Data visualisation infographic with insights on salary level of data scientists - how to create the JavaScript dashboard and analyse its data - javascript infographic
An infographic is one of the most effective ways to share data because it helps simplify information. Just by briefly looking at a well-made infographic, you can get an overall understanding of the subject as well as catch some interesting, and not obvious, insights.
An infographic has two main aspects - content and visuals. The information included needs to be essential for the topic at hand. It should also be concise, leaving long paragraphs of text for textbooks. The look of an infographic is not any less important than the data it contains since visuals impact readers’ attention and as such how well the information is understood. Infographics can also be interactive. This is very useful, for example, if the goal is to analyze something. Interactive charts allow us to include more data in the infographic, making it even more convenient to work with.I recently researched data science salaries and thought it was a perfect subject for an infographic. It has a lot of data that needs to be analyzed and, in my opinion, is perfect for visualization. Data science is a rapidly growing field, and salary is a hot topic for all professionals regardless of their experience, which means this is not only interesting to talk about but also relevant.Flexmonster . Highcharts is one of the most popular charting libraries, and Flexmonster is a pivot grid with good easy-to-use data analytical features.Looking at every possible data science position and its salary is, firstly, not achievable, and secondly, not the most effective way to retract useful information. To see overall tendencies instead of particular cases, we need some measures to analyze the data against.job title, years of experience, region, industry, and, unfortunately, gender of a particular professionalBefore we compare, note that all the salaries in this article are listed in USD.As the data science field grows, it creates increasingly more jobs. You can often encounter different descriptions for positions with the same job titles. But just because there are too many specialties in data science doesn’t mean it’s impossible to estimate the salary for some of them.Data scientist, data analyst, data architect, data engineer, machine learning engineer, machine learning scientist, business intelligence developer, and database administrator.data for these specialties in the US, machine learning engineering is the highest-paid job in data science today, with the average annual salary being 127k. The next two in the ranking are machine learning scientist and data scientist positions, with around 120k and 118k annually. The chart on global average salary shows that though wage numbers may differ drastically, specialty rankings stay the same with machine learning as the highest-paid job., average annual wages for machine learning engineers and data scientists in the US equal 113k and 97,5k, accordingly, and for machine learning scientists, there’s no data. But since the machine learning specialists are ranked by salary the highest there, too, it’s safe to stick to only one data source for analysis. Remember that the wages you encounter later may vary from the data shown in this or any article on salaries.The more experience you have in a field, the higher are salary expectations. Let’s look at the chart of salary progression based on data from PayScale, Salary Expert, and Glassdoor. For less than a year of experience, all three sources report the average annual salary to be a little below 90k. But for professionals with 20+ years of experience, numbers differ.data, salary almost doubles and equals 174k dollars a year. On the other hand, salary Expert and PayScale’s reported wages are much less, 147k and 138k annually. This means that salary progression throughout the years of a data scientist’s career depends on much more factors than just experience. And from the second chart, we can tell that with each level of experience, salary expectations grow similarly for all specialties.As the first tool to analyze salary data in different countries collected from Salary Expert, I wanted to use a pivot table as it has some great and easy-to-use analytical features. I was interested in which countries out of 25 present in this dataset have annual wages above 90k and which ones fall below 25k. After applying conditional formatting, it seems that those two categories have about the same number of countries. But after sorting countries in descending order by value, it’s clear that the only countries where the average salary for every listed data science job is above 90k are the United States and Australia. While as many as four countries have annual wages only under 25k USD: Russia, India, the Philippines, and Turkey. Since Flexmonster Pivot integrates well with Highcharts, I used data from the pivot table to build line charts on top-5 and bottom-5 countries by salary expectations for data scientists. At the top, there’s the US, Australia, Germany, Canada, and Finland, and the bottom 5 are Russia, India, Ukraine, the Philippines, and Turkey.Data scientists are required in almost every industry, so knowing which ones pay the most makes sense. According to O’Reilly’s 2021 AI survey data, the highest salaries are in tech industries. Professionals working there reported the average salary to be from 164k to up to 171k. The worst paid are specialists in education with an annual wage of a little over 100k, which is still not that bad.Unfortunately, the gender pay gap still exists, so it’s important to consider it. According to the O'Reilly survey, in data science, women are paid 7 to 20 percent less compared to their male colleagues with the same job titles, regardless of their education level. As shown on the chart, although women can still get higher positions at work, they are often noticeably underpaid.Overall salary expectations here are pretty high, which makes pursuing a career in data science desirable for more and more people every day. But for more specific payment information, you need to consider several factors like job, experience, region, and industry. Unfortunately, professionals’ gender can also drastically influence their paycheck, but I hope it’s temporary and the situation will change over time.Infographics are one of the most effective ways to display data. This example was created to analyze data science salaries depending on different factors. Creating an infographic is easy, thanks to many available libraries and tools. I used different charts and a pivot table in this example, which helped structure and simplify information. It also made concluding the data much easier.
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