This article is based on reporting by ZDNet, drawing on a SlashData developer survey of more than 20,500 respondents.
Developers Are Betting on Machine Learning and Data Science
A large-scale survey of more than 20,500 developers conducted by SlashData identified machine learning and data science as the single most important skill set developers planned to pursue in the year ahead. Forty-five percent of respondents said they intended to gain expertise in these fields — placing it well ahead of other high-demand disciplines.
The findings reflect a broader shift in how the industry values data fluency. As businesses increasingly depend on predictive models, recommendation engines, and automated analytics, developers who can bridge the gap between software engineering and data science are becoming highly sought after.
Top Skills Developers Planned to Pursue
According to the SlashData survey, the leading skills developers intended to build over the coming year were:
- Machine learning / data science — 45%
- UI design — 33%
- Cloud-native development — 25%
- Project management — 24%
- DevOps — 23%
Design thinking and UI design ranked a strong second, underlining that developer ambitions are not purely back-end or algorithmic — there is sustained interest in the full product experience.
Why Data Science Demands a Broad Skill Set
The report’s authors, led by Stijn Schuermans, emphasised that data science sits at the intersection of multiple disciplines. As the report states:
“Data scientists live in the intersection of coding, mathematics and business and, therefore, they need to possess a mixture of technical and soft skills as well as have the domain-specific knowledge. Analyzing large volumes of data with advanced statistical and visualization techniques often requires good mathematical background, especially in probability and statistics. It also requires the ability to write code in at least one programming language like Python, which is currently by far the most popular language among data scientists.”
— Stijn Schuermans et al., SlashData Developer Economics Survey
In practice, this means that developers moving into data science need to invest not only in Python proficiency and statistics, but also in communication skills and business domain knowledge — capabilities that pure software engineering roles have not traditionally required.
Python’s Rise in the Developer Ecosystem
The survey found that Python had reached approximately seven million active developers at the time of publication and was rapidly closing the gap with Java in overall popularity. Among machine learning developers and data scientists specifically, 62% reported using Python as their primary language.
JavaScript remained the most widely used programming language overall, with more than ten million active developers. However, Python’s strong foothold in data science, scientific computing, and automation is driving consistent growth that cuts across experience levels — from students learning their first language to senior engineers expanding their skill set.
The combination of readable syntax, a mature ecosystem of libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch), and strong community support makes Python the pragmatic default for anyone entering the machine learning space.
DevOps Enters the Mainstream
The survey also captured meaningful momentum for DevOps adoption. Twenty-three percent of developers indicated they wanted to strengthen their DevOps skills, and one in eight respondents reported already being engaged in active DevOps projects. This signals that DevOps has moved well beyond an early-adopter niche and is now an expected competency across a wider range of engineering teams.
The convergence of MLOps — the application of DevOps practices to machine learning pipelines — means that the two fastest-growing areas in this survey are increasingly intertwined. Engineers who can manage model versioning, automated retraining workflows, and deployment pipelines are positioned at the intersection of both trends.
What This Means for DevOps and Engineering Teams
For engineering leaders and individual contributors, this survey reinforces several practical takeaways:
- Python proficiency is increasingly table stakes — even for engineers not working directly in data science, familiarity with Python tooling and scripting is valuable across cloud, automation, and DevOps workflows.
- Data literacy is a cross-functional expectation — understanding how ML models are built, evaluated, and deployed helps engineers collaborate more effectively with data science teams.
- Cloud-native and DevOps skills reinforce each other — with 25% of developers targeting cloud-native development and 23% targeting DevOps, teams that invest in both areas build compounding capabilities in CI/CD, containerisation, and infrastructure-as-code.
- Upskilling paths should reflect business context — the SlashData findings highlight that domain knowledge matters as much as technical proficiency in data science roles, so learning programs benefit from pairing technical training with real business problems.
The trajectory identified in this 2018 survey has proven durable — machine learning, Python, and DevOps have each continued to grow in prominence in subsequent years, validating the instincts of developers who prioritised these areas early.