Data Science vs. Software Engineering: Which Is For You?

Software engineers and data scientists are two of the most important roles in the tech industry. While both segments work with technology, they are two different routes with different identities.

The demand for these two workforces is rapidly increasing, so young adults interested in computing and technology may have difficulty choosing the major.

Data science vs software engineering: Which is best for you? This post thoroughly overviews the significant difference between the two majors.

Read on to decide the suitable path for yourself.

Data Science And Software Engineering Overview

Data science is derived from computer science – an interdisciplinary field that uses multiple scientific methods and processes to study various sorts of data.

It involves using numerous technologies, such as data mining, transformation, and purging, to research and analyze specific statistics.

On the other hand, software engineering is building software by systematically applying engineering principles.

Software engineers analyze user requirements and design, build, and test software applications to check if they satisfy those requirements.

Both domains require comprehensive programming knowledge, yet DS focuses on manipulating heavy-duty datasets more.

Data Science Vs Software Engineering: 6 Critical Differences

Before having close access to the six critical dissimilarities between DS and SE, let’s get a peek into the comparing table to have an idea about how they differ.


Data science

Software engineering

Responsibilities & roles

Gather and process datasets

The development of features and applications for users


Database tools, visualization tools, analytics tools, etc

Testing tools, continuous integration tools, database tools for software, SCM tools, etc

Programming languages

Jupyter Notebooks, SQL, R, Python, etc

Github, Jenkins, RestAPI, Docker, etc


MBA, M.Tech, B.Tech, MSc, or

Bachelor’s degree in computer programs


Statistics and probability, coding, algorithms, machine learning, domain Knowledge, etc

programming languages, Release and build management, testing, etc

Salary & job outlook

– About $116,654

– 15% growth in the next ten years

– About $108,201/year

– 22% growth in the next ten years

Responsibilities & Roles

Are you interested in working with datasets or applications?

DS specialists are responsible for analyzing and collecting statistics for their managers. They utilize a discovery method to detect queries for info analysis.

They solve those queries by integrating and storing data, choosing algorithms, using machine learning and artificial intelligence, and etc.

SE experts have to write code to build applications utilizing programming languages. They will test the codes, identifying areas and bugs that can improve.

They need to modify existing systems, make improvements and add features.


Both professions have to get used to and master numerous tools during the work process. Specifically, DS professionals need to use various tools to complete daily, essential tasks.

DataRobot, Tableau, BigML, Apache Spark, and SAS are common examples of the tools that you may expect to learn to use as a data scientist.

Likewise, SE specialists often need to combine the uses of different tools to develop an application. Jenkins, Jira, Docker, IntelliJ IDEA, and Github are common tools for these engineers.

Programming Languages

You’ll need to learn various programming languages.

Here are the most broadly used programming languages among software engineers:

  • Github
  • Jenkins
  • Rest API
  • Docker
  • Python
  • Java

The most important programming languages used by data scientists include:

  • Jupyter Notebooks
  • Machine Learning Models
  • SQL
  • R
  • Python


DS workers may come from different educational backgrounds. But most of them are MBA grads from reputable business schools, M.Tech or B.Tech majoring in Information Technology or Computer Science, and MSc or in Statistics.

SE graduates need a bachelor’s degree specializing in a relevant computer program, but that’s just the minimum requirement for entry-level positions.

Higher positions demand a comprehensive understanding of programming languages and their functions.


Both careers require good teamwork skills.

DS experts typically show hard skills in data storytelling, computer science, math, critical thinking, and statistical analysis.

The role mainly demands a skill set of statistics, computing essentials, programming languages, and mathematics. Machine learning is also a critical part of this topic.

Concerning soft skills, they need to have excellent organization, adaptability, leadership, communication, and teamwork skills.

SE and DS staff share many soft skills in common, but they have to be adept at application design, testing, troubleshooting, debugging, and programming regarding hard skills.

Salary & Job Outlook

An average DS specialist in the US can earn a base salary of $116,654 annually, while an average SE professional’s base salary is $108,201 a year.

Both careers enjoy great benefits, high pay, and opportunities that reward and challenge them at the same time.

The Bureau of Labor Statistics forecasts that employment opportunities for DA workers will increase by 15% in the next ten years.

Meanwhile, SE employees will expect to see a 22% rise in employment opportunities in the next ten years, which outpaces many tech-based fields.

Which Career Applies Best to You?

Both software engineering and data science require candidates to acquire programming skills. DS involves machine learning and statistics, whereas SE leans more towards coding languages.

Yet, both career paths are highly rewarding and in high demand. Generally, it’s all up to you, your interest, and your strengths.

If you’re more interested in fields that focus on computer science, you will probably thrive in the software engineering area.

On the other hand, if you’d love to work with datasets and identify or analyze statistics, the latter career is perhaps a suitable choice.

After all, the decision on your career path should match your personal goals, interests, and ambitions.

Generally, though the DS field is surging, its importance is not likely to outgrow the SE field since we need those engineers to develop the software that data scientists have to work on later.

Still, DS plays a critical role in analyzing the datasets and bringing new improvements to the company, on which SE can develop new applications.


It is paramount to decide because the two careers have some significant differences, despite sharing many aspects in common.

Don’t mind too much about the employment prospect since both fields are cutting-edge areas that can be lucrative and rapidly growing.

Focus on determining whether you’re more fond of building software or analyzing statistics to dig out insights.