Best Ways to Create Reusable Code for Data Science Projects

It is complicated to create reusable code for data science projects, but it is not impossible

Data Science consolidates various fields, including statistics, logical strategies, Machine Learning (ML), and data analysis, to separate value from information. The individuals who practice data science are called data scientists. The principal purpose of Data Science is to find patterns within data. It uses various statistical techniques to analyze and draw insights from the data. As aspiring Data Scientists, people spend a lot of their time writing code, however looking at the bigger picture, the core of Data Science is not about writing code, but understanding data and extracting value out of it. The coding part is just a means to accomplish this goal. Obviously, one cannot avoid writing code, and doing so is probably detrimental to the process, however, you can reduce the amount of time you spend doing it. This article features the best ways to create reusable code for data science projects.

Modular

Modular code means that your code is broken into small, independent parts (like functions) that each does one thing. Each function, whether in Python or R, has several parts:

A name for the function.

Arguments for your function. This is the information you'll pass into your function.

The body of your function. This is where you define what your function does. Generally, I'll write the code for my function and test with an existing data structure first and then put the code into a function.

A return value. This is what your function will send back after it's finished writing. In Python, you'll need to specify what you want to return by adding return (thing_to_return) at the bottom of your function. In R, by default, the output of the last line of your function body will be returned. It is one of the best ways to create reusable code for data science projects.

Readable

"Readable" code is code that is easy to read, even if it's the first time you've seen it. In general, the more things like variable and function names are words that describe what they do/are the easier it is to read the code. In addition, comments that describe what the code does at a high level or why you made specific choices can help you.

You can improve names by following a couple of rules:

Use some way to indicate the spaces between words in variable names. Since you can't use actual spaces, some common ways to do this are snake_case and camelCase. Your style guide will probably recommend one.

Use the names to describe what's in the variable or what a function does. For example, sales_data_jan is more informative than just data, and z_score_calculator is more informative than just calc or norm.

It's ok to have not-ideal variable names when you're still figuring out how you're going to write a bit of code, but I'd recommend going back and making the names better once you've got it working. It is one of the best ways to create reusable code for data science projects.

Versatile

Versatile code solves a problem that will happen more than once and anticipates variation in the data.

Data scientists have to do and know about a lot of different things: you've probably got a better use for your time than carefully polishing every line of code you ever write. Investing time in polishing your code starts to make sense when you know the code is going to be reused. It is one of the best ways to create reusable code for data science projects.

Creative

Being creative while creating reusable codes for data science projects is crucial. It encourages you to seek out existing libraries or modules that already exist to solve your problem. If someone has already written the code you need, and it's under a license that allows you to use it, then you should probably just do that. It is one of the best ways to create reusable code for data science projects.