Data science has become democratized, making it accessible to more people. It isn’t just for data scientists anymore. With tools like AutoML and Python, which lower the barrier to entry for individual contributors and organizations, a wide variety of people have joined the ever-growing industry. Sophia Yang, Senior Data Scientist at Anaconda, shares lessons that new-age data scientists can learn from the art and science of software development.
The U.S. Bureau of Labor Statistics predictsOpens a new window growth in data science job skills, with roughly a 28% increase (or 11 million new jobs) estimated for 2021 through 2026.
While this growth contributes to more data-informed decisions, greater data literacy, and more innovation throughout industries, it also means that more data scientists enter the field without a professional background in software development. Data scientists who don’t have extensive experience in software development may be highly skilled in mathematics, statistics and AI/ML, but their core competencies aren’t in computer science or software development. However, as the field continues to mature and requires data scientists to be more end-to-end, data scientists will need to learn from software developers. It will be crucial for data scientists to learn from the best practices of the modern software development lifecycle.
Creating Reproducible Data Science
At the core of any scientific process is the ability to verify and replicate. With software development and data science, the first step of that verification comes in the form of building a reproducible environment, meaning that anyone should be able to use your data, run your code, and use your package to get the same results. Through reproducible environments, practitioners can verify the code and ensure that it is built exactly how it was intended. Building reproducibility into your environments may feel like an unnecessary step, but it also allows for better collaboration and safe upgrades. Anyone can create something for their own use, but creating it for others to use and for production is the key to good data science. Reproducibility builds trust, and for code and packages to be trusted, data scientists need to ensure that they record all of the tools, libraries, and versions used.Â
Automate Everything
In particular, automate deployment. Automating deployment is vital for modern software development and is just as important to data scientists as software developers. If data scientists fail to automate their entire workflows from data to production (which can mean various things in their context), they won’t have reproducible results even if they have reproducible environments, and others won’t be able to trust their conclusions on an ongoing basis. It might seem quicker to grab some data, run some analysis, and report a result. And it is, for something you only need once. However, as soon as someone needs something re-run, modified, or updated, or if someone wants more justification for any results, a one-off workflow won’t provide a complete record of what they did. Automation provides that record, ensuring transparency and the entire work history while efficiently delivering up-to-date results.Â
Testing, Testing, and More TestingÂ
Like a sculptor meticulously crafting a statue, constantly chiseling away at certain areas until it’s perfect, data scientists need to fine-tune the code and packages they are working on. Testing is crucial to ensure that everything is working as intended and free of any obvious bugs. Testing is so important for software development that developers sometimes use test-driven development to write tests even before developing the software. With that approach, the tests declare and define what the software should do, and then the task is to write software that passes the tests. It’s not always plausible for data scientists to do test-driven development, but data scientists should test their code. By taking the extra time to go over everything with a fine-toothed comb, most defects or issues can be tracked and fixed before the project is shared widely or deployed to production.Â
However, testing doesn’t stop there. Only through continuous testing, especially when making updates, can you have confidence that your code and packages are working as intended. When creating tests, create ones that cover all possible ways that your code will be exercised, not just how you think it will be used. Comprehensive testing is key to creating optimized and secure code.
Version ControlÂ
Software developers use version control to keep everything organized, and similarly, the best data scientists also use version control. To secure your working code and packages, keeping track of your work including changes, notes, and file locations, will make management easier and more efficient. Using a version control system like Gi makes this process simple and guarantees that your source code, documentation, data, and trained models are all managed and secured in one place, allowing you to go back to any previous version for comparison or debugging. An organized system is an efficient system and data scientists should ensure that all of their work is tidy and documented to diminish any risks or confusion.Â
By taking a page out of software developers’ book of best practices, data scientists with all levels of expertise can become better informed and confident in their data science projects. Following these best practices allows practitioners to keep complexity under control and ensure that the right problems are being solved and makes it possible to apply previously developed solutions to new problems. At first, these extra steps can feel like unnecessary work but are necessary to progress your skillset as a data scientist, whether you’re a beginner or an expert looking to improve.Â
What data science- practices will you adopt from the software developers’ guidebook? Share with us on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to know!