Community Languages News and Resources Platforms Tools

Current development trends in software engineering

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 areas such as mobile, cloud, desktop, IoT, web, augmented and virtual reality, machine learning and games. 

It’s no secret that we are data-enthusiasts. Data is in our DNA.

After each survey wave, we transform these data into graphs and insights and offer part of them as resources to our developer community. Our methodology is founded on 9 essential and non-negotiable qualities:  magnitude, impartiality, inclusivity, consistency, substantive, engagement, diligence, confidence and breadth. See more on how our methodology allows us to understand and profile developers.

Our goal is not only to help the world understand developers but also to add value to all the developers out there, by offering them the necessary insights to benchmark themselves and make smarter business decisions based on current development trends.

So let’s have a look at what our developers are saying, shall we?

Starting from some basic insights, it is important to know in which age group our respondents belong: 35% of developers worldwide are between 25 and 34 years old. The second largest demographic – almost 28%- is the young developers, aged 18 to 24 years old. 

What age group are you in?

Development trends

Just over half of our respondents reported having less than 5 years of coding experience. As our research covers both professionals and amateurs such as hobbyists and students, the experience mix makes perfect sense and is representative of the coding skills of the global developer population. We find that the young and relatively inexperienced are the first to jump into emerging sectors drawn by the hype, and they play a key role in their evolution.

How many years have you been working on software projects?

Development trends

Focusing on programming language preferences of mobile and backend developers, we find that Java is the third option for backend developers, while the most popular choice of mobile developers. The first choice of backend developers is instead Javascript with over half using it for cloud development. 

Which programming languages do you use to write code that runs on the device in your mobile apps?

development trends

Which programming languages do you use to write code that runs on the server?

development trends

When it comes to front-end frameworks or libraries for web applications most programmers use jQuery (49.7%) and Bootstrap (48%). Other frameworks our respondents stated they’re using are React (42.9%), Vue (28%) and Angular (2+) (25.2%). 

What about trends in augmented and virtual reality (AR/VR)? Almost half of the developers working on AR/VR use C#. Moreover, as is typical of a still-emerging sector, almost 60% of respondents said they are hobbyists in this field.
Last but not least game development. Developers mostly prefer to create adventure and action game apps with 44% of respondents choosing each of these. 36% create Arcade games while almost 23% choose Role Playing or Strategy games.

Which categories do your games fit in?

development trends

For more insights from our latest survey, you can check out the Developer Economics graphs dashboard. It’s also a great opportunity to benchmark yourself against the global average. 

Looking for a more thorough report analysing the developer population and trends? Download our next State of the Developers Nation report 18th Edition. You will find it here.


Infographic: Top programming language communities

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, let’s all hail for our two years in a row queen, ? JavaScript. The JavaScript community counts more than 12 million users worldwide with an increase of 33% over the last two years.

Among the top programming languages, Python and Kotlin have climbed up faster than any other. With a slow and steady rise Python finally managed to edge out Java, counting 8.4 million users and ranking as the second most used language. When it comes to Machine Learning, Python is the first choice of the developer community, chosen from more than 70% of developers involved in ML. Meanwhile, Kotlin has shown significant growth, it nearly doubled in size in the past two years, finding its way into mobile and AR/VR programming.

After almost 10 years of its launch date and a head to head race with Ruby, Go (or Golang) managed to enter the club of the top 10 most used languages, counting 1.4 million users. Another up and coming language making its way mostly through the AR/VR field is Rust exceeding half of million users.

Let’s not forget that developers are dropping languages all the time. The practice of programming is not static. Even though Swift and Objective-C have been used significantly by the Apple community it seems that the developers are slowly abandoning them. On a similar trend, Ruby and Lua seem to have the biggest decrease (30% & 40%).

Check out our infographic which highlights the top trending programming language communities:

programming language communities

The estimates we present here look at active software developers using each programming language, across the globe and across all kinds of programmers.

Looking for a more thorough report on programming language communities? Check out our free State of the Developer Nation Q4 2019 report examining also different topics such as Contribution to Open-Source Software, DevOps Participants and Adoption, Machine Learning, Augmented & Virtual reality and Emerging technologies.

Also, here you can view the latest global average data trends on major development areas.


Developer Psychographics: Curious & Creative Problem-Solvers

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 surveys. These are answered annually by tens of thousands of developers, from all over the world. Therefore, these are some of the most reliable reports globally when it comes to developer trends.

The developer community is one of the most developing (see what I did there?) communities in the world. Changes are constant and sometimes, unpredictable. That is why our surveys are as inclusive and all-around as they can be, so that we don’t miss a single thing and we are always up to date.

Developer Psychographics: How do developers describe themselves?

This differs depending on their technology sector, their age and more. For instance, most developers see themselves as Logical persons. However, those working on game development see themselves mainly as Gamers (obviously).


Younger developers mainly see themselves as Gamers. On the other hand, those over 35 years old are more likely to self identify as Readers. In addition, “Gamers” are the least popular term amongst developers aged 45-54 years old.


During the Developer Economics Q2 2019 Survey, we offered respondents over 20 words to create a word sketch that would best describe themselves. Participants could use up to five words and also provide their own descriptions in a text field. We received over 300 responses which ranged from “analytical” to “zoned out”.

It would seem that we are amongst some Curious, Enthusiastic, Friendly, Geeks, and the occasional Innovative, Lazy Leaders Developers.

A few developers pondered if we were asking these questions in an effort to set up a dating site (we are not!). Speaking of love,  however, we found a few developers who love dancing, love the future, love puzzles and music.

Finally, we discovered that a lizard person had answered the survey.

This will surely help our research in years to come!


If you enjoyed this post, you can read the first part of our Developer Psychographics here: How developers see themselves . We also created a webinar on this topic, which is now available on YouTube. Check it out: Developer Personas and Psychographics.

The full State of the Developer Nation report, 17th Edition, with more interesting facts like these ones, is available here.

Join our community for more

Our next report will be out soon, including all the latest trends of the developer world. Why not sign up in our community and receive it right in your inbox?

Our Developer Economics newsletters are fortnightly, include useful developer resources and news, always start with dev humor and are 100% spam free.

Till our next survey! 


How developers see themselves?

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 the opportunity to provide their own text description.

Here’s what we got:


Over half of the developers say they are logical

Perhaps unsurprisingly, nearly six out of ten developers say they are logical. And as it turns out this is the most popular choice of description across all software development sectors, except in games development.  Next in line, but some way behind, are the descriptors team player and introvert at 37% each. By comparison, just 10% label themselves as an extrovert. But can you guess which programmers consider themselves less introvert? Those involved in AR/VR and IoT sector. Interesting, right?

Moving on to a slightly more unusual pair of labels: there are slightly more dog lovers than cat people in the developer population, although the numbers are close at 15% and 13% respectively. A much greater difference seems to exist though between developers working at night (night owls, 29%) and those who prefer the fresh morning breeze (early birds, 14%).  



What about hobbies and spare time?

A third (33%) of developers say they are a reader, which makes it the most popular choice of description related to spare-time activities. It is closely followed by 31% who say they are a gamer. Our data shows that developers tend to perceive themselves differently as they grow older. More than one in three developers up to the age of 34 years consider themselves to be a gamer, compared to fewer than one in four of the 35-44 age group, and fewer than one in five of the 45-54-years.  Older programmers are more likely to describe themselves as readers.


What’s this “real life” you’re talking about like? Is it similar to WoW? Does it run on a 64 bit OS?”

Other activities such as
music and sport score lower, at 20% and 17%. A low 7% make LEGO models, although the popularity of LEGO seems to be very much dependent upon age. A respectable 12% of developers under 18 make LEGO models, but the proportion halves to 6% within the age group 18-24. 

What about the artistic ones?

Even though a developer’s work demands a high level of creativity, just 14% use “artistic” to describe themselves. Those involved in games or in augmented reality and virtual reality development are far more likely than others to use this word to describe themselves. 21% of game developers and about 25% of AR/VR developers see themselves as artistic, as compared to 16% or less of desktop, web and backend developers. 

Lastly, in out Q2 2019 Developer Economics survey, a few programmers were confused as to why we were asking the question and pondered if we were trying to set up a dating site. Well, we weren’t! We were collecting the data to create the State of the Developer Nation Report, 17th Edition.

Interested in joining forces with 40,000 developers worldwide in shaping the future of the developer ecosystem? Take our survey.


Community Platforms

Decoding development trends: The 17th State of the Developer Nation Report is out

Every six months, the Developer Economics Survey captures the voice of more than 20,000 developers globally. Our surveys engage developers working across mobile, desktop, IoT, cloud, web, game, AR/VR, machine learning development and data science, decoding development trends.

The 17th Developer Economics survey ran between June and  August 2019. The data analysed provided really interesting insights about the different developer profiles out there.

For instance, one in three developers are all-rounders. Only one in five declare themselves as specialists. There are almost four times as many introverts (37%) as extroverts (10%) among developers. This is a significant difference from the 2:1 ratio in favour of extroverts found in the wider community.

We also included several unusual labels, uncovering, for example, that there are double the number of night owl developers than early birds (29% compared to 14%).. What time is it with you right now?

2X night owl developers compared to early birds (29% compared to 14%

Javascript remains the Queen

Looking, into programming language trends we found that JavaScript remains the queen with a community of over 11M active developers. On the second tier we have Java (6.9M) and Python (6.8M).

Our data challenges the assumption that developers’ language use is relatively stable over time. Instead, it seems that developers drop and adopt new languages all the time, depending on their needs and on their running projects.

Kotlin is the rising star among programming languages. It moved up from 11th to 8th place in just a year.

Growing interest and adoption in 5 emerging technologies

We saw a significant increase in developers’ involvement and adoption of five technologies in the 6 month period ending Q2 2019. These are DevOps, mini-apps, computer vision, cryptocurrencies, and fog/edge computing. For DevOps in particular, the percentage of developers who are either interested in it, learning about it, or have already adopted it increased from 66% to 70%.

Computer vision, on the other hand, saw a noticeable growth in the number of developers involved in it.  Meanwhile, the share of those developers who are actually adopting it increased only slightly.

Interest in robotics and quantum computing also increased.

However, the share of interested developers that are working on the technology dropped.

ŸInterest and adoption in blockchain applications other than cryptocurrency, conversational platforms/voice search, drones and biometric technologies remains constant.

Streaming games and extending reality

ŸJust 16% of professional and 10% of hobbyist game developers say they are actively working on designing games for streamers to live-stream their gameplay to an audience. Gameplay streaming is mostly associated with brand promotion and revenue generation. Therefore, the difference between professional and hobbyist interest is to be expected.

One in five AR/VR game developers design for gameplay streaming. This might be because they are the most comfortable with different models for their games, on emerging hardware and across new business channels.

Decoding development trends across regions and screens

  • 2 out of 5 app developers in Asia build apps for messaging platforms and/or chatbots.
  • 34% of mobile developers used cross-platform frameworks in the last 12 months (40% of professional mobile developers, 33% of hobbyists and students).
  • Almost one in four mobile developers opt to use React Native.
  • 31% of mobile developers whose primary target is iOS are using React Native. This compares with 21% of those who primarily target Android.

You can read the full State of the Developer Nation report here.

We look forward to decoding development trends in our next report. You can help shape the trends by taking the 18th Developer Economics survey here!


Dear all taking our Developer Economics surveys – or wondering why you should

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”? Right. About time we provided a comprehensive answer then! Transparency is, after all, one of our core values.

  • “Be more transparent about how you will use the data, who you will sell it to, how much you intend to spam me, and why, exactly, are you offering a range of inducements at later stages”
  • “Explaining a bit more what is this for. :)”
  • “More detailed description of your activities and details of cooperation with you for new users.”
  • “You just started to ask questions w/o sharing why you are asking your questions… Why?”
  • “It’s a little hard to be sure who this data is for. It seemed like it came from Mozilla, but got so many questions about Microsoft it made we wonder!”
  • “It’s cool but needs to be explained in more detail”

Our mission is to help the world understand and support developers.

In this way, we aim to contribute to evolving technology in all the ways that matter to developers and, consequently, to end users too. The Developer Economics surveys are our means of doing just that. Yes, of course we sell the insights and the anonymised aggregate data in the process, as we also need to make a living somehow. But out of all the ways in which we could be making a living, we very consciously choose this one, as we are a team of people who first of all strive to make this world a better place in the infinitesimal ways that we can, and this is our very own geeky way of doing so. We are sworn data geeks, or as our marketing team more elegantly puts it: Data is in our DNA.

Now, as to who our data and insights go to: Our client base includes the leading tech organisations, such as Microsoft, Intel, Google, Amazon, Facebook, Mozilla and many more. We take pride in supporting them to design future technologies around actual developer needs and wants – your own needs and wants. So please be truthful in your answers, or you may lead the decision makers, and therefore your development tools, in a very wrong direction!  

Not all of our data is behind a paywall though. 

As a thank you to all of you who contribute to our life’s work, we release our free State of the Developer Nation report, filled with what we hope is valuable information for all developers out there, whether professionals, hobbyists, or just students on the onset of their exciting journey in the world of software development.

There are also free interactive graphs that aim to help you benchmark yourself (or rather, your technology choices) against the rest of the community. Check out the resources space on our Developer Economics website for all the data goodies we have to offer. 

It’s not just data we give back to the community. 

For the past three surveys and for every qualified response that you provided us, we have been donating $0.10 to a good cause within the developer world. In previous years we supported the Raspberry Pi foundation, and at the same time asked you to tell us where you think our contributions would count the most.

Many of you suggested we should support women in coding, and also developers in Africa. Combining the two suggestions, this time around, and for every qualified response that you provide us, we donate $0.10 to the South African Chapter of Women in Big Data. 

Thank you for making this happen!

  • “How are you supporting female developers?”
  • “Help the developers in west Africa gain the knowledge we desire.”
  • “It would be great if sub-sahara African countries could get more attention and accessibility to internship with all these companies.”
  • “Just want to suggest that you consider investing in Nigeria as the youth are passionate about learning but the constraints are just too much. To give you an idea, compare our achievements with the available resources.”

Onto the key question: what data do we collect? Here are the highlights. 

We track key trends in ten development areas, namely mobile, desktop, web, backend, industrial IoT, games, augmented and virtual reality, consumer electronics, machine learning and data science, and apps/extensions to third party ecosystems (such as voice or CRM platforms). For the areas you tell us you’re involved in we ask you which programming languages, tools and platforms you use, how happy you are with the ones that you use (say, with your selected Cloud PaaS), and what you consider important in tools/platforms of this category (for example, scalability, ease of development, community). We ask you not just about the “how”, but also about the “what” and the “why”: why you got into development to begin with, what type of projects you’re working on, if and how you’re building a business around software development, and more. By understanding your motivations, projects and aspirations the technology builders can design solutions that are better suited to help you achieve your goals. We also ask about your learning interests, methods, and needs. Hopefully, that will lead to learning experiences suited to your style. Last but not least, and in order to help focus efforts on the most promising technologies, we gauge interest in and measure adoption of relatively new or emerging technologies, such as fog/edge computing and self-driving cars.

Developer Economics Survey: We know it is long.

Taking in your past feedback on the matter, we have put effort in making it shorter, and when some of you actually noticed I am not (very) ashamed to say I was hopping around the room in excitement. Some of you suggested that we break it down into smaller surveys. I might as well admit it, I am the villain who stubbornly resists that change!

The reason is simple: most of you are involved in more than one development areas, using multiple categories of tools, and the whole point here is to capture your full experience, across all sectors, and to map synergies between tools and platforms. We wouldn’t be able to do that if we were to ask you about each of the areas in a separate survey (plus we would be pestering you to take a survey ten times as much! You’re convinced now, right?). As another of our core values is to be data-driven, here’s the key data point behind this decision: “More than 80% of developers are involved in 2+ of the development areas that we track, and half are into at least four.” 
This is just an outline of who we are, what we do, and why. In case you have any comments or questions, please feel free to drop us a note and let us know of your thoughts. If you have already taken our 18th Developer Economics survey, we hope you enjoyed it and that you’ll spread the word among your friends – we’d love to welcome you all to our community. If you haven’t yet taken the survey we very much hope that you will, and that you won’t forget to say hello under that “Anything we forgot to ask?” open question at the end! There are 20+ pairs of eyes eagerly reading your feedback almost in real time, and virtually waving back to you. See you there.

Take the survey

developer economics survey


Data scientists need to make sense of the big picture, rather than the big data

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.  


What is the best programming language for Machine Learning?

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 machine learning language?


There’s an abundance of articles attempting to answer these questions, either based on personal experience or on job offer data. Τhere’s so much more activity in machine learning than job offers in the West can describe, however, and peer opinions are of course very valuable but often conflicting and as such may confuse the novices. We turned instead to our hard data from 2,000+ data scientists and machine learning developers who responded to our latest survey about which languages they use and what projects they’re working on – along with many other interesting things about their machine learning activities and training. Then, being data scientists ourselves, we couldn’t help but run a few models to see which are the most important factors that are correlated to language selection. We compared the top-5 languages and the results prove that there is no simple answer to the “which language?” question. It depends on what you’re trying to build, what your background is and why you got involved in machine learning in the first place.

Which machine learning language is the most popular overall?

First, let’s look at the overall popularity of machine learning languages. Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% prioritising it for development. Little wonder, given all the evolution in the deep learning Python frameworks over the past 2 years, including the release of TensorFlow and a wide selection of other libraries. Python is often compared to R, but they are nowhere near comparable in terms of popularity: R comes fourth in overall usage (31%) and fifth in prioritisation (5%). R is in fact the language with the lowest prioritisation-to-usage ratio among the five, with only 17% of developers who use it prioritising it. This means that in most cases R is a complementary language, not a first choice. The same ratio for Python is at 58%, the highest by far among the five languages, a clear indication that the usage trends of Python are the exact opposite to those of R. Not only is Python the most widely used language, it is also the primary choice for the majority of its users. C/C++ is a distant second to Python, both in usage (44%) and prioritisation (19%). Java follows C/C++ very closely, while JavaScript comes fifth in usage, although with a slightly better prioritisation performance than R (7%). We asked our respondents about other languages used in machine learning, including the usual suspects of Julia, Scala, Ruby, Octave, MATLAB and SAS, but they all fall below the 5% mark of prioritisation and below 26% of usage. We therefore focused our attention on the top-5 languages.

Python is prioritised in applications where Java is not.

Our data reveals that the most decisive factor when selecting a language for machine learning is the type of project you’ll be working on – your application area. In our survey we asked developers about 17 different application areas while also providing our respondents with the opportunity to tell us that they’re still exploring options, not actively working on any area. Here we present the top and bottom three areas per language: the ones where developers prioritise each language the most and the least.

Machine learning scientists working on sentiment analysis prioritise Python (44%) and R (11%) more and JavaScript (2%) and Java (15%) less than developers working on other areas. In contrast, Java is prioritised more by those working on network security / cyber attacks and fraud detection, the two areas where Python is the least prioritised. Network security and fraud detection algorithms are built or consumed mostly in large organisations – and especially in financial institutions – where Java is a favourite of most internal development teams. In areas that are less enterprise-focused, such as natural language processing (NLP) and sentiment analysis, developers opt for Python which offers an easier and faster way to build highly performing algorithms, due to the extensive collection of specialised libraries that come with it.

Artificial Intelligence (AI) in games (29%) and robot locomotion (27%) are the two areas where C/C++ is favoured the most, given the level of control, high performance and efficiency required. Here a lower level programming language such as C/C++ that comes with highly sophisticated AI libraries is a natural choice, while R, designed for statistical analysis and visualisations, is deemed mostly irrelevant. AI in games (3%) and robot locomotion(1%)  are the two areas where R is prioritised the least, followed by speech recognition where the case is similar.

Other than in sentiment analysis, R is also relatively highly prioritised – as compared to other application areas – in bioengineering and bioinformatics (11%), an area where both Java and JavaScript are not favoured. Given the long-standing use of R in biomedical statistics, both inside and outside academia, it’s no surprise that it’s one of the areas where it’s used the most. Finally, our data shows that developers new to data science and machine learning who are still exploring options prioritise JavaScript more than others (11%) and Java less than others (13%). These are in many cases developers who are experimenting with machine learning through the use of a 3rd-party machine learning API in a web application.


Professional background is pivotal in selecting a machine learning language.

Second to the application area, the professional background is also pivotal in selecting a machine learning language: the developers prioritising  the top-five languages more than others come from five different backgrounds. Python is prioritised the most by those for whom data science is the first profession or field of study (38%). This indicates that Python has by now become an integral part of data science – it has evolved into the native language of data scientists. The same can not be said for R, which is mostly prioritised by data analysts and statisticians (14%), as the language was initially created for them, replacing S.

Front-end web developers extend their use of JavaScript to machine learning, 16% prioritising it for that purpose, while staying clear of the cumbersome C/C++ (8%). At the exact opposite stand embedded computing hardware / electronics engineers who go for C/C++ more than others, while avoiding JavaScript, Java and R more than others. Given their investment in mastering C/C++ in their engineering life, it would make no sense to settle for a language that would compromise their level of control over their application. Embedded computing hardware engineers are also the most likely to be working on near-the-hardware machine learning projects, such as IoT edge analytics projects, where hardware may force their language selection. Our data confirms that their involvement is significantly above average in industrial maintenance, image classification and robot locomotion projects among others.

For Java, it’s the front-end desktop application developers who prioritise it more than others (21%), which is also inline with its use mostly in enterprise-focused applications as noted earlier. Enterprise developers tend to use Java in all projects, including machine learning. The company directive in this case is also evident from the third factor that is strongly correlated to language prioritisation – the reason to get into machine learning. Java is prioritised the most (27%) by developers who got into machine learning because their boss or company asked them to. It is the least preferred (14%) by those who got into the field just because they were curious to see what all the fuss was about – Java is not a language that you normally learn just for fun! It is Python that the curious prioritise more than others (38%), another indication that Python is recognised as the main language that one needs to experiment with to find out what machine learning is all about.

It seems that some universities teaching data science courses still need to catch up with this notion though. Developers who say that they got into machine learning because data science is/was part of their university degree are the least likely to prioritise Python (26%) and the most likely to prioritise R (7%) as compared to others. There is evidently still a favourable bias towards R within statistics circles in academia – where it was born – but as data science and machine learning gravitate more towards computing, the trend is fading away. Those with university training in data science may favour it more than others, but in absolute terms it’s still only a small fraction of that group too that will go for R first.

C/C++ is prioritised more by those who want to enhance their existing apps/projects with machine learning (20%) and less by those who hope to build new highly competitive apps based on machine learning (14%). This pattern points again to C/C++ being mostly used in engineering projects and IoT or AR/VR apps, most likely already written in C/C++, to which ML-supported functionality is being added. When building a new app from scratch – especially one using NLP for chatbots – there’s no particular reason to use C/C++, while there are plenty of reasons to opt for languages that offer highly-specialised libraries, such as Python. These languages can more quickly and easily yield highly-performing algorithms that may offer a competitive advantage in new ML-centric apps.

Finally, contractors who got into machine learning to increase their chances of securing highly-profitable projects prioritise JavaScript more than others (8%). These are probably JavaScript developers building web applications to which they are adding a machine learning API. An example would be visualising the results of a machine learning algorithm on a web-based dashboard.

There is no such thing as a ‘best language for machine learning’.

Our data shows that popularity is not a good yardstick to use when selecting a programming language for machine learning and data science. There is no such thing as a ‘best language for machine learning’ and it all depends on what you want to build, where you’re coming from and why you got involved in machine learning. In most cases developers port the language they were already using into machine learning, especially if they are to use it in projects adjacent to their previous work – such as engineering projects for C/C++ developers or web visualisations for JavaScript developers.

If your first ever contact with programming is through machine learning, then your peers in our survey point to Python as the best option, given its wealth of libraries and ease of use. If, on the other hand, you’re dreaming of a job in an enterprise environment, be prepared to use Java. Whatever the case, these are exciting times for machine learning and the journey is guaranteed to be a mind-blowing one, irrespective of the language you opt for. Enjoy the ride!