Software Engineering for Data Scientists in Python

Data science integrates math and statistics, specialized programming, advanced analytics, machine learning, and artificial intelligence (AI) with specific subject matter expertise to reveal actionable insights hidden in an organization’s data.

Data science is one of the fields which has shown the quickest growth rates across all industries. This is a result of the increasing volume of data sources and data that results from them.

Data Science has generated controversy among other disciplines as a field ever since it began to gain recognition.

In this article we will be learning about the fundamentals of software engineering, why it is important for data scientists and various principles. We will further have a glimpse on refactoring, clean and modular code (here we are mainly concerned with python) , testing and reviews.

Why is software engineering important for data scientists?

Mathematicians oppose the use of tools without a thorough understanding of the underlying principles, software engineers criticize data scientists' ignorance of fundamental programming concepts, and statisticians bemoan the lack of fundamental statistics knowledge frequently seen among practitioners.

And, to be honest, they are all valid.

You indeed need to have a firm grasp of ideas like probability, algebra, and calculus when it comes to statistics and arithmetic.

How extensive must that knowledge be?

The basics are non-negotiable, although a lot depends on your function.

Similar circumstances apply when it comes to programming; if your job requires you to write production code, you must at the very least be familiar with the basics of software engineering.


There are various causes, but in my opinion, they may be summed up in following principles −

  • Integrity − Code integrity refers to how effectively it is written, resilient to errors, catching exceptions, tested, and subject to outside scrutiny.

  • Explainability − Code's ability to be understood and its adequate documentation.

  • Velocity − The code's speed at which it can be executed in real-world settings.

  • Modularity − Scripts and objects should be modular to allow for reuse, reduce repetition, and improve code efficiency across classes

The significance of refactoring

After we get our code to work, refactoring offers us the ability to tidy and modularize it. We also have the opportunity to increase the effectiveness of our code. A software engineer typically uses one of these terms when discussing effective code −

  • Less run time

  • Less memory space

We can work on these two points in the following ways −

  • Parallelization is a terrific way to cut down on our run time. Writing a script to process data in parallel while utilizing some or all of the machine's processors is known as parallelization.

    Our scripts typically serially compute data, solving one problem before moving on to the next and so forth. That typically occurs when we develop Python code, and if we want to benefit from parallelization, we have to be specific about it.

  • Since Python doesn't truly release memory to the operating system, it is challenging to decrease memory usage in this language. When objects are deleted, their memory becomes available to new Python objects, but it is not returned to the system for free().

The importance of writing clean code

The majority of the topics we'll discuss in this article may theoretically be categorized as tools or advice for creating cleaner code. However, we'll concentrate on the precise definition of the word "clean" in this particular section. Even flawed code can run, as Robert Martin notes in his book Clean Code, yet dirty code can bring a development team to its knees.


To be honest, there are a lot of choices, but consider the time wasted when reviewing code that was poorly written or when starting a new job only to discover that you would be working with old, illegible code.

The importance of writing modular code

Although Python is inherently an object-oriented programming language, a detailed explanation of what that entails is outside the scope of this article.

But in brief, object-oriented programming is about creating modules with their properties and behaviors, as opposed to procedural programming, where you code a list of instructions for a script to follow.

In real life, these traits are referred to as qualities and deeds as techniques.

The objects Computer and Printer in the aforementioned scenario would be independent classes.

A class is a blueprint that includes the properties and methods for every object of that kind

In other words, all of the computers and printers that we design would have similar features and workings.

Encapsulation is the theory that underlies this proposition. Encapsulation refers to the ability to integrate data and functions into a single object or module.

Additionally, when a program is broken down into modules, various modules don't need to be aware of how something is done if they aren't in charge of carrying it out.

And how does this help?

In addition to making your code more efficient across classes and reusable, as previously said, it also makes it simpler to debug if necessary.

It is simpler to reuse separate modules in other programs when each portion of the program is perfected before the entire program is put together. You may also more easily remedy issues by being able to identify the error's root cause.

Importance of testing

Data science tests are required. The absence of testing in data scientists' code is frequently a source of complaints from other software-related fields. While in other algorithms or scripts, a mistake might just cause the program to stop working, in data science, this is even more dangerous because the program might run but produce incorrect insights and recommendations due to incorrectly encoded values, inappropriately used features, or data that contradicts the assumptions the models are based on.

When we talk about testing, two key ideas deserve discussion −

  • A unit test

  • development that is test-driven

Importance of code reviews

Everyone on a team benefits from code reviews, which encourage excellent programming practices and get code ready for production. Code reviews' primary objective is to find errors. However, they are also useful for enhancing readability and ensuring that team standard is met, preventing the feeding of sluggish or unclean code into production.

Code reviews are beneficial for knowledge sharing in addition to these benefits since team members get to read examples of different coding approaches and backgrounds.


In this story, we saw some of the fundamentals that are useful even for people who despite not being a programmer by nature are trying to come into the field from a completely different background. These help to write better production code, save time, and make the life of programmers easier when implementing scripts.