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Data Science vs Software Engineering: What's the Difference?

While data scientists extract and analyze data sets, software engineers develop systems and applications to meet user and business needs. Learn more from Rice.

Data Scientist collaborating with Software Engineer at a company

If you want a career with a focus on computers and programming, data science and software engineering are both suitable job fields. To choose the right career path for you, it’s helpful to develop a thorough understanding of the differences between software engineering and data science.

In short, data science deals with handling, storing, processing, analyzing, and presenting data, especially large datasets, to gain insights and make predictions that can guide business decisions and strategies and solve real-world problems. Software engineering, on the other hand, focuses primarily on the design, development, and maintenance of software – like apps – and leverages user data to deliver and improve personalized user experiences.

Data scientists work with big data and analytics at scale, making strategic recommendations and solving problems through analysis. Data engineers architect the "pipes" through which big data flows, is stored, and ultimately used by software engineers and data scientists, in a secure and protected manner.

Software engineers develop applications and products that use data to deliver unique or personalized experiences, for example: in machine learning-based recommendation engines like Netflix and Spotify.

This article takes a detailed look at the differences between data science and software engineering including education requirements, skill sets, careers, and more to help you develop a plan to pursue a rewarding career path in the field of your choice.

Differences in Data Science and Software Engineering Methodologies

Data scientists and software engineers use different methodologies for their work. Data scientists use the Extract, Transform, Load process (ETL), while software engineers use the Software Development Lifecycle (SDLC).

ELT

The ETL process refers to the act of making data easier to work with and storing it in a database for processing. While the exact steps vary between examples, they have the same primary elements and end result. Data is extracted from its sources, like online transaction databases, and then it’s ‘transformed’ in a staging area. The transformation process cleans and optimizes data for the infrastructure it will be loaded into.

Once prepared, the data is loaded to an analytics database, ending the ETL process and allowing analysis through database queries to begin. If the analytics database doesn’t support the queries needed, the process starts over, and the data is transformed differently to allow for the desired queries. The insight gained from the analysis is then presented to the business to inform critical organization decisions.

SDLC

The SDLC process refers to the steps required to launch and maintain software, from conception to live updates. There are varying SDLC models, but they include the same core steps that help ensure peak efficiency. SDLC begins by gathering information about what’s needed from the software, such as from data insights or consultations with the business, and assessing the feasibility of meeting user needs. Then the design starts, where aspects like system architecture and hardware requirements are defined.

Once the design is solidified, the project is broken into modules and coding begins, bringing the software to life. After coding, the software is tested, analyzed, and refined until it’s ready for launch (also known as implementation). The final stage in the SDLC process is ongoing development, like adding new features or improving the UI based on user data and feedback.

Differences in Education Requirements

Data science positions tend to have more rigorous education requirements than software engineering roles. Aspiring Data scientists typically have an undergraduate degree in computer science, economics, engineering, or statistics, and almost all go on to attain advanced degrees in those or related fields. Earning at least a master’s degree is essential for significant career advancement and salary opportunities. There are also certifications – like the IBM Data Science Professional or HarvardX Data Science Professional certifications – that may further maximize expertise and earning potential.

Education Requirements for Software Engineers

Software engineers usually earn bachelor’s degrees in computer science, engineering, programming or math, but they may also use certifications like the Secure Software Lifecycle Professional certification or Software Development Professional certification to gain an entry-level position while pursuing an undergraduate degree. Though software engineering careers have less stringent education requirements, the value of a master’s degree can’t be overstated. Having an advanced degree increases your salary potential and can make you a standout candidate for advanced positions.

Differences in Job Title & Salary

Though both fields require knowledge of programming and data utilization, they have different roles, job titles and salary ranges. Data science job titles often speak to the data or business analysis aspects of the field, while software engineering job titles are generally app-, software- or product-based. With a solid understanding of coding, programming and data utilization, it’s possible for a data scientist to become a software engineer – or even vice versa – though you may need supplemental training.

Job Titles & Average Salaries

DATA SCIENCE JOB TITLES AVG ANNUAL SALARY
Senior Data Scientist $160,000
Artificial Intelligence Architect $129,000
Data Analyst $73,000
SOFTWARE ENGINEERING JOB TITLES AVG ANNUAL SALARY
Software Developer $110,000
Build and Release Developer $102,000
Web Developer $77,000
*Source: U.S. Bureau of Labor Statistics, Glassdoor, ZipRecruiter

Differences in Data Scientist and Software Engineer Skill Sets

Data scientists and software engineers have overlapping skill sets, like programming, coding, machine learning, problem-solving, business acumen, organization, adaptability and a willingness to learn. They also both rely on communication and teamwork.

“No one can know and do everything, so you will have to share information —and depend on others to use that information— to achieve success for your project. Collaboration is so important. That means articulating ideas quickly, efficiently, and with good quality,” says Bingbing Huang (MCS '22).

While the skill sets between the disciplines have some overlap, they are used for different purposes.

Data scientists clean, process and analyze data to identify all valuable insights and patterns within a dataset and make predictions about future trends. They then present these findings to business management or investors, conveying the insights’ values and implications to help guide the organization’s decisions. Therefore, they need technical skills like data mining and statistical analysis, as well as communication skills for conveying information.

Software engineers develop, test, and maintain software programs, understanding and analyzing user needs in the process. They need hard skills in debugging, troubleshooting, and software design, and they should have a thorough understanding of concepts like encapsulation, abstraction, and polymorphism to master programming languages used in the industry.

Differences in Tools Used

Both Data scientists and software engineers use tools in their day-to-day operations, though they differ from one another. Data scientists use tools for programming, analysis, data visualization, machine learning, and predictive modeling. Software engineers use tools for software design, testing, programming, and analysis.

Data Science Tools

Common tools used by Data scientists include:

  • Apache Spark: An open-source data processing framework that allows for large-scale data processing that’s too complex for traditional databases
  • MySQL: An open-source relational database management system that allows you to store, manipulate, and query data
  • SAS: A tool designed for data management and analysis, allowing you to generate and analyze statistical models
  • Stats models: A statistical Python module for performing statistical tests, data exploration, and statistical model estimation
  • Tableau: A secure visual analytics platform that creates visualizations of data

Software Engineering Tools

These are tools commonly used by software engineers:

  • GitHub: A software development platform that allows users to collaborate on software projects
  • IntelliJ IDEA: A package of software development tools, including a compiler, debugger and code editor
  • Python: A high-level programming language for web and app development
  • AJAX: A set of web development techniques that allow apps to communicate with servers in the background, without interfering with the information displayed
  • Atom: An open-source customizable text and code editor developed by GitHub

The Future of Data Science and Software Engineering

To summarize, software engineers create applications and programs tailored to user experiences and needs, working intensively with programming and coding functions. Data scientists help organizations make optimal business decisions by working with massive amounts of data to identify patterns and make predictions about the future, using machine learning, predictive models, and databases.

To switch from one profession to the other would take time and additional training, so carefully consider which profession you’re better suited for. Both fields are expected to see much faster job growth than average, according to the U.S. Bureau of Labor Statistics.

If you’re interested in a future in data science or software engineering, Rice University offers a Master of Data Science program (MDS@Rice) and Master of Computer Science program (MCS@Rice). As an online or in-person program, MDS@Rice and MCS@Rice provide a robust, interdisciplinary curriculum with customizable and specialized degrees. Visit us online to learn more about the benefits of MDS@Rice.