Every year we conduct two global, independent developer surveys engaging more than 30,000 developers. We track development trends across platforms, revenues, apps, tools, languages etc. The 18th Developer Economics survey ran from November 2019 to February 2020 with more than 17,000 developers and tech-makers participating, allowing us to analyze and understand development trends on major […]
Which programming languages the developer nation uses the most? Our data reveal which programming language communities are rising faster than others, which are dropping down the rankings, and which are the new additions to the club! Take a look at our infographic containing key findings from our Developer Economics Q4 2019 survey. First of all, […]
Did you know that more developers are team players than loners? If you are like us, you probably love these kinds of facts. You can find more of these in our State of Developer Nation Report (SoN). This edition includes a chapter on Developer Psychographics. The SoN report comes as a result of our Developer Economics […]
For the first time in our Q2 2019 Developer Economics survey, we tried to introduce developers in their own words by asking them about how they see themselves. We provided a set of 21 words and asked them to choose up to five to form a word sketch of their personality. We also gave them […]
Every six months, the Developer Economics Survey captures the voice of more than 20,000 developers globally, across mobile, desktop, IoT, cloud, web, game, AR/VR and machine learning development and data science.
First of all – thank you. Thank you for taking, or even for just considering taking, our Developer Economics Survey. Some of you have given us feedback (yes, we do read all of it!) asking what the survey is about, where we use the data, why we do this, and “who are you people anyway”? […]
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.
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?