Analytics matter more than ever: that’s the big takeaway from this month’s breakdown of data from Burning Glass’s NOVA platform, which analyzes millions of active job postings.

As with previous months, mathematician tops the list, followed by data engineers. This time around, job postings for mathematicians grew 80.9 percent year-over-year in October—an acceleration from the already-blistering 70.9 percent growth in September. It’s clear from this breakdown that companies really want employees who not only analyze valuable datasets, but also turn it into algorithms and insights.

Here’s the full chart below; as you can see, jobs that demand some kind of analysis—data engineer, data scientist, biostatistician, and so on—dominated the top 20. (Click that little button in the upper-right corner to see the back half of the list, by the way.)

Data engineers came in second behind mathematicians, and it’s easy to see why. As companies continue to recognize the value of their enormous datasets, they need employees who can help store, order, and retrieve that information in useful ways. Data engineers may find themselves constructing huge repositories, monitoring the movement and “health” of data throughout a company’s systems, and proposing the adoption of new technologies that make it all easier for other stakeholders to analyze and use.

To accomplish those ends, data engineers rely on a variety of tools, including Hadoop, Docker, Scala, Apache Spark, and Kubernetes. Because they must communicate with people throughout an organization, they must also have excellent “soft skills.” It’s one thing to architect an enormous system; but can you explain that system in “plain language” to someone who can barely handle iPhone updates? 

If you want to break into a profession that hinges on analytics, some experts suggest you start by learning Python, which is supplanting R as the programming language for number-crunching applications (plus, since it’s a generalist language, you can use it for many other things, as well). Depending on your specific interests, the tools and platforms you’ll want to learn will necessarily change; for example, the apps used by a geographer or GIS specialist are radically different from those used by a business intelligence architect (in many cases).

And as always, keep in mind that specialization is key. When word gets out that a particular profession is “hot,” that compels a new generation of students to gravitate toward it. In order to stand out from the growing crowd within a particular field, you’ll need to specialize in those cutting-edge technologies that companies will need tomorrow—things like artificial intelligence (A.I.) and machine learning.