Is a career in data right for you? Dive into answering this question through data.

If you’ve done a google search regarding careers in data, you may have seen headlines such as, “Sexiest job of the 20th century: data scientist,” or “The need for data professionals is expected to grow exponentially.” Indeed, reading such statements enticed me to take a closer look at careers in data.

While I’ve worked with data for several years on a nearly daily basis as an academic researcher in training, I knew very little about data careers in the industry. What better way to learn more about this than to go through actual data itself?

Here were the research questions I posed:

What are the common jobs in the field?

What are the general job aspects?

What educational background and skills are required?

Who stands to benefit from this information?

While I conceptualized the project with my own questions in mind, I am not the only one who stands to benefit from this information. Others who find themselves considering careers in data can use this information for a quick overview of what to expect, benefit- and requirement-wise. Existing data professionals may also find this information helpful because they will be able to see how they stack up to others, or even compare across different roles and countries if they plan on making a role change or relocate.

How do I answer my question?
My first step was to find a dataset that contained the relevant information to answer my questions. Lately, I’ve been troubleshooting a lot of my data-related questions on a site called Stack Overflow. Many helpful suggestions from experts in data, or even just data enthusiasts can be found there. Something the site does annually is conduct a survey among its users. Though a lot of Stack Overflowers are developers/programmers/computer scientists, a decent share of them are data professionals. I decided to capitalize on this dataset and learn about data careers from the Stack Overflow community of data professionals. Though it is important to note that a sizable amount of the community are from a computer science background, which could introduce some bias.

After the data were downloaded, cleaned, and wrangled, I opted to create a Tableau story with 3 dashboards of several visuals that touched on the following broad topics in dashboard 1: an introduction to the field and common roles; dashboard 2: overall role environment (e.g., benefits, work life balance); and in dashboard 3: overall role requirements (e.g., education, job-related tools/skills).

I present to you, the Tableau Story:
(or click here to view via Tableau Public)

Data and design decisions.

Because my research question was a three part one, I thought a story made up of 3 dashboards would be most appropriate. On the latter 2 dashboards, you can filter all of the visualizations by role and country.

For design elements, I chose to use a modern looking clean, but bold title in the form of a question because that’s what the story is trying to accomplish, to answer the question. I accompanied the titles with vectors of data analytics-themed graphics. The color scheme of the story is in a cool tone and kept consistent throughout.

First, I added a brief summary to introduce the field of data analytics, why it might be of interest and a statement on why the viewer might benefit from the visual.

Dashboard 1: In this first dashboard, have a brief summary on the upper right to tell the viewer about the field and the purpose of the project. I chose to make a wordcloud of the data roles in the dataset so the viewer has a quick glance of the most common roles, where the larger the font, the more popular. If they are interested in learning more about a particular job, they can hover to reveal a description and the number of individuals with that title in the dataset. I kept this first dashboard simple as to not overwhelm the viewer with information.

Dashboard 2: Those who are curious to learn more will then proceed to the next dashboard where more information is presented. I titled the second dashboard, “So, you want a career in data?,” because those who are most interested in this information are probably trying to answer that question for themselves. Each of the charts and figures in this dashboard contain information that I considered to be highly important factors when thinking about a career. I provided this context for the viewer in a brief summary on the top-right. I also put two dynamic filters so that the viewer can view the information based on a particular role and country.

I chose to use big bolded numeric values to visualize the salary and work hours. Typically when people consider a job, these are some of the top influencing factors. I felt that this representation made the numbers really stand out. To get more details about other environment factors, I used different chart types. A treemap was used to depict the distribution of professionals across companies of varying organization size. A bar chart was used to show the percentage of data professionals and their overtime requirements. Finally, a bubble chart was used to represent job satisfaction ratings among data professionals. I turned the bubbles into faces corresponding to the rating scale.

Dashboard 3: After the viewer is finished with the 2nd dashboard, they may proceed to this final dashboard, which gives them insight on how to become a data professional, as the title states. Like the previous dashboards, there is a summary on the upper right with context. I chose to list educational requirements first because typically people focus on that before the skills required.

For the skills, I chose to use big bolded numbers for emphasis, as these are generally quite important job requirements. Though the last visualization of the top programming languages, I chose to do sort of similar to a bubble chart, where the logos of the languages are shown. Though size couldn’t be utilized in this chart due to the nature of the data that was given.

Final thoughts.

Overall, I am satisfied with the way the visualization came out. I was able to draw insights from the visual and so were my peers. I think keeping it simple helped with the ease of understandability even if I was not present to provide any context. There were additional questions about the career I wish I could include, but was not able to because the dataset did not have the information available. Compiling the data for this project made me realize how much or how little we can answer our research questions depends on how the experiment or survey was conducted. You have to work with what you’re given.However, I think the charts and figures that I was able to incorporate provided a concise snapshot of careers in data and will be useful for the aforementioned audiences. Moving forward, I would love to do a more comprehensive version of this visualization such as incorporating time, where I would download the dataset from previous years, which may tell us about the temporal trends of the field. I hope people who stumble upon my visualization are able to benefit from what I visualized and make a data-informed decision for themselves, just as I have.