Data Visualization Challenge: Job Opportunities in Data Science & Analytics

Our goal for the project is to create a dashboard that can help students gather more information on types of jobs in the field. This guide can give information on the compensation of positions at each level, as well as geographical information tied to the salary data. We created visualizations that we thought would be helpful to give students or prospective professionals in the field of data science a way to make an informed decision on the types of jobs they apply to.

Visualizations

Note: The visualizations below are screenshots of our work created in Tableau. Here is a link to our published Tableau Dashboard. The visualizations are interactive on the dashboard. (We have also submitted a Tableau Workbook)

Average Salary By Location & Job Title

The map highlights the average salary for each city given in the dataset by job title. A few observations were excluded from the density map due to ambiguity in city names that exist in multiple countries. The darker data points on the map depict the cities that have higher salaries for the given job title. Careers in Data Analytics show to have the highest salaries in the US as well as the UK, which was different from the other job titles. Our map highlighted that US cities, specifically in California and the east side, consistently showed to have the highest average salaries for each job title. We found both Machine learning and Machine Learning Operations to be surprising. According to our dataset, Mexico City offered the highest salaries for careers in Machine Learning. We had the fewest observations recorded for Machine Learning Operations which made it difficult to get an accurate understanding of cities that offer the highest salaries or trends based on the global region. However, similar to most other job titles, we saw the highest salaries in the US eastside.

Average Salary By Title & Experience

For the second visualization, we wanted to get some insight into how the different data jobs pay at a variety of experience levels. It is important to consider how an entry level position might pay but more importantly how ones compensation is projected to grow over the course of someones working life. One useful example was the compensation path for a data analyst. Starting pay was around 30k which was close to the lowest average starting wage. The interesting part was that the compensation quickly grows. Once someone hits mid level they almost double their earnings and earn approximately 56k. That was good for second highest of the positions we analyzed. This visualization created valuable insights for me and my group members to be able to make informed decisions on the path we want to take. The general trend showed that the more experience an individual has, the more money that they will make. Regardless of the niche, experience is the most important thing when it comes to salary expectations.

Total Salary in the Market Per Job VS Average Salary Per Job

This visualization is important because it gives us an idea of the amount of jobs in the market. By comparing the total amount of money in each job to the average salary of each job, we can see disparities in the data to help us draw conclusions. For example, if you look at the Salary (USD) for ML Ops, you can see that the total money amount is around 1.4 million, while the average salary is around 82,000. If we just looked at the average salary, a student might see ML Ops and pursue it without realizing that there isn’t as much opportunity as in other positions. On the other hand, if you look at becoming a data scientist, although the average salary is only about 66,500, the total amount of money is around 24million which is over 17 times the amount of ML Ops. There are a lot more positions and a lot more money being put into the data scientist positions, which most likely means that there is a relatively higher chance for workers to land a job in this field. 

If you look at the top (salary USD) from left to right you can see that the amount of money in the market for each job decreases, and because the average salaries do not deviate too much from each other, we can conclude that this roughly shows a decrease in the amount job positions per job title. With this we can see a sort of general market share for each job title in the data science field.

Entry Level Employees By Job Title

The final chart shows the entry level positions/employees based on the job title and field. To further show our point when it came to positions and salaries, we can see that data analyst, even if it isn’t the field that earns the most, it has the most roles and positions, almost about half. It is followed by the data scientist entry level positions and the machine learning roles. Both of those fields have a pay bump in their entry level positions, but they also have less roles to fill.

The most difficult roles to find an entry level position in are the machine learning operations and big data. They have 2 and 4 roles within our data-set respectively. On the other end, they do also come with a significant pay bump for the entry roles. As we were looking at the data, we inferred that the pay bump and the significantly less amount of roles may be due to both big data and machine learning operations could require a more niche and focused skill set.

The pie chart gave a good idea on which job field markets have more roles and which ones may require a more specific skill set.

Conclusion

The visualizations we have created are a tool that you can use to guide yourself through the job search journey. For newer professionals coming into the field, we would recommend looking and applying for a lot of entry level jobs in the data science/analytics positions. Although they do not pay as well as the other positions, there are a plentiful amount of positions in the market for these roles which means that you will have a higher chance of landing a role in these positions. After that, you can climb the ladder as you gain more experience. We hope that our visualizations were informative and helpful in your search for jobs in the data science field.