Data Science vs Software Engineering: Key Differences

Once you decide you want to work in the tech industry, the next step is to determine which specific field is the best fit for your abilities and interests. The required skills and job opportunities in data science vs software engineering might be more different than you realize. 

This article will detail the differences between data science and software engineering, including descriptions of popular jobs in both branches of the tech sector. We discuss the various tools, methodologies, and technical skills used by professionals in each field.

Key Differences: Data Science vs Software Engineering

Data science and software engineering both involve programming skills. The difference is that data science is more concerned with gathering and analyzing data, whereas software engineering focuses more on developing applications, features, and functionality for end-users. 

If you know you want to work in the tech sector, deciding between data science vs software engineering can be difficult because both fields offer strong benefits. Learning more about the required skills and job responsibilities for each of these careers is a good starting point.

Software Engineer vs Data Scientist Quick Facts

When you first begin your research in the tech field, you’re likely to come across a vast array of different specialties and careers. Two of the best tech jobs are data science and software engineering. These careers both come with an impressive average annual salary and, importantly, a strong job outlook over the next decade, which means you should have plenty of advancement opportunities. 

Although they’re both segments of the tech industry, they’re definitely two very different paths to go down. You’ll need to consider your skills and interests to determine whether to become a software engineer vs data scientist.

What Is Data Science?

Data science gives you an enlightening perspective on large-scale trends. 

Data science is notoriously hard to define, but in general, these professionals use algorithms and statistics to draw insights from structured and unstructured data. The goal of a data scientist is going to depend on the problem they’re examining. 

In a business context, a data scientist might be measuring the impact of changes in promotional material. Whereas in finance, a data scientist is probably trying to discover what (if anything) accurately predicts returns in one of the major markets.

Data scientists with advanced skills or a high level of education might also be tasked with creating new algorithms and frameworks for processing datasets. It is a valuable, growing field that offers plenty of opportunities to those with the right skills and experience.

It is a valuable, growing field that offers plenty of opportunities to those with the right skills and experience.

What Is Software Engineering?

One major difference between data science and software engineering is that software engineers generally need far more advanced programming skills.

Software engineering is another one of the major divisions of the tech industry. It involves using programming knowledge and engineering skills to develop new software. In software development, the goal is to create new programs, applications, systems, and even video games. Like with data scientists, the projects you work on as a software engineer will greatly depend on the company you work for. 

Because there’s no such thing as bug-free software, an inescapable secondary goal for software engineers is to constantly patch and iterate on existing software to make it better and ensure it performs as required.

Data Science vs Software Engineering: Differences

While data scientists and software engineers often work side-by-side, they play a very different role in the tech industry.

Data science and software engineering are both highly technical fields that require similar skillsets, but there are big differences in how these skills are usually applied. So, let’s compare data science vs software engineering to find the most important differences.

For the most part, a data scientist uses their skills to sift through data, identify patterns, and interpret their findings in meaningful ways. Then, they use what they’ve learned to help a business make a decision or learn how to be more efficient. Data science tends to be much more about analysis in practice, with some aspects of programming and development thrown in.

Software engineering, on the other hand, tends to focus on creating systems and software that is user-friendly and that serves a specific purpose. It’s not uncommon for there to be a heavy analytical component to this process, so it’s easy to see how the two fields overlap.

Here is an example to help you visualize the difference between data science and software engineering. If you’re reading this article on the Google Chrome browser on your phone, it’s a safe bet that a team of software engineers developed it and continue to support it to ensure that it works well with your new phone or after updates.

Now let’s say you use Chrome to search “Best Data Science Bootcamps”. You’ll see a listing of results relevant to your search, and there’s a good chance you’ll find exactly the type of article you’re looking for within the first few links. This is achieved through the power of a data science algorithm.

The algorithm is able to sift through a truly enormous data set and serve up a list of content it thinks you’ll find useful by looking for specific keywords, the authority of the sites, the quality of their content, and a number of other factors.

So now maybe it’s easier to see why these two fields are important and what they involve, but it’s probably not enough to help you pick between the two. Let’s look at the difference between software engineering and data science more closely.

Impact

It’s difficult to quantify how immensely and irreversibly both data science and software engineering have changed life as we know it. Almost everyone has a smartphone in their pocket that can answer almost any query in seconds.

Today, you don’t have to drive around searching for a coffee shop when you need a quick caffeine jolt. You just ask your phone assistant (developed by a software engineer) to find one for you. Then, an algorithm (developed by a data scientist) searches your query and finds a coffee shop just one mile away. It then opens up your map application (developed by a software engineer) to tell you exactly where to go.

And that’s just considering our personal lives. Software engineering and data science have, if anything, had an even more outsized impact on how businesses operate. Using software and big data, businesses are able to make data-informed decisions. This means being better able to identify an audience, anticipate their needs, give them what they want, and make bigger profits.

Methodologies

While both fields have some overlap in work processes, software engineers and data scientists tend to have very different methodologies. Let’s look at data science first.

There are a variety of roles and expertise that fall under the data science umbrella. An expert in charge of gathering data is generally called a ‘data engineer’. They are going to be pulling data from various sources, cleaning and processing it, and storing it in a database. This is usually referred to as the extract, transform, load (ETL) process. In modern cloud stacks, teams often use ELT (extract, load, transform) with cloud data warehouses before downstream modeling.

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Anyone using this data to build models and do analysis is called a ‘data analyst’ or ‘machine learning engineer’. The crucial aspects of this part of the data science pipeline include making sure that any models built aren’t violating their underlying assumptions and that the models are actually driving worthwhile insights. Many teams follow a structured process such as CRISP-DM for problem framing and modeling, and adopt MLOps practices to deploy and monitor models over time.

Software engineering, on the other hand, uses a methodology called SDLC, or the Software Development Life Cycle. This workflow is used to develop and maintain software. The SDLC steps include planning, implementation, testing, documentation, deployment, and maintenance. Today this is commonly implemented with Agile practices and DevOps/CI/CD automation to ship reliable software frequently.

Approaches

Another big difference between data science vs software engineering is the approach they tend to use as projects evolve. Data science is a very process-oriented field. Its practitioners ingest and analyze datasets in order to better understand a problem and arrive at a solution.

Software engineering, on the other hand, is more likely to approach tasks with existing frameworks and methodologies. The Waterfall model, for instance, is a popular technique that maintains that each phase of the Software Development Life Cycle must be completed and reviewed before moving on to the next. Some other frameworks used in software engineering include Agile, the V-shaped model, and Spiral. In practice today, Agile/DevOps with CI/CD is the dominant approach for many teams.

Tools

Data scientists and software engineers use a wide variety of precision machinery to do their jobs effectively and efficiently. A data scientist’s wheelhouse contains tools for data analytics, data visualization, working with databases, machine learning, and predictive modeling. The data science tools you use most often will depend on your job title.

If you are doing a lot of data ingestion and storage, you may use Amazon S3, MongoDB, Hadoop, MySQL, PostgreSQL, or something similar. For model building, there’s a good chance you’ll be working with Statsmodels or scikit-learn. Distributed processing of big data requires Apache Spark. It’s also common to use Jupyter, TensorFlow/PyTorch, and modern cloud warehouses such as BigQuery or Snowflake.

A software engineer utilizes tools for software design and analysis, software testing, programming languages, web application tools, and much more. As with data science, a lot depends on what you’re trying to accomplish.

For actually producing code, Visual Studio Code and other IDEs are widely used. In the world of backend web development, Ruby on Rails, Python’s Django, and Flask are common tools. On the web stack, Node.js and React remain among the most-used frameworks, and many teams rely on Docker and Kubernetes to build and deploy services.

Skills

There is a fair amount of overlap between data science and software engineering skills. The most important things you’ll need to know to become a data scientist include programming, machine learning, statistics, and data visualization. Different positions may require more than these skills, but it’s safe to say these are the bare minimum when pursuing a career in data science.

If you’re interested in software engineering, the necessary skills will often be a little more intangible. The ability to program and code in multiple programming languages will be required, but you also need to be able to work well in teams, problem solve, adapt to different situations and have a willingness to learn.

Salary

It probably comes as no surprise, but software engineers and data scientists get paid quite well. This is due to the fact that both professionals need to master highly technical skills in order to excel. They also need to continually update their knowledge to stay on top of industry trends.

Comparing data scientist vs software engineer salary figures can be tricky because there are so many variables that contribute to wage. Your salary will depend on your level of education, years of experience, location, and job title. According to the US Bureau of Labor Statistics, the median annual wage in May 2024 was $133,080 for software developers and $112,590 for data scientists.¹ ²

Career Paths: Data Science vs Software Engineering

There are a variety of data scientist and software engineer job opportunities for those with impressive technical skills. Both fields have a strong job outlook over the next decade (software developers +17%, data scientists +36%).¹ ²

Common Data Science Careers

Once you have the essential skills you need to understand and work with data, there are several roles you can step into. Many data scientists also receive an education in business to ensure they are prepared to interpret data and advise companies adequately.

• Data analyst. A data analyst uses various methods to gather, clean, and interpret data. They need to identify patterns and gain insights to help businesses solve a problem or answer a question.
• Machine learning engineer. In this role, you will be in charge of using large datasets to create artificial intelligence systems and predictive models. In addition to coding, you’ll need to know how to clean and interpret data. 
• Database architect. A database architect is in charge of designing database solutions for a company. They ensure that data is stored safely and remains accessible to the right people.
• Data engineer. The primary responsibility of a data engineer is to design pipelines to bring data together from different sources. They then prepare that data for analysis.
• Business IT analyst. A business IT analyst reviews massive amounts of data to help companies develop better business strategies, products, or technological solutions.

Common Software Engineering Careers

Software engineers can work in a wide variety of fields since most companies these days rely on technology to some extent. Many software engineering roles require a range of soft skills because these professionals often supervise others and collaborate with experts from different departments.

• Computer programmer. Computer programmers use their extensive coding skills to test and program software to fit guidelines laid out by software engineers and software developers. 
• Web developer. These professionals use front end and backend development to create and maintain functional web applications. 
• Software developer. Software developers design, test, and develop all kinds of computer programs. They work with their clients and employers to ensure the software they create will meet the company’s needs. 
• Information systems manager. An information systems manager works with a company to identify its technology needs. Then they plan, install, and develop the appropriate systems. 
• DevOps engineer. The primary goal of a DevOps engineer is to identify and solve problems that occur throughout the Software Development Life Cycle.

What About Software Developers and Data Engineers?

People often use the terms ‘software developer’ and ‘software engineer’ interchangeably. The same can be said for data scientists and data engineers. Keep reading to learn a bit more about how these roles are different from each other.

Software Developers vs Data Scientists

Software developer can be a more generic term for anyone who develops software. It often overlaps with software engineering. The key difference is that software engineers apply engineering principles specifically.

When comparing a software developer vs a data scientist, you’ll find that the software developer primarily focuses on creating computer products. Their work may involve data and databases, but not to the level you’d experience as a data scientist.

Data Engineers vs Software Engineers

Data engineers are actually closer to software engineers than data scientists are. Data engineers differ from data scientists in that engineers focus on how data is handled, while scientists focus on the result of that data. Data engineers primarily work on the software that gathers and handles data that data scientists often use. 

Data engineers develop software just like software engineers, only that software is solely focused on data. The relationship is similar to game developers, who also develop software like engineers but only focus on video games. Basically, the difference between a data engineer vs software engineer is that a data engineer has a more specific focus.

Data Science vs Software Engineering: Summary

There are many differences if you compare data science vs software engineering job responsibilities. Data science involves collecting and analyzing data, while software engineering is concerned with creating useful applications. While data scientists use the ETL process, software engineers use the Software Development Life Cycle. Modern teams also commonly use ELT for cloud analytics, CRISP-DM/MLOps in data science, and Agile/DevOps with CI/CD in software engineering.

Overall, data science is more process-oriented, whereas software engineering uses frameworks like Waterfall, Agile, and Spiral. The two fields also differ in what tools and skills they use. Data scientists use tools like Python (NumPy, pandas), Jupyter, scikit-learn, Spark, and SQL on platforms like PostgreSQL/MySQL or cloud warehouses. Engineers commonly use Python/JavaScript/TypeScript, React/Node.js, Git/GitHub, Visual Studio Code, Docker, Kubernetes, and web frameworks such as Django, Flask, or Rails.

Data Science vs Software Engineering FAQ

Sources

1. https://www.bls.gov/ooh/computer-and-information-technology/software-developers.htm
2. https://www.bls.gov/ooh/math/data-scientists.htm