Data Science vs Software Engineering - What you should know?

Introduction

According to a Google Economist, the sexiest job in 2019 was predicted to be Data Scientist, and it has proven true even in 2025, as expert data scientists remain in high demand across industries, commanding higher salary packages. The role of a data scientist closely resembles that of a software engineer, with an average salary of approximately $137k.

With almost the same salaries, do they share the same roles or responsibilities? Well, it depends on the company and how it defines these roles. In most cases, both have a different set of responsibilities.

So in this blog we would discuss a few of the useful comparisons between.data science vs software engineering

What is Software Engineering?

Software Engineering is a structured approach to design, develop, and maintain the software program and avoid quality issues. It makes the requirements clear so that the development process can be easier to understand and implement.

A Software Engineer is a person who applies principles of Software Engineering to design, develop, maintain, and test the software program. The job role of a Software Engineer involves analyzing a problem, thinking, design and implement the best software solution and test it continuously. Software engineers majorly deal with complex business problems and try to find out the best solution too. The work of a Software engineer ends when he finishes implementation work for the problem.

What is Data Science?

Data Science converts or extracts data in multiple formats to meaningful information. Businesses have the flexibility to use this knowledge to make decisions and improve overall business processes. With data science, businesses can become intelligent enough to sell or push products.

Data scientists are data wranglers. The job role of a Data scientist involves making good data prediction on the basis of past behavior. A data scientist does not create solutions but he creates data models to generate such predictions or classifications. He tries to optimize data models so that new modes can be generated for better predictions or classifications. The work for a Data Scientist never ends because data behavior changes over time.

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It may take a lot of time when a data scientist creates its first data model. Data scientists must analyze the data, clean the data, generate new features, decide the best features, train models, try different AI or machine learning algorithms, and measure different online metrics. Each of these steps demands extra efforts and time like weeks or even months.

In this way, the work of Data Scientists does not have hard deadlines. It is tough for any data scientist to estimate when a data model can be deployed in production.

Data Science vs. Software Engineering – Technical & Career Comparison

Choosing between a career in Data Science or Software Engineering can be challenging, especially since both roles are critical in today’s tech-driven world. While they share some common ground, such as programming and problem-solving, they differ significantly in methodology, focus, and career outcomes.

To help you make a well-informed decision, here’s a side-by-side comparison covering everything from work nature and tools to salary expectations:

Data Science Versus Software Engineering: General Key Differences

Now you know the basic concepts of Data Science and Software Engineering, let us look at the major comparisons between the two.

• Data science comprises machine learning, data analytics, and data architecture whereas software engineering is more of a framework that helps to deliver a high-quality software product.
• A data analyst analyzes data and converts it into meaningful information. A software engineer helps to build software with maximum accuracy.
• Data science is all about big data whereas software engineering is the result of demand for new features and functionalities.
• Data science helps in making good business decisions while software engineering makes the development of a software product more structured.
• Data science is mostly driven by data and software engineering is driven by the needs and requirements of end-users.
• Data science utilizes big data technologies to design data patterns. Software engineering is based on different programming languages and tools as per the Company requirements.
• Data extraction is considered one of the most useful steps in data science and requirement gathering and management is taken as a vital step in software engineering.
• Software engineering is all about building apps or systems. Data science is all about building a data model that helps to consolidate, retrieve, and store data from multiple sources or applications.
• Software engineering uses SDLC models to develop a software product systematically. It helps in product development step by step without any vulnerabilities. Data science uses the ETL process for quick data management that involves data extraction, transforming it into useful information and loading the processed data into the system.

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Wrapping up:

An important observation is that software design is made by the software developer based on the requirements as identified by the Data scientist or Data engineer. So, data science and software engineering usually go hand-in-hand. It is also useful to find information and patterns about specific functions  or products in data science.

Effective communication with clients or end-users helps to create more powerful business solutions because requirement gathering is the most important step in the SDLC. To know more about data science vs machine learning and its important tools and techniques, join data science certification program at JanBask Training and become a certified data scientist right away.

FAQs

1. What is the difference between Data Science and Software Engineering?

Data Science focuses on extracting insights from data using statistical analysis, machine learning, and data visualization. Software Engineering, on the other hand, is centered around building and maintaining software systems, applications, and tools through structured programming practices.

2. Which career has better salary potential: Data Scientist or Software Engineer?

Both offer lucrative salaries. In the U.S., entry-level Software Engineers earn around $80,000–$100,000 per year, while Data Scientists start around $90,000–$120,000. Experienced professionals in either field can earn well over $150,000 depending on location, domain, and skillset.

3. Do I need to learn coding for Data Science?

Yes, coding is essential in Data Science. Python and R are the most common languages used, along with tools like SQL for data handling and libraries like Pandas, NumPy, and Scikit-learn for analytics and modeling.

4. Can a Software Engineer transition into Data Science?

Absolutely. Software Engineers already have strong programming skills, which is a big plus. With added knowledge of statistics, machine learning, and data analysis tools, they can successfully switch to Data Science roles.

5. Which is easier to learn: Data Science or Software Engineering?

This depends on your background and interests. If you enjoy logic, structure, and building applications, Software Engineering might feel more natural. If you like working with data, patterns, and solving business problems through insights, Data Science could be more enjoyable.

6. Are there common skills shared between both careers?

Yes. Both careers require strong programming abilities, problem-solving skills, version control (e.g., Git), and understanding of data structures and algorithms. Collaboration and communication are also vital in both roles.

7. Which career path is better for remote work opportunities?

Both fields offer strong remote work potential. Data Science roles often allow flexible and remote work, especially in analytics and product teams. Software Engineering has long been at the forefront of remote-friendly tech careers.

8. Can I learn both Data Science and Software Engineering together?

Yes, you can pursue a hybrid learning path. Many AI/ML engineers today combine knowledge of both. It’s common for developers to pick up data science skills to expand their role and vice versa.

9. What industries hire Data Scientists and Software Engineers the most?

• Data Scientists: Finance, Healthcare, Retail, Tech, Government, and Marketing.
• Software Engineers: Tech, E-commerce, SaaS, Fintech, Gaming, and Cybersecurity.

10. Where can I learn Data Science or Software Engineering online?

You can start with platforms like JanBask Training, Coursera, edX, or Udemy. JanBask offers instructor-led training, real-world projects, resume & interview guidance, and job placement assistance.