The importance of unit tests and testing in general in data science
- Von Dr. Stanislav Khrapov
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In the fast-paced world of data science, the pressure to deliver quick results often leads to a critical oversight: the lack of rigorous software engineering practices, including unit testing. Many data scientists come from non-IT backgrounds such as statistics, physics, economics, or biology. As a result, they may not be well-versed in the established best practices for software development, which can lead to significant problems when the code needs to scale or move into production environments.
This issue becomes even more pronounced when there is a lack of qualified software and data engineers available to support data science projects. Unfortunately, this is often the case in many organisations, either due to the scarcity of such professionals in the job market or because management underestimates the importance of robust software practices for long-term operational success. This article explores why testing, and particularly unit testing, is essential in data science and how its neglect can lead to unmanageable systems and production nightmares.
THE DATA SCIENCE CONUNDRUM: FAST RESULTS VS. SUSTAINABLE SYSTEMS
Data science teams often work under tight deadlines and high pressure to deliver tangible business results as quickly as possible. This is understandable: companies invest heavily in data science initiatives in the expectation of insights, predictions or automation that will give them a competitive advantage. To achieve these goals, data scientists typically start with experiments, proof of concepts (PoCs) and models run in environments like Jupyter Notebooks. These notebooks are great for exploration and experimentation, enabling rapid prototyping, data visualization and model evaluation.
However, notebooks can also promote a culture of lax code quality. The flexibility of a notebook environment often leads to poorly structured ad hoc scripts that are only designed to “work” in a specific, non-reusable context. In the heat of the moment, data scientists may take shortcuts, such as using hard-coded variables, copying code, or performing calculations in a non-deterministic way. At this stage, testing is rarely considered, as the main focus is on getting the model to work, no matter how messy or brittle the code base becomes.
While this may work for PoCs, the situation changes drastically when the same models need to be deployed into production. What was once an exploratory notebook is suddenly expected to run reliably as a production microservice, expected to handle live data, scale under real-time demands, and integrate with other systems. Without proper testing and quality assurance, these models often break in production, leading to frustration, wasted time, and a loss of trust in the data science team.
WHY UNIT TESTS ARE CRUCIAL IN DATA SCIENCE
Unit testing is a fundamental software development practice that ensures individual pieces of code (i.e., units) work as expected. In the context of data science, these units can be individual functions, data transformation steps, or model components. Implementing unit tests early in the development process has several advantages:
- Early detection of errors: Unit tests help identify bugs and edge cases in the early stages of development. This is especially important in data science projects, where small changes in data preprocessing, feature engineering, or model parameters can have cascading effects. For instance, a minor bug in a data transformation function can introduce data leakage, skewing your model’s performance and rendering it unusable in production.
- Refactoring with confidence: In data science, experimentation is key. You might want to test different models, experiment with new features, or optimise existing ones. Without unit tests, refactoring code can be risky, as you can’t be sure that your changes haven’t broken other parts of the pipeline. With a solid unit test suite in place, you can refactor code with confidence, knowing that your tests will catch any regressions.
- Encouraging modular and maintainable code: Writing unit tests encourages data scientists to break their code into smaller, more manageable pieces. This practice naturally leads to cleaner, more modular code, which is easier to maintain and extend. If you need to add a new feature or modify an existing one, modular code with proper unit tests will make it much simpler to implement these changes without introducing bugs.
- Improving collaboration across teams: In larger teams, collaboration between data scientists, data engineers, and software engineers is essential. Well-tested code with clear responsibilities is easier for other team members to understand and work with. This is particularly important when data engineers or software developers take over productionizing a data science model. They need to be able to trust that the code works as intended and can integrate with the broader system architecture.
THE CONSEQUENCES OF SKIPPING TESTS IN DATA SCIENCE PROJECTS
Neglecting testing may save time in the short term, but it often leads to major issues down the road, especially when the project transitions from development to production. Here are some of the most common problems that arise when testing is neglected.
Unstable production environments
Deploying untested code into production is like walking through a minefield. Small, undetected bugs in the data pipeline, model, or post-processing can cause your entire system to crash or generate incorrect results. Worse, these errors may only surface intermittently, making them difficult to detect and resolve without proper tests in place.
Unscalable and rigid systems
Many data science models start as proof of concepts, built under time pressure with little consideration for scaling. When these models are pushed into production without proper refactoring or testing, they often become rigid, hard-to-maintain systems. Adding new features, changing data sources, or tweaking model parameters becomes a nightmare. The lack of tests makes it risky to change anything, which slows down the entire development process.
Loss of trust and reputation
When models in production fail, it not only causes technical issues but can also have a significant impact on the business. Incorrect predictions, downtime, or flawed recommendations can lead to financial losses, damaged customer relationships, and a loss of trust in the data science team. Once trust is eroded, it becomes difficult for the data science department to justify further investment, stifling innovation and slowing down the development of future projects.
HOW TO START INCORPORATING TESTING INTO DATA SCIENCE
Incorporating testing practices into data science workflows doesn’t have to be complicated. Here are a few practical steps to get started:
- Start with unit tests for core functions: Begin by writing simple unit tests for core functions in your codebase. Test key data transformation functions, model evaluation metrics, and any custom logic that plays a critical role in the pipeline. Frameworks like pytest for Python make it easy to write and run these tests.
- Use mocking for external dependencies: In many data science projects, your code may rely on external resources like APIs, databases, or large datasets. Use mocking libraries (e.g., unittest.mock) to simulate these external dependencies in your tests. This will ensure that your tests run quickly and are isolated from external factors.
- Implement continuous integration (CI): Incorporate testing into a CI pipeline. Every time you or a teammate makes a change to the codebase, your unit tests will automatically run, catching any potential issues before they make it into production.
- Test data quality: Beyond unit testing, you should also test the integrity of your data. Data pipelines can fail if the incoming data format changes, missing values appear, or outliers occur unexpectedly. Write tests that validate the schema, distributions, and consistency of your input data.
- Monitor model performance in production: Once your model is in production, testing doesn’t stop. Implement monitoring to track how well the model performs with live data. Alerts should trigger when model performance deviates from expectations, allowing you to address issues quickly.
CONCLUSION: TESTING IS NON-NEGOTIABLE FOR DATA SCIENCE SUCCESS
In the long run, cutting corners on testing is never worth it. Although it might seem like a time-saving measure at first, the costs of neglecting unit tests and other forms of testing become painfully clear when models fail in production. By adopting a testing mindset, data scientists can not only create better-quality models but also ensure that their work is reliable, maintainable, and scalable.
As the lines between data science and software engineering continue to blur, testing will become an increasingly essential skill for data scientists to master. Organisations that prioritise testing will see greater long-term success, avoiding the pitfalls of fragile, unstable systems and setting themselves up for scalable, future-proof data science initiatives.
Dr. Stanislav Khrapov
Lead Data Scientist