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To become a data scientist, you usually need to build skills in 1) statistics, 2) programming, 3) SQL, 4) data analysis, 5) machine learning and 6) communication – in that order. For most people, the path looks like this:

Learn the fundamentals →

Practice with real datasets →

Build a portfolio →

Apply for entry roles in data or analytics.

The exact route depends on your background. If you already have experience in coding, math or analytics, you may be able to move faster. If you are starting from scratch, it will likely take longer, but the path is still realistic when broken into clear stages.

This guide explains what you need to learn, the steps to follow, how long it can take, how hard it is and how to get started even with no formal experience. We also conclude with key insights for women hoping to access this area.

What do you need to become a data scientist?

Before getting into the full roadmap, it helps to know what employers are usually looking for. In most cases, you need a mix of technical skills, applied experience and proof that you can solve real problems with data.

At a minimum, that usually means a working knowledge of statistics and probability, one main programming language such as Python, SQL for handling data in databases, and the ability to clean, analyze and visualize messy real-world datasets. It also means being able to explain what you found in a clear and useful way.

You do not need to know everything at once. What matters more is learning in a logical sequence and building evidence of your skills through projects that reflect the type of role you want.

How to become a data scientist step by step

1. Choose the kind of data role you want

Data science is a broad field, and not every role looks the same. Some positions are closer to business analysis and experimentation. Others focus more on predictive modeling, machine learning or product development.

That is why the first step is to define your direction. Do you want to work in finance, healthcare, consulting, technology or another industry? Are you more interested in analytics, modeling or AI applications? The clearer your target role is, the easier it becomes to choose the right tools, projects and learning path.

This also helps you avoid a common mistake: trying to learn every part of data science at the same time. A focused route is usually more effective than a broad but shallow one.

2. Build your foundations in the right order

A strong data science profile starts with fundamentals. Statistics and probability come first because they help you understand patterns, uncertainty, testing and model performance. Programming comes next, because it allows you to work with data directly and automate analysis.

From there, move into data cleaning, exploratory analysis and visualization. These are core parts of real data work and often matter more early on than advanced machine learning. Once those foundations are solid, machine learning becomes much easier to understand and apply properly.

This order matters. Many people jump straight into models too early, but strong data scientists are usually the ones who can clean data well, ask good questions and interpret results with care.

3. Learn the tools used in real data teams

Most aspiring data scientists should focus first on a manageable tool stack rather than trying to master everything. Python is usually the main language to learn for analysis, modeling and data workflows. SQL is essential for querying and manipulating structured data. Git is useful for version control and collaboration.

You will also benefit from knowing how to visualize findings, whether through Python libraries or tools used in business settings. Depending on the role, you may later expand into machine learning frameworks, cloud tools or business intelligence platforms.

The key is to learn tools through practice. It is much easier to retain them when you are using them to answer real questions or complete projects, rather than studying each one in isolation.

4. Build projects that prove you can do the work

Projects matter because they turn learning into evidence. A good project shows more than technical output. It shows that you can define a problem, work with raw data, make decisions about your method, evaluate results and explain what they mean.

A few solid projects are usually more valuable than a large number of half-finished ones. Aim for work that is complete, well-documented and relevant to the role you want. For example, if you are interested in product or business-focused roles, projects tied to user behavior, forecasting or decision-making can be especially useful.

Your portfolio should show process as well as outcome. Employers want to see how you think, not just that you can produce charts or models.

5. Apply through realistic entry points

Not everyone gets their first job with the title “data scientist.” In fact, many people enter the field through adjacent roles such as data analyst, business intelligence analyst, junior data engineer or research analyst.

These roles are valuable because they give you real exposure to data systems, business problems and stakeholder communication. They also help you develop the professional judgment that employers look for when hiring for more advanced data science positions.

If your long-term goal is data science, an adjacent first role is not a detour. For many candidates, it is the most practical path in.

How long does it take to become a data scientist?

There is no single timeline because it depends on your starting point. Someone with a background in programming, quantitative research or analytics may be able to build toward entry-level roles much faster than someone starting with no technical base.

A more useful way to think about timing is by milestones. You are moving into data science when you can clean and analyze data independently, write code with confidence, build and assess basic models, and show completed projects that match the kind of role you want.

Progress tends to become clearer when you stop asking whether you are “ready” in the abstract and start asking whether you can already perform the tasks required in junior or adjacent roles.

How hard is it to become a data scientist?

Data science is challenging because it combines several disciplines at once. You need technical skills, statistical reasoning, comfort with ambiguity and the ability to explain your work clearly. That combination is what makes the role attractive, but it is also what makes the path demanding.

For most learners, the hardest part is not memorizing theory. It is applying ideas to messy, incomplete data in a way that makes sense for real decisions. This is why practical repetition matters so much.

The process becomes more manageable when you narrow your focus. One language, one target role and one project style is usually a better starting point than trying to cover the whole field at once.

How to become a data scientist with no experience

No experience does not usually mean no ability. More often, it means no formal job history in the field. In that situation, your goal is to replace missing experience with proof of competence.

That proof comes from projects, documentation, GitHub repositories, case studies and clear explanations of your work. If you can show how you approached a problem, handled the data and justified your conclusions, you are already building credibility.

It is also worth targeting related roles first. Data analytics and business intelligence positions often give you the hands-on experience that helps you grow into a full data science role over time.

How much does it cost to become a data scientist?

The cost depends on the route you take. Self-directed learning can keep financial costs relatively low, but it requires discipline, structure and time. Online certificates and short programs can add support and clarity, but they vary widely in price.

More intensive options such as bootcamps or specialized graduate programs involve a larger investment. The trade-off is often greater structure, stronger project work, career support and a more guided transition into the field.

It is also important to think beyond tuition. Time, consistency and the quality of your practical work are part of the cost too. The most effective route is usually the one that helps you build usable skills and credible evidence of them as efficiently as possible.

Where a specialized master’s can fit

A specialized master’s can make sense if you want a more structured, accelerated and outcomes-focused route into data roles. This can be especially useful if you are changing fields, want to build a stronger portfolio or need a clearer connection between technical skills and business application.

IE School of Science & Technology’s Master in Business Analytics & Data Science is designed around that kind of transition. You develop capabilities in business analytics, data science, machine learning and AI, with a strong emphasis on applying data to decision-making, product improvement and organizational change.

You can choose the format that fits your goals: full-time over 11 months or part-time over 17 months, delivered in person or in a blended format, with Madrid as the base and an international component built into the experience.

Women at IE School of Science & Technology

At IE School of Science & Technology, support for women in STEM is part of the wider academic and professional environment. The goal is not only to help women build technical skills, but also to strengthen the confidence, visibility and leadership needed to grow in high-impact industries.

That support extends beyond the classroom. Through mentorship, networking opportunities and close contact with faculty and industry professionals, students gain access to guidance from people who understand the realities of building a career in science and technology.

These initiatives help students navigate challenges such as career progression, work-life balance and leadership development while strengthening their professional networks and sense of belonging in the tech sector.

The community is reinforced through events and outreach initiatives that connect students with inspiring role models and emerging opportunities in science and technology. Programs such as Women in STEM Day bring together researchers, entrepreneurs and industry leaders for workshops, panels and discussions on topics ranging from entrepreneurial leadership to salary negotiation. Together, these initiatives create a supportive environment where women can connect, collaborate and step confidently into the future of science and technology.

We detail everything in our dedicated page for Women at IE School of Science & Technology.

Want to see how we support our students? Read our guide on IE mentorship in tech.

Want more information on what you can earn? Read our guide on data analyst salaries in Europe.

Need to find your best option? Read our guide on how to choose the best data analytics program.

Need reassurance on career outlook? Read our guide on the global demand for data science.