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Understanding how to become a data scientist requires clarity about the role, the skills it demands, and the sequence in which those skills are usually developed. Data science is not a single skill but a combination of statistical reasoning, programming, data handling, and communication, applied to real organizational problems.

This guide explains what data scientists do, the skills you need, and the steps most people follow to enter the field, including realistic timelines, difficulty and costs.

What does a data scientist do?

A data scientist works with data to support decision-making. This includes defining analytical questions, collecting and preparing data, exploring patterns, building statistical or machine learning models, evaluating results, and communicating findings to non-technical stakeholders.

In practice, the role often overlaps with data analysis, applied machine learning and data engineering. Responsibilities vary by organization. Some roles focus on experimentation and business insights, while others emphasize predictive modeling or automation.

Because of this variation, job titles alone are not a reliable guide. What matters is the type of work being done: problem framing, data preparation, modeling, and interpretation.

What skills do you need to become a data scientist?

Most data science roles require competence across a common core of skills.

Programming is essential. Python is the most widely used language in data science, and SQL is required to work with relational databases. Together, they cover most day-to-day tasks.

Statistics and probability are equally important. These skills support hypothesis testing, model evaluation, and an understanding of uncertainty and bias.

Data preparation is a major part of the job. This includes cleaning datasets, handling missing values, joining sources, and creating features suitable for analysis.

Data visualization and communication matter because results must be understood and acted on. Clear charts and concise explanations are often more valuable than complex models.

Many roles also expect familiarity with machine learning fundamentals, including model training, validation, overfitting, and baseline comparisons.

How to become a data scientist step by step

Step 1: Define the type of data science role you are targeting

Before learning tools or techniques, clarify the kind of role you are aiming for. Data science roles differ across industries and teams.

Some roles emphasize product analytics and experimentation. Others focus on predictive modeling or applied machine learning. Industry context also matters, as expectations differ between sectors such as healthcare, finance, and technology.

Defining a target role helps you prioritize the right skills and choose relevant projects.

Step 2: Build foundational knowledge in a logical sequence

Most learning paths are more effective when skills are developed in order.

Start with basic statistics and probability, followed by programming fundamentals. From there, focus on data cleaning and exploratory analysis. Only after these foundations are in place does machine learning become useful.

This sequence reflects how data science work actually happens and reduces gaps that often appear when advanced topics are learned too early.

Step 3: Learn the tools used in professional environments

Data scientists rely on a small set of tools consistently.

Python and its data libraries are central to analysis and modeling. SQL is used to access and manipulate data stored in databases. Version control tools such as Git support collaboration and reproducibility.

Visualization libraries and business intelligence tools are also common, especially in roles that support decision-makers directly.

Learning these tools alongside practical tasks is more effective than studying them in isolation.

Step 4: Build applied projects that demonstrate competence

Projects are the primary way to show that you can apply data science skills.

Strong projects are structured around a clear question, use real data, document cleaning and assumptions, and evaluate results using appropriate metrics. They also explain conclusions in plain language.

A small number of complete, well-documented projects is more effective than a large number of unfinished ones. Projects should align with the type of role you are targeting.

Step 5: Prepare for entry into the job market

Before applying for roles, ensure your portfolio, resume, and technical skills are aligned with job descriptions.

Many candidates enter data science through adjacent roles such as data analyst, business intelligence analyst, or research analyst. These roles provide exposure to real data, stakeholders, and production environments.

This step bridges the gap between learning and professional practice.

How long does it take to become a data scientist?

The time required depends on your background and goals. Candidates with prior experience in programming, mathematics, or quantitative fields often progress faster because they can focus on applied data work. Those starting without technical experience typically need more time to build foundational skills.

Rather than focusing on months or years, it is more useful to track progress through milestones:

– Ability to clean and analyze datasets independently

– Ability to build and evaluate basic models

– Completion of multiple role-relevant projects

How hard is it to become a data scientist?

Data science is challenging because it combines multiple disciplines. You are expected to write code, understand statistics, work with imperfect data, and explain results clearly.

Difficulty often comes from applying concepts to real datasets rather than learning theory. Real-world data is incomplete, inconsistent, and constrained by business requirements.

The complexity becomes manageable when scope is limited. Focusing on one programming language, one role type, and one industry reduces cognitive load and accelerates progress.

How to become a data scientist with no experience

“No experience” usually means no paid experience rather than having no skills. In this case, your goal is to replace formal job history with credible evidence of competence. This includes projects, documentation, and clear explanations of your work.

Entry through adjacent roles is common and practical. These positions allow you to apply data skills in a professional setting while continuing to develop toward a full data scientist role.

Consistency and relevance matter more than background. Employers look for proof that you can handle real data and make defensible decisions.

How much does it cost to become a data scientist?

Costs vary depending on how you learn.

Self-directed learning using free or low-cost resources can be done with minimal financial investment, but it requires discipline and structure.

Online certificates and professional programs typically involve monthly subscriptions or fixed fees. These costs range from hundreds to a few thousand dollars, depending on duration and provider.

Data science bootcamps represent the highest financial cost. Tuition often falls in the high four-figure to low five-figure range, with differences based on format, support, and location.

Beyond tuition, time is a significant cost. Programs that prioritize applied work and portfolio development tend to offer better returns on that investment.

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If you want a practical, outcomes-focused way to move into data roles, explore the program to review the curriculum, speak with the admissions team, or start your application.

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.