A Continued Exploration of the Data Analytics, Product Design, UX Design, and UX Research Fields


Charts & Graphs, Final Projects, Visualization

Background

In my previous lab report, I utilized ArcGIS to visualize data across four different fields I am interested in – Data Analytics, Product Design, UX Design, and UX Research – in order to determine the best states to live in for each of these fields. More specifically, my goal was to find which states had the highest starting and average salaries in each field as well as the highest number of jobs available. Furthermore, I also explored which states would allow me to save at least 10% of my salary when starting off in either of these fields and at least 20% of my salary once I gain more experience and start earning an average salary. 

Due to the difficulty of comparing these variables across all four fields in my previous lab report, this report aims to develop a more seamless comparison using Tableau dashboards. Doing so would allow me to determine whether there is a state that has an abundance of opportunities across all four fields and that I can live comfortably in no matter which field I decide to go into. I am also interested in comparing how much I could save in each field depending on where I live and whether being in that specific field and location would cause me to go into debt after factoring in average expenditures in each state. 

Since I am still at a stage in my career where I am unsure which of these fields I prefer the most, my overall goal for this project is to see which states would allow me to flexibly move between these fields and whether these results include the locations I would prefer to live in the most: New York, California, Washington, and D.C. Moreover, I am also curious to see which field is best suited for me based on the factors I am considering. 

Note: for simplification purposes, the term “states” in this report includes the District of Columbia

Methods

Please read the methods section of my previous lab report here for more details about my data collection process and how I calculated the average annual expenditure in each state as well as the estimated savings in each state on both a starting and average salary in each field. 

The maps I created on ArcGIS from my previous lab report added limitations on what I could visualize at once, meaning I would have to hide one field layer in order to see the relationship between job availability and the starting or average salary within each state in another field. Thus, I decided to utilize a Tableau dashboard to make it easier to compare the fields with each other. More specifically, I imported the datasets used in my previous lab report into Tableau to create a tree map for each field. Within each tree map, I included the total number of jobs in each state, indicated by the size of the rectangles, and the average salary in each state, indicated by the color of the rectangles. I adjusted the color range for each field’s tree map to have the same minimum and maximum average salary value in order to allow for a more accurate comparison across all the fields. More specifically, I used the lowest average salary across all four fields as the minimum average salary value and vice versa. 

I was then faced with an obstacle due to my inability to adjust the size of the rectangles in each tree map to create an intuitive comparison between the number of jobs available in a specific state across all four fields. To work around this limitation, I added a selection feature on the dashboard. To elaborate, when you click on a specific state in either of the tree maps, it expands the state (or rectangle) in the rest of the tree maps and remains static. This allows you to hover over each one and view the tooltips (or pop-ups) that provide information on the variables, providing a more seamless comparison. I also added a highlight feature (search bar) to select the “State” variable across all four datasets, meaning if you select a state from the dropdown menu, it will highlight the corresponding rectangle across all the fields in order to compare the color of the rectangles and the placement of the rectangles on each map. The rectangle’s placement on each field tree map (just like the size of the rectangle) indicates the job availability in that state relative to the other states in that field. Furthermore, I added a hover feature which does the same action as the highlight feature to add different methods of interaction within the dashboard. 

To determine the best field overall in terms of average salary and job availability, I calculated the average annual salary across all the states in the U.S. and the total number of jobs in the U.S. for each field within separate tables and added them to the Tableau dashboard. 

I then created two separate bar charts to visualize how much money I would have left in savings after subtracting the average annual expenditure for a single person in each state from both the starting and average salaries in each field and corresponding state. The first bar chart visualizes savings on a starting salary in each field, and the second visualizes savings on an average salary. I developed a second dashboard to visualize these charts side by side and added different interactive features to allow for an easy comparison between the estimated savings on a starting salary versus an average salary across each state and field. 

Results

Figure 1: Dashboard comparing the total number of jobs and average annual salary in each field by state as well as the overall average salary in the U.S. and total number of jobs in the U.S. in each field.

To interact with the first dashboard, click here.

Figure 2: Dashboard comparing the estimated savings on a starting versus average salary within each field and state.

To interact with the second dashboard, click here.

As seen in Figure 2, if I were to factor in the average annual expenditure for a single person in each state, there are several states I would not even be able to afford living in with a starting salary. However, this greatly differs depending on the field. Earning a starting salary as a Data Analyst significantly limits the options of where I can live comfortably and even afford to live compared to the rest of the fields. Moreover, I would have less money to put into savings as a Data Analyst earning an average salary compared to the rest of the fields in more than 90% of the states. 

Overall, Hawaii and Massachusetts are the only two states that would leave me in debt on a starting salary in all of the fields. However, once I start earning an average salary in any of these fields, I would be able to afford living in Massachusetts, but would still be in debt in Hawaii no matter what field I choose. The only other state that would leave me in debt while earning an average salary is Vermont and potentially North Dakota, but only if I were to be a Data Analyst in those states. 

Since I want to keep my options open for all of the fields, the only states that would allow me to do so without going into debt are Alabama, Illinois, Iowa, Kansas, Michigan, Minnesota, Missouri, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Texas, Virginia, and Wisconsin. Unfortunately, my most desirable locations to live in (New York, California, Washington, and D.C.) do not fit into that list. If I decide that living in one of these locations is the most important factor, I would have to choose a specific field within each of these states that would not leave me in debt on a starting salary. For example, I would be comfortable living in Washington as a Product Designer, UX Designer, or UX Researcher, but would not be able to afford living there as a Data Analyst with a starting salary. Thus, if I wanted to move to Washington right after graduate school, I would have to stick to looking for jobs in the Product Design, UX Design, or UX Research fields unless I am able to find a Data Analyst position that pays more than the average starting salary in Washington. 

After interpreting my visualizations in Figure 1, I was able to come to a conclusion that California and New York are the best options among my desired locations in terms of both job availability and average salary no matter which field I go into. Moreover, California and New York are the best states in the U.S. as a whole for both UX Design and UX Research in terms of those same variables. According to Figure 2, both of these states would also allow me to save a considerable amount of money (over 24%) as a Product Designer, UX Designer, or UX Researcher earning an average salary. However, I would be in debt if I were earning a starting salary as a Data Analyst in California and also as a Data Analyst, Product Designer, and potentially even a UX Designer in New York. I would also only be saving around 16% of my salary if I were to be a Data Analyst earning an average salary in both California and New York. This is less than my ideal goal of saving at least 20% once I start earning an average salary or higher, as mentioned in my previous lab report. 

Overall, Data Analytics is the best field in terms of job availability, but the worst in terms of salary. In contrast, UX Research is the best field in terms of salary and estimated savings throughout the majority of the U.S. compared to the other three fields, but the worst field in terms of job availability, meaning it is the most competitive field of the four. Fortunately, the UX Research field is projected to grow 20% between 2018 to 2028, even more so than the other three fields, which would therefore increase my chances of being considered for a UX Research job down the road. 

Future Considerations

It would be unsurprising to see a huge variation in all the factors I am analyzing if I were to break it down by city rather than state. Thus, a future analysis could compare specific cities I am interested in to further help with my decision making process. Furthermore, I could dive even deeper to analyze other factors that may impact my decision, such as projected growth for all of the fields, gender distribution between each field, the gender wage gap in each state and field, whether I would earn more than the average starting salary in each state or city since I have a graduate degree (making it less likely for me to go into debt and thus increasing my opportunities of where I could live), and many more.

Resources

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