Changing direction in your working life is a big move. It takes honesty to admit that what you’re doing now no longer feels stable, meaningful, or sustainable.
If you’re reading this, you’re probably considering a move into tech, and two career paths come up again and again: data science and software engineering. Both offer strong salaries, long-term demand, and clear opportunities for growth.
But the moment you start researching, everything gets noisy. One source says data science is the “hotter” field. Another says software engineering is “easier to break into.” Someone else insists you need advanced math, while another says you don’t. It’s hard to know what the actual difference between data science and software engineering is, and what’s just internet confidence.
That's what this guide is for: explaining the real differences and helping you choose a path that fits how you think, what you enjoy, and the kind of future you want to build.
Role Descriptions: Data Scientists vs. Software Engineers
Before choosing a direction, it helps to understand what these roles actually look like in real working environments.
What Is a Data Scientist?
Data scientists collect, categorize, and interpret data to extract meaning that helps a company make smarter decisions. The strongest data scientists ask deep questions and see patterns other people miss. They also use data visualization tools to communicate those insights to teams who rely on them: product managers, marketing teams, executives, and more.
Data science blends several disciplines: computer science, math and statistics, machine learning, and domain knowledge. Because the field is broad, data scientists often show up under different job titles depending on the company and specialization. Some focus on financial modeling, others on scientific research, and others on building prediction systems.
Common job titles: Data Scientist, Machine Learning Engineer, Quantitative Analyst, Senior Data Analyst, Algorithmic Trader, Bioinformatics Scientist, Research Scientist
Common technical skills: Python, R, SQL, Tableau or Power BI, Apache Spark, Hadoop, Databricks, SAS, MATLAB
Not all roles require all of these. Most people start with Python, basic statistics, and SQL, then grow into more specialized tools as their career develops.
What Is a Software Engineer?
Software engineers use their skills to develop software solutions we use every day: everything from apps and websites to internal systems, automation tools, and complex platforms. Their job is to take an idea and turn it into something that actually works for the people who need it.
Most software engineering projects move through a predictable software development lifecycle: analyzing needs, planning the architecture, developing features, testing, launching, and maintaining the software systems once they're live.
Common job titles: Front-End Developer, Back-End Developer, Full-Stack Developer, Application Developer, Systems Engineer, Embedded Software Engineer
Common technical skills: JavaScript, Python or Java, C++ or other compiled languages, SQL, Git and version control, object-oriented programming, frameworks like React, Node.js, or Spring (depending on the role)
Again, you don’t learn all of these at once. You start with one language, one framework, and one problem and build from there.
Key Differences Between A Data Scientist and Software Engineer
Data science and software engineering are often lumped together as “tech jobs,” but the day-to-day work, mindset, and learning journey differ in ways that matter.
1. The Core of the Work
Data science involves making sense of information. Data science teams dig into vast amounts of data, explore patterns, and uncover insights that influence business decisions. The job revolves around asking good questions and translating complex findings into something clear and actionable.
Software engineering focuses on building and maintaining software applications. When something breaks, you fix it. When something needs improving, you enhance it. The goal is to create reliable, functional solutions that people can use every day.
2. The Work Style
Software engineering often suits people who enjoy structure, clear steps, and steady progress. There’s a craft-like rhythm to writing code, testing it, refining it, and watching something take shape.
Data science may suit people who like to explore, interpret, and understand the “why” behind trends. The work often involves open-ended questions and the freedom to investigate different possibilities before arriving at an answer.
3. The Learning Experience
Software engineering builds in layers. One concept leads directly to the next. You’ll write lots of code and see direct, immediate feedback.
Data science introduces several areas at once: coding, data analytics, statistics, and visualization. You learn them gradually, but it’s a more varied journey.
4. Entry-Level Accessibility
Software engineering tends to have more clearly defined entry-level roles, making it slightly more accessible on paper.
Data science roles sometimes require a bit more specialization in the early stages, but strong portfolios and real-world data science projects close the gap quickly, especially if you have past experience in business, research, finance, or communication.
5. Ambiguity
One of the biggest differences between data scientists and software engineers comes down to how each handles uncertainty. Software engineers work in a world where logic rules the outcome. A feature either works or it doesn’t. The system follows a defined set of rules: for example, "waive the fee if the customer is platinum status and decline it otherwise."
Data scientists need to accept that they rarely get perfect information, and answers aren’t always absolute. Instead of a single “right” outcome, they're often estimating what’s most likely to happen based on the patterns they uncover: like "a customer with traits X, Y, and Z is highly likely to upgrade their seat."
If you prefer clear logic and concrete answers, software engineering tends to feel steadier. If you’re comfortable with exploration, nuance, and shades of gray, a data scientist's role offers space to work with uncertainty and still produce meaningful results.
What It Takes to Enter Software Engineering vs. Data Science
Let’s look at the skills you’ll build in each path. The goal here is to give you a clear picture of what’s ahead, so the learning curve feels less mysterious.
Skills You’ll Need as a Data Scientist
A career in data science blends technical ability with analytical thinking and clear communication. The role is equal parts curiosity, problem-solving, and storytelling. Key skills include:
1. Python and SQL. These are the workhorses of the field. You’ll use them for data manipulation, analysis, and insights. Most beginners start here.
2. Statistics. This is important to understand patterns, probabilities, and relationships in the data.
3. Data analysis techniques. This includes spotting trends, answering business questions, and summarizing insights clearly.
4. Machine learning basics. As you advance, you’ll learn to build predictive models using libraries like scikit-learn. This helps you forecast outcomes and automate insights.
5. Big data tools. Many companies work with massive datasets, so familiarity with technologies like Hadoop or Spark becomes useful as you advance.
6. Visualization skills. Charts, dashboards, and clear visuals help you turn your findings into something others can understand and act on.
7. Communication skills. Insights don’t speak for themselves. You translate your findings into recommendations that help teams make decisions. Many people underestimate how valuable this skill is.
If you've worked in roles where you solved problems, worked with stakeholders, or interpreted information, you already have strengths that translate well into this field.
Skills You’ll Need as a Software Engineer
A career in software engineering involves building, things, and improving things every day. These are the skills that form the backbone of the role:
1. Strong programming foundations. You should learn how to think like an engineer: break problems into clear steps, write clean code, and understand how different parts of a system connect. This is the starting point for everything else.
2. Debugging and troubleshooting. You need to learn to identify what’s broken and fix it systematically.
3. Logical thinking. Coding is mostly about logic. If you can follow steps, understand rules, and stay patient when things go wrong, you’re already halfway there.
4. Building projects. From small apps to bigger systems, project work helps you prove your skills to employers.
5. Understanding software design and architecture. You’ll learn how systems are built at a high level and go deeper as you progress.
6. Collaboration skills. You’ll work with other software developers, designers, and product teams.
If you enjoy building, experimenting, and seeing immediate results from your work, software engineering will feel rewarding.
Do You Need a Degree in Computer Science?
Not necessarily. A bachelor's degree in computer science can help both software engineering and data science careers, but it’s not a hard requirement for either. What matters far more is your ability to learn the core skills, build real projects, and show that you can solve problems the way the job demands.
Many software engineers and data scientists didn’t start with a technical degree. They learned through structured programs, online courses, mentorship, or by building projects on their own. Skill and evidence of ability carry far more weight than a diploma.
What matters most is having:
- A clear roadmap, so you’re learning in the right order instead of jumping between random tutorials.
- Consistent guidance and feedback, so you stay on track, avoid common mistakes, and finish the work that actually moves you forward.
- Projects that prove your ability, because a portfolio is the strongest signal you can send to an employer.
Career Growth and Salary Comparison
In the US, average salaries sit roughly around $122,738 for data scientists and $147,524 for software engineers, with higher compensation in major tech hubs and industries that offer bonuses or equity.
In Europe, salaries are lower on paper: €58,000–€90,000 for mid-level data scientists and €56,000–€80,000 for software engineers in Western Europe. But benefits, work–life balance, and overall living costs often shift the equation.
But salary shouldn’t be the only decision factor.
High salaries don’t matter if the work exhausts you or doesn’t align with how you think. The biggest predictor of long-term income is staying in the field long enough to grow.
How to Decide Which Career Path Fits You
Here’s the simplest, most honest framework for career switchers:
1. What energizes you more: building or understanding?
- Building things → software engineering
- Understanding patterns → data science
2. What type of problems feel more natural to you?
- Step-by-step logic → software engineering
- Open-ended questions → data science
3. How do you feel about troubleshooting?
- Genuinely enjoy fixing things → software engineering
- Prefer interpreting information → data science
4. Do you want a highly structured job or a more exploratory one?
- More structured → software engineering
- More exploratory → data science
5. Do you enjoy details?
Software engineers live in details. Data scientists use details to tell bigger-picture stories.
6. Can you see yourself talking about data with others?
Data science requires communication. If you enjoy explaining your thinking, this is an asset.
7. Which path feels less draining to imagine yourself learning?
This question matters more than people realize. If one field feels slightly more exciting or slightly less intimidating, that’s a real clue.
Not sure which path fits you best? Try the Turing College career quiz and see whether your strengths lean more toward data science or software engineering.
How to Take the First Steps Today
Here’s how to move from curiosity to action without overwhelming yourself:
1. Try a small taste of each field. Spend 20 minutes writing a bit of code. Then spend 20 minutes exploring a small dataset. Notice which one sparks curiosity or makes you want to dig deeper.
2. Look at beginner-friendly projects online. Browse GitHub, Kaggle, or simple tutorials. See which types of work feel more natural to you.
3. Talk to people who switched. Conversations with engineers or data professionals cut through buzzwords fast. They’ll tell you what the job actually feels like day to day.
4. Don’t start by learning everything. The biggest trap at the beginning is chasing every topic. Your early steps should be small, clear, and confidence-building.
5. Look for a structured, guided learning environment. A roadmap, solid guidance, accountability, and a supportive community will speed up your progress and keep you moving when motivation dips. Learning alone is possible, but it’s slower and a lot more frustrating.
6. Start building simple projects early. Projects turn learning into proof. They show both employers and yourself that you can apply what you’re learning in a practical way.
How Turing College Helps People Succeed in Both Paths
Learning tech skills on your own often feels chaotic. Guidance, structure, and real accountability make all the difference. Turing College is built with that in mind, helping people gain real competence without the overwhelm.
1. Programs built for true beginners. Turing College's Data Science and Software & AI Engineering programs start at the foundational level. They don’t assume prior knowledge, and they guide you step by step.
2. Learning through real work. You don’t learn by memorizing theory. You learn by doing. From day one, you work on practical projects, building apps, analyzing datasets, and solving real problems. By the time you’re done, you have a portfolio that actually proves what you can do.
3. Support from industry mentors. Everyone gets stuck. What matters is having someone who can get you unstuck. Turing College gives you direct access to 150+ mentors who guide your thinking, help you troubleshoot, and push you toward professional-level problem solving.
4. A supportive community. You’re surrounded by people who are serious about growing and who understand your struggle better than anyone.
5. Flexible but structured learning. You move at a manageable pace. The program adapts to your life but still keeps you progressing, helping you stay consistent without burning out.
6. Career support designed for a career switch. You’ll refine your story, build a strong CV and LinkedIn, and prepare for interviews with companies hiring engineers and data professionals.
The approach is straightforward: Teach you what matters. Skip what doesn’t. Build skills employers actually want.
Time for Your Next Move
A career switch is all about using your experience in a new context — one with more stability, more growth, and more long-term opportunity.
Whether you choose to become a data scientist or software engineer, you’re choosing to build something new and to bet on yourself. And you don’t need to have everything figured out right now. Just choose a direction that feels right and take your first step.
If you want a structured, supportive, and practical way to make that step, Turing College is ready to help you turn intention into reality.