read charts and graphs with minimal text to convey information effectively.
Why is combining statistical analysis with data visualization important in understanding data?
Combining statistical analysis with data visualization is important because data sets that appear similar may actually be quite different.
Visualizations, such as scatterplots, provide a more complete picture of the data by revealing differences that statistical properties alone might not capture.
This approach allows for a comprehensive understanding of the data and its underlying story and interpretation.
What are the three software tools mentioned in the text that have excellent data visualization capabilities?
The three software tools mentioned are Excel, Power BI, and Tableau, all of which offer robust data visualization features.
Why is it recommended to use a bar chart to present data about company total sales, as opposed to a table with numerical values?
Using a bar chart is recommended because it makes it faster and easier to identify companies with the highest and lowest sales. The visual representation simplifies the process of understanding the data, especially when the data set is not logically ordered, as in this case.
What are the essential components of a chart for structured data, as explained in the text?
Horizontal axis: Used for labels (e.g., company names in the given example).
Vertical axis: Used for values (e.g., sales figures in dollars).
Scale: Indicates the range of values (e.g., from $0 to $40,000 in the given example).
Tick marks: Incremental values on the vertical axis (e.g., in increments of $5,000, starting from $0).
Data series: Represents the raw data visually (e.g., total company sales).
Data point: Individual values/categories within a data series (e.g., Total Sales data point for each company in the example).
How can you sort numerical data in Excel in ascending order, and what does this sorting do to the chart representation?
To sort numerical data in ascending order in Excel, ensure the active cell is in the Total Sales data column, click the Data tab, and select A-Z. This sorting arranges bars in the chart from left to right in ascending order, making it easier to identify the company with the highest sales.
Besides sorting numerical data, what additional elements were added to the chart in Exhibit 6.4 to enhance readability?
In Exhibit 6.4, a descriptive title, X and Y-axis labels, and formatting Total Sales values as currency on the Y-axis were added to the chart to enhance readability.
What is the significance of sorting when working with categorical data, and how does it vary for nominal and ordinal data?
When working with categorical data, especially nominal data (non-ranked categories like eye color), sorting is typically done based on numerical values (e.g., Total Sales).
However, if the categorical variable is ordinal (categories with a natural rank, like letter grades), it may make sense to sort by the category itself, following the natural order (e.g., A, B, C, D, F grades).
Why is it important to choose an appropriate sorting method based on the type of data being visualized?
Choosing an appropriate sorting method is crucial because it significantly affects the interpretation of the data.
For instance, sorting numerical data helps in identifying trends and comparisons.
For categorical data, sorting based on the nature of the categories (nominal or ordinal) ensures a logical and meaningful arrangement, aiding in a more accurate understanding of the data.
