The importance of unit tests and testing in general in data science

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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:

  1. 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.
  2. 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.
  3. 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.
  4. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Picture of Dr. Stanislav Khrapov

Dr. Stanislav Khrapov

Lead Data Scien­tist

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