Becoming a data analyst without prior experience is totally possible, and in Argentina the demand has been growing strongly. The key is not a grandiose title, but a clear roadmap and a portfolio that demonstrates you know how to transform data into decisions.

The data analyst role is one of the most accessible entry doors to the world of technology, because it combines tools you can learn in order and a way of thinking that many already bring from other professions: organizing information, finding patterns, and telling a story with numbers.

What a data analyst does

Concretely, a data analyst receives business questions ("why did sales drop?", "which customers are leaving?") and answers them with data. Their day-to-day includes cleaning databases, crossing sources, calculating metrics, and building reports or dashboards that the team can understand without being technical.

The roadmap, step by step

Step 1: Solid Excel

Before jumping to complex tools, master Excel or Google Sheets: pivot tables, lookup functions, data cleaning, and charts. It's the conceptual base of everything else and many companies still use it daily.

Step 2: SQL

SQL is the language for querying databases and is, probably, the most requested skill of the role. Learning to do SELECT, filters, JOIN, and aggregations opens the door to working with real data, not toy files. In fact, in the Stack Overflow Developer Survey 2024 SQL appears year after year among the most used technologies in the world.

Step 3: Visualization with Power BI or Looker Studio

A good analysis is lost if no one understands it. Learning a visualization tool lets you transform queries into clear dashboards, which is what the decision-maker ultimately sees.

Step 4: Basic statistics and a little Python

You don't need to be a statistician, but understanding averages, medians, correlation, and sampling avoids erroneous conclusions. Python adds up when volumes grow or you want to automate.

What projects to build for your portfolio

Without work experience, your portfolio is your calling card. Ideas that work:

• Analyze a public dataset (transportation, health, sports) and answer 3 concrete questions.

• Build a dashboard of fictional sales with actionable insights.

• Document the complete process: where the data came from, how you cleaned it, and what you found.

What differentiates a strong portfolio from a weak one is not the tool, but the question you answer. To understand what companies are looking for today, it's worth seeing which are the most sought-after tech profiles by companies in Argentina.

How much you earn when you enter the role

Salaries vary according to the company, the modality (local or getting paid in dollars), and seniority, but the junior data analyst starts in a competitive range within tech and scales quickly by adding advanced SQL and experience. Regional demand sustains those numbers: according to the World Economic Forum's Future of Jobs Report 2025, data analysis and data scientist roles are among those with the highest projected growth globally.

Recommended Coderhouse courses

To build this base in an orderly way and with a focus on employability:

• To understand the ecosystem: the Introduction to Artificial Intelligence Course places you in how data, models, and decisions connect.

• For the technical side: the AI Engineering Course goes deeper into working with data at scale and its practical application.

• To build products on data: the AI Products Course helps you think of data as an input for real solutions.

Frequently asked questions

Can I be a data analyst without knowing how to program?

You can start with Excel, SQL, and a visualization tool, which don't require traditional programming. Python helps later, but it's not a blocker to enter the role.

How long does it take to be ready to apply?

With constant study, between six and nine months is usually enough to have the base skills and a presentable portfolio. The important thing is to practice with real data, not just watch tutorials.

What's the difference between a data analyst and a data scientist?

The analyst answers business questions with existing data and reporting; the data scientist builds predictive models and works closer to machine learning. The analyst is the natural entry point.

Is my experience in another profession useful?

A lot. If you come from finance, marketing, health, or logistics, you already understand a business domain, and that lets you do more relevant analyses than someone purely technical without context.