Data Science vs Software Engineering: Which to Choose?

William Imoh · Improve this Guide

Software engineering and data science aren’t completely separate; they’re more like two overlapping fields. They share similarities, but each has its own focus. Software engineering centers on creating functional products and systems, while data science focuses on extracting insights from data and building predictive models.

You might want to pick software over data science if you’re good at or interested in building applications, solving engineering problems, and working on system architecture. Otherwise, you might want to go for data science if you’re good at or interested in analyzing data, finding patterns, and developing models to make data-driven decisions.

So, how do you choose between these two exciting and in-demand careers? When I started my career, I constantly switched between writing software that powered applications and analyzing data to drive decisions. Both fields fascinated me, but they required different skill sets and ways of thinking. If you’re torn between data science and software engineering, this guide will help you understand their key differences and choose the path that aligns with your strengths and interests.

Data science vs software engineering

The table below highlights the major differences between software engineering and data science to help you evaluate which one might be the right fit for you:

Aspect	Software Engineering	Data Science
Focus	Software engineers focus on building, maintaining, and scaling software applications	Data scientists focus on analyzing data to extract insights and build predictive models
Key skills	Programming, system design, DevOps, cloud computing	Statistics, machine learning, data visualization
Languages	Python, Java, Go, JavaScript, C#	Python, R, SQL, Scala
Tools and frameworks	Git, Docker, Kubernetes, React, Spring Boot	TensorFlow, PyTorch, Pandas, Jupyter, Spark
Primary goal	Deliver reliable and scalable software systems	Extract meaningful insights from data
Work environment	Agile development, collaboration with product teams	Research-oriented, collaboration with analysts and business teams
Mathematical intensity	Moderate (algorithms, data structures)	High (statistics, probability, linear algebra)
Problem-solving type	Debugging, optimizing performance, scalability	Pattern recognition, prediction, optimization
Typical output	Websites, mobile apps, cloud platforms, APIs	Reports, machine learning models, dashboards
Career paths	Backend Developer, Frontend Developer, DevOps Engineer	Data Analyst, Data Scientist, ML Engineer
Best for people who	Enjoy building and improving systems that users interact with	Love working with data, uncovering patterns, and making predictions

Before looking at these aspects in detail, let’s take a closer look at these two fields.

What is software engineering?

Software engineering is the process of designing, building, testing, and maintaining computer programs. It involves using structured methodologies, programming knowledge, and best practices to develop software that is reliable, efficient, and easy to maintain.

For example, when you work on an app like WhatsApp as a software engineer, you work on different parts, like handling how messages are sent, how the app looks and feels, and much more.

Key components of software engineering

The key aspects of software engineering involve the following:

1. Software development lifecycle (SDLC): Every application goes through several stages before it’s ready for use:
   1. Planning and requirements: Define what the software should do and identify the necessary tools.
   2. Design: Outline how the software will function and how different parts will interact.
   3. Coding: Write the actual program using languages like Java, Python, or JavaScript.
   4. Testing: Find and fix bugs before launching.
   5. Deployment: Release the software for users.
   6. Maintenance: Update and improve the software by adding features or fixing issues.
2. Software design and architecture: This involves structuring the software and deciding how different components work together. It’s like designing a building. You need a solid foundation and a layout that’s organized and easy to navigate.
3. Programming and Development: Writing code is a major part of software engineering. You’ll use languages like Python, JavaScript, or Java to develop web, mobile, and backend applications.
4. Testing and Quality Assurance: Before releasing software, testing helps identify and fix bugs so users don’t run into problems.
5. Deployment and DevOps: Once the software is ready, it needs to be deployed properly. This includes setting up CI/CD pipelines for automated testing and deployment, as well as monitoring systems to track performance and apply updates when needed.

Essential skills you need for a successful software engineering career

To build a successful career in software engineering, you’ll need a mix of technical and soft skills like:

• Programming languages: Coding is the heart of software engineering. A strong grasp of languages like JavaScript, Python, and Java will help you build both frontend and backend systems.
• System design: This involves architecting software so that they are scalable, maintainable, and efficient. You’ll make key decisions about databases, API structures, and how different services communicate within an application.
• Database management: Data is an essential part of your application. You’ll need to know how to structure it, optimize queries, and keep it secure. For example, in an online banking system, customer balances must be stored securely while still allowing fast retrieval for transactions.
• Cloud platforms: Cloud computing enables businesses to run applications without managing physical servers. Familiarity with platforms like AWS, Google Cloud, or Azure will help you deploy and scale applications efficiently.
• Communication and teamwork: You’ll often need to explain technical concepts to non-technical stakeholders, write clear documentation, and collaborate with teams across different functions. Strong communication skills will help you work effectively and drive projects forward.

What is data science?

Data science is the process of collecting, analyzing, and interpreting large amounts of data to find useful insights. It combines programming, statistics, and problem-solving to help businesses and organizations make better decisions.

For example, when Netflix suggests movies based on what you’ve watched before, it’s using data science to predict what you might like.

Key components of data science

The key aspects of data science involve the following:

1. Data collection: This is the first step in a data science project. As a data scientist, you’ll gather information from sources like websites, databases, and sensors for further analysis. For example, in an e-commerce project, you’ll collect data on products people buy to understand shopping trends.
2. Data cleansing and preparation: The data you collect from various sources are raw and won’t come in ready for analysis. You need to clean and organize the raw data to remove missing entries, duplicated items, and missing headers.
3. Data analysis and visualization: This involves the use of charts and graphs to find patterns and trends. For example, you can analyze social media company data to see how many users are active at different times of the day.
4. Machine learning and predictions: Beyond manually identifying trends and patterns, you’ll also use machine learning algorithms to train computers to recognize patterns and make predictions. For instance, a data scientist working in a bank can use machine learning to detect fraud by spotting unusual transactions.

Essential skills you need for a successful data science career

To build a successful career in data science, you’ll need a mix of technical and soft skills like:

• Programming languages: A solid understanding of Python or R is essential for data analysis, machine learning, and automation. SQL is also crucial for retrieving and managing data from databases.
• Statistics and mathematics: You need a strong grasp of probability, distributions, and calculus, as they come in handy when interpreting data and building accurate models.
• Data visualization and machine learning: You need a solid understanding of how to transform complex numbers into easy-to-understand visuals like bar charts, line graphs, and heat maps. Beyond visualization, you also need a good understanding of how to build models that enable computers to recognize patterns and make predictions without being explicitly programmed.
• Big data tools: You need a good knowledge of big data technologies like Hadoop and Spark that let businesses process and analyze large datasets quickly. For example, you can use Spark to analyze millions of users data on a social media platform to personalize content recommendations.
• Problem solving and critical thinking: As a data scientist, you’ll need to question data sources, uncover hidden insights, identify biases, and draw accurate conclusions from complex datasets.
• Communication and teamwork: Even the best insights are useless if they aren’t clearly communicated. You’ll need to translate technical findings into actionable insights that business teams can understand and act on.

Now that we’ve covered software engineering and data science regarding processes, tools, and required skills, let’s explore why software engineering might be a better option for you or if data science is better suited.

What your day-to-day involves

If you’re excited by how big tech companies like Uber or Netflix design, develop, and maintain mobile apps, backend systems, or large distributed systems, then software engineering might be a great fit for you. As a software engineer, you will focus on building functional and efficient digital products, using and developing the technical skills that interest you.

If you’re more interested in extracting insights from data rather than building software systems, data science might be a better fit. As a data scientist, your core focus will be analyzing large datasets, applying machine learning models, and deriving actionable business insights. Unlike software engineering, this role involves more statistical modeling and data interpretation than designing or developing applications.

What skills and background do you currently have?

Are you familiar with a programming language? Have you taken any courses related to computer science or how the Internet works?

If your answer to some of these questions is “YES,” and you enjoyed learning these skills, software engineering might be a great fit. Your existing knowledge and background will be valuable as you build your career in software development.

However, data science might be worth considering if you’re more comfortable working with numbers, statistics, or data visualization rather than building software systems. A strong foundation in statistics, data analysis, and business intelligence is key in this field, making it a good fit for you as you enjoy working with data rather than writing application logic.

Do you enjoy working in a collaborative environment?

As a software engineer, you will work in teams that include both technical (software developers, product managers, and designers) and non-technical (sales and marketing) stakeholders. A typical workday involves writing code, fixing bugs, reviewing pull requests, and discussing features. If you enjoy working collaboratively and like having teammates with whom to brainstorm ideas, then software engineering could be a great fit for you.

While data scientists also collaborate with other stakeholders, their teams are usually smaller, especially in startups or smaller companies. In many cases, a single person or a very lean team often handles data-related tasks, meaning the role may involve more independent research, analysis, and reporting rather than large-scale team collaboration. If you prefer deep, focused work with fewer distractions, data science might be the better choice.

Data science vs software engineering: Where do you see yourself in the future?

Both software engineering and data science are rewarding career paths with diverse opportunities for growth and specialization.

In software engineering, you can work across various industries that continuously adapt and innovate with technology. You also have the flexibility to explore related career paths like:

• Frontend developer
• Backend developer
• Fullstack developer
• DevOps engineer
• Senior software engineer
• Cloud engineer
• Software architect

This broad range of roles increases your chances of landing a job, as there are many positions to apply for.

The average salary of a software engineer in the US is about $105,596 per annum, and software engineers tend to earn even more with experience and specialized skills.

In data science, the career path is more specialized, with key roles like:

• Data analyst
• Machine learning engineer
• Business intelligence analyst
• AI researcher

While the average salary for a data scientist may be slightly higher than that of a software engineer, software engineering offers greater career flexibility and a wider range of job opportunities. If you’re unsure which path to take, consider whether you enjoy building systems (software engineering) or analyzing data to extract valuable insights (data science).

The average salary of a data scientist in the US is about $123,111 per annum.

While data science is leading in AI adoption, software engineering is just as important. Data scientists focus on tasks like collecting, cleaning, and preparing data and training machine learning (ML) models that power AI systems.

But AI isn’t just about data science; software engineers play a key role too. They build the infrastructure and pipelines needed to deploy and manage these models, making it possible to run them efficiently on large datasets.

Plus, AI APIs have made it much easier for software engineers to integrate AI into applications without deep expertise in machine learning. So, if you’re worried about missing out on the AI wave, don’t be. You can still do amazing work in the space.

Ultimately, both software engineering and data science are rewarding careers. To determine which one suits you best, consider doing a litmus test:

• Try building a small application using version control and deploying it to see if software engineering interests you.
• Analyze a dataset with Python, create visualizations, and experiment with basic machine learning to see if data science excites you.

If you’re leaning toward software engineering, check out our software development roadmaps for frontend, backend, or full stack development. If data science resonates with you, explore our data science roadmap to get started.