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1 Data Visualization Assignment 0 5 10 15 20 25 30 35 0 0 0 1 0 0 1 0 0 1 1 5 3 0 4 3 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 2 8 14 15 1 7 7 3 3 1 3 16 2 2 7 2 2 0 Sum of Asian Sum of White Sum of Native_Hawaiian Sum of Black Sum of Hispanic Sum of American_Indian 0 2 4 6 8 10 12 14 16 18 Insomnia in Adolescents Sum of American_Indian Sum of Hispanic Sum of Black Sum of Native_Hawaiian Sum of White Sum of Asian Age(years) # of people 1. Create an example of a good and a bad visualization. Use the principles of data visualization to tell a statistically meaningful story. Export the images and insert them into a Word document. To create a statistically meaningful story from the provided dataset, I focused on the distribution of race, age, and gender among the individuals in the dataset who tested yes for insomnia. I used data visualization techniques to gain insights into the representation of different racial groups and genders based on adolescents. Story: Both charts visually present insomnia recorded across the racial distribution among male and female individuals of different age groups (16 to 20). Among males aged 16 to 20, the majority are White, making it the most prominent racial group testing for insomnia. The second tallest group is Asian, while Hispanic and American Indian, and Black populations scored significantly less, and each has smaller columns. Among females aged 16 to 20, White remains the dominant racial group testing yes for insomnia, as Asian follows closely, then Hispanics
2 make up in third, while American Indian and Native Hawaiian each account for 4% and 1%, respectively. The charts show that the White population is the tallest in both gender categories, and Asians also have a significant presence. Overall, this column chart provides a clear visual representation of racial composition within different gender and age categories, highlighting the significant racial groups testing yes for insomnia while providing a clear comparison between males and females. 2. Discuss the changes made between the first and second visual. In your discussion, include the type and level of the data, the type of visualization that was used for the data, why that visualization was selected and how color, shape and size can be used to increase engagement with a visualization. Finally, discuss what you are communicating of managerial importance in your visual. For this data, I used a bar chart to represent the bad example, and a column chart to represent the good example in visualizing the distribution of mentioned variables. (Please note that the provided dataset is relatively small, and the insights obtained are limited to the sample provided from an online source.) However, the above visualizations offer an initial overview of the race, age, and gender distribution of insomnia. The first example of the bar chart was very poor and confusing to read and identify due to the clash of colors combined, and the added numbers counted across the races. Also, the missing x-and y-axis make it confusing for the reader to identify the age column. In the good example with the column chart, one can easily see x-axis represents the gender and age categories (Male and Female, Age 16 to 20), and the y-axis represents the percentage distribution of each racial or ethnic group within those categories. The chart uses solid and consistent colors for each group, making it easy to distinguish them. Type and Level of Data: The provided dataset contains categorical data for these attributes: "Race" and "Gender." The "Race" attribute is a nominal categorical variable, indicating the different racial groups of individuals (e.g., American Indian, Asian, etc.).The "Gender" attribute is also a nominal categorical variable, representing the gender of individuals (Male or Female). While age is the quantitative variable that can take on multiple numerical values depending on the data set information. This data is categorical, representing the counts of individuals in each gender group by race that tested yes to insomnia. Type of Visualization: Bar and column charts were used to visualize the distribution of race, age, and gender in the dataset. The column chart was selected because it is an effective and straightforward way to represent the distribution of categorical data across multiple subcategories. It allows for easy comparison of data points within each gender and age category and between the male and female groups that tested yes for insomnia. The vertical orientation of the bars makes it simple to compare the different racial groups visually, especially when the chart contains many categories. In addition, the use of color, shape, and size can be used strategically in visualization to increase engagement and enhance the understanding of the data. Color: The use of consistent colors for each racial group in the column chart helps the audience quickly associate each group with a specific color. This consistency aids in easy comparison and recognition of the data. Shape: While the column chart does not explicitly use shapes, in other visualizations, different shapes can be used to represent distinct categories. For example, if there
3 were additional types of data (e.g., different products or departments), unique shapes could be employed to differentiate them, increasing visual appeal and comprehension. Size: In some visualizations, size can be used to represent a quantitative variable. This data set was a lot smaller than hoped for, but it helped provide a good visual. Managerial Importance of the Visual: The column chart communicates critical managerial insights related to the racial composition within different gender and age categories testing for insomnia at a young age. This information can be of importance to managers and decision- makers in various ways when looking to hire young adults. Overall, the chart provides an easily interpretable overview of the dataset. 3. Discuss your thoughts on data visualizations prior to taking this course. How has this course changed your thoughts on data, data analysis, data visualizations, and their impact on business? Before taking any course or learning about data visualizations, I had very limited exposure or understanding of their importance. The only data visualizations I was familiar with were the pie charts and only because, I had to create simple table charts and graphs to represent data in a basic way, without fully comprehending their significance in conveying insights and supporting decision-making through visuals such as color, shape, and size as I do now. This course has taken me out of my comfort zone with Excel and has helped me realize the type of impact data, data analysis, and data visualization can have in any business workplace. By utilizing data analysis and visualizations, managers can make more informed and data-driven decisions. Converting raw data into meaningful visuals that help identify patterns, trends, and outliers can allow managers to make strategic choices based on concrete evidence. Instead of analyzing lengthy spreadsheets or reports, managers can interpret visualizations efficiently, saving time and facilitating rapid decision-making that can make a bigger and more positive impact in their organizations. These insights can lead to more accurate predictions and help companies stay ahead of the competition. Ultimately, understanding and embracing data-driven practices and incorporating effective data visualizations can lead to improved efficiency, competitiveness, and overall success in the modern business world.
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