The data science (DS), machine learning (ML), and artificial intelligence (AI) field is adapting and expanding. From the ubiquity of data science in driving business insights, to AI’s facial recognition and autonomous vehicles, data is fast becoming the currency of this century. This post will help you to learn more about this data and the profile of the developers who work with it.

In this blog post we’ll explore where ML developers run their app or project’s code, and how it differs based on how they are involved in machine learning/AI, what they’re using it for, as well as which algorithms and frameworks they’re using.

The web echoes with cries for help with learning data science. “How do I get started?”. “Which are the must-know algorithms?”. “Can someone point me to best resources for deep learning?”. In response, a bustling ecosystem has sprung to life around learning resources of all shapes and sizes. Are the skills to unlock the deepest secrets of deep learning what emerging data scientists truly need though? Our research has consistently shown that only a minority of data scientists are in need of highly performing predictive models, while most would benefit from learning how to decide whether to build an algorithm or not and how to make sense of it, rather than how to actually build one.

Here are some of the most interesting insights from the latest State of the Developer Nation 15th edition, based on the data from 20,500+ developers in 167 countries, who took part in our Developer Economics survey in May-June this year. We reveal top skills developers want to learn in 2019, the most popular programming languages globally, and to how many developers are big data and real-time predictions relevant.

Q&A sites and data science forums are buzzing with the same questions over and over again: I’m new in data science, what language should I learn? What’s the best language for machine learning?