The data analyst role is one of the most sought-after in the tech sector, but also one of the most misunderstood: it's not enough to know Excel or to master a single tool. Recruiters evaluate a combination of technical and soft skills that define who passes the filter and who's left out. In this guide we break down what's really asked for today and what the difference between a junior and a semi-senior profile looks like.

If you're considering entering Data or you've already started your training and want to know what to focus on, this is the roadmap of competencies that matter when you're looking for your first or second job as a Data Analyst.

The essential technical skills

These are the competencies that almost no data analyst search leaves out. Mastering most of them makes you a competitive candidate.

SQL: the non-negotiable language of data

SQL remains the most requested skill for any Data role. Most companies store their information in relational databases, and knowing how to query them —with JOINs, subqueries, aggregation and window functions— is the first thing evaluated in a technical interview. If you only learn one tool, let it be this one.

Python for analysis and automation

Python became the second pillar. With libraries like pandas to manipulate data, NumPy for computation and Matplotlib or Seaborn to plot, you can do analyses that would be impossible in a spreadsheet. It also lets you automate repetitive tasks, something teams value more and more.

Visualization: Power BI and Tableau

Data is useless if no one understands it. Tools like Power BI and Tableau let you build clear dashboards that business people can read without help. Knowing how to choose the right chart for each message is a skill in itself. If you're unsure which to learn first, we have a detailed comparison in our article on Power BI vs Tableau for data analysts.

Applied statistics

You don't need a PhD, but you do need to understand averages, medians, deviations, correlation and significance. Without this foundation, it's easy to draw wrong conclusions from a dataset. Statistics is what separates a serious analysis from a superficial reading of numbers.

The soft skills that make the difference

This is where many technical profiles lose opportunities. Recruiters know it, and that's why they evaluate them as much as the code.

• Data storytelling: translating a finding into a story that drives a decision. A good insight poorly communicated generates no impact.

• Communication with non-technical areas: explaining results to marketing, sales or management without jargon.

• Critical thinking: questioning the quality of the data and the business questions before starting the analysis.

• Curiosity and autonomy: looking for the "why" behind the numbers without being asked.

Junior vs. semi-senior: how they differ

Understanding this difference helps you know what to expect and how to grow.

The leap from junior to semi-senior is rarely technical alone: it's about gaining business judgment and autonomy. According to the World Economic Forum's Future of Jobs Report 2025, data analysis and analytical thinking are among the fastest-growing competencies globally, which sustains the demand for these profiles. Studies like Stack Overflow's annual survey also confirm SQL and Python as core technologies for those who work with data.

Recommended Coderhouse courses

To build this profile in an orderly way, it's worth combining technical data training with a foundation in artificial intelligence, which today runs through all analytical work:

• Entry level: the Data Analytics Course gives you the fundamentals of analysis, SQL and visualization to take your first steps.

• Visualization tools: the Power BI Course focuses you on professional dashboards, one of the most requested skills.

• Advanced profile: the Data Scientist Career goes deeper into Python, statistics and machine learning to scale your career.

• Key complement: the Introduction to Artificial Intelligence Course helps you add AI to your analysis workflow.

Ready to start? Choose the course that fits your level and start building the profile that data teams are looking for.

Frequently asked questions

Do I need to know how to program to be a data analyst?

Yes, although not at a developer's level. With solid SQL and an intermediate command of Python you cover most of an analyst's tasks. Programming here is a tool for analyzing, not an end in itself.

What do I learn first: SQL or Python?

SQL. It's the most requested skill and the foundation for working with almost any data source. Once you master it, Python adds analysis and automation capabilities.

How long does it take to be ready for a first job?

With consistent dedication, between six months and a year is a realistic range to reach a solid junior level, as long as you add a portfolio with your own projects that demonstrate your skills.

Is Power BI or Tableau better?

It depends on the market and the company. Power BI has a lot of adoption in companies that use the Microsoft ecosystem; Tableau is strong in advanced visual analysis. Learning one makes it easier to understand the other, so start with the one that appears most in the searches that interest you.

Do soft skills weigh as much as technical ones?

In practice, yes. Two candidates with a similar technical level are differentiated by their ability to communicate findings and understand the business. Data storytelling is usually the tiebreaker.