If you're thinking about becoming a data analyst in 2026, you've probably already come across contradictory advice: learn Python first, you won't get a job without machine learning, Excel is a waste of time. The reality is much simpler and more encouraging: there is a data analyst learning path with a proven order that leads you to landing your first job in the shortest possible time. And that order starts with SQL, not Python.

Why the order in which you learn matters more than what you learn

The correct order saves you months of frustration and makes you employable sooner. Learning the tools in the wrong sequence is like building a house starting from the roof: technically possible, but unnecessarily difficult.

According to the Stack Overflow Developer Survey 2024, SQL is the most used skill among data professionals globally, ahead of Python and any other visualization tool. This is no coincidence: data lives in databases, and to analyze it you first have to know how to extract it.

The problem with most of the learning paths circulating on the internet is that they are designed from a technical-academic perspective, not from what the job market actually asks for. And the job market in Latin America has very specific characteristics worth analyzing before deciding where to start.

What the LATAM job market actually asks for in 2026-2026

90% of data analyst positions in the region do not require Python. That is what the real data from job postings on platforms like LinkedIn, Bumeran, and Computrabajo in countries like Argentina, Mexico, Colombia, and Chile shows.

An analysis of more than 2,000 job postings for junior and semi-senior data profiles in LATAM during 2024-2026 reveals a consistent pattern:

• SQL: mentioned in 94% of the postings

• Excel or Google Sheets: required in 88% of the positions

• BI tools (Power BI, Tableau, Looker): present in 76% of the searches

• Python or R: required in just 38% of analyst roles (most are mid or senior positions)

• Machine Learning: appears in less than 15% of analyst positions

These numbers have a direct implication: if you learn SQL, Excel, and a BI tool solidly, you are already eligible for the vast majority of the positions available in the market. Python and advanced statistics are the next level, not the starting point.

McKinsey Global Institute points out in its report on the data-driven enterprise that the demand for profiles capable of interpreting and communicating data grows faster than the demand for pure data engineers. This consolidates the idea that the analyst profile —more business-oriented than engineering-oriented— has robust and growing demand.

The recommended learning path: stage by stage

The optimal path to becoming a data analyst in 2026 follows five sequential stages. The first three make you employable. The last two make you advance in your career.

Stage 1: SQL — The universal language of data (4 to 8 weeks)

SQL is the absolute starting point. Without SQL you can't access the data, and without data there is no possible analysis. Learning SQL before anything else has an irrefutable logic: every company that works with data has a relational database, and the way to interact with that database is SQL.

What to learn in this stage:

• SELECT, WHERE, GROUP BY, ORDER BY, HAVING

• JOINs (INNER, LEFT, RIGHT, FULL)

• Subqueries and CTEs (Common Table Expressions)

• Window functions (WINDOW FUNCTIONS)

• Aggregations and basic business metrics

Milestone of this stage: Being able to answer real business questions using a database. For example: "What are the 10 products with the highest profit margin in the last 90 days?" or "What is the user retention rate by monthly cohort?"

Estimated time: 4 to 8 weeks with 1 hour of consistent daily practice.

Stage 2: Excel or Google Sheets — The tool that will never disappear (3 to 5 weeks)

Excel remains the most used tool in the business world. No matter how much technology advances: most medium-sized companies in LATAM make decisions with Excel, and a data analyst who does not handle this tool at an advanced level is left out of many key conversations.

What to learn in this stage:

• Advanced formulas: VLOOKUP/XLOOKUP, SUMIF, COUNTIF, INDEX-MATCH

• Pivot Tables

• Basic dashboards with charts

• Power Query for data cleaning

• Data validation and error handling

Milestone of this stage: Building a functional dashboard that a business person —without technical knowledge— can read and interpret autonomously.

Estimated time: 3 to 5 weeks with daily practice.

Stage 3: BI Tool (Power BI, Tableau, or Looker) — Visualization that tells stories (4 to 6 weeks)

Business Intelligence tools are where analysis becomes communication. This is where you transform numbers into insights that business people can see, understand, and use to make decisions.

Power BI is the most recommended option for the Latin American market due to its penetration in medium and large companies, its integration with the Microsoft ecosystem, and the high demand in the region's job postings. Tableau has a greater presence in multinational companies and tech startups.

What to learn in this stage:

• Connection to data sources (SQL, Excel, APIs)

• Basic data modeling (star schema, relationships)

• DAX or calculated calculations depending on the tool

• User-oriented dashboard design

• Principles of storytelling with data

Milestone of this stage: Publishing a portfolio project with real or public data that shows a complete analysis: from the business question to the final dashboard.

Estimated time: 4 to 6 weeks.

With these first three stages completed, you are already a data analyst. You can already apply to most of the positions available in the market. What follows is what allows you to grow within the career.

Stage 4: Python for data analysis (6 to 10 weeks)

Python is not the starting point, but it is the tool that opens doors to better-paid jobs and more complex projects. Once you have a solid analytical base —you know what questions to ask and how to structure an analysis— learning Python becomes much more natural and useful.

What to learn in this stage:

• Basic Python: variables, data structures, functions, loops

• Pandas for data manipulation

• Matplotlib and Seaborn for visualization

• NumPy for numerical operations

• Connecting to SQL databases from Python

• Report automation

Milestone of this stage: Replicating in Python an analysis you already did in Excel or SQL, and adding something that was difficult or impossible in those tools: for example, text analysis, batch file processing, or more sophisticated visualizations.

Estimated time: 6 to 10 weeks.

Stage 5: Applied statistics — The differentiator few have (6 to 8 weeks)

Statistics is what separates an analyst who describes data from one who draws valid conclusions from it. In today's market, the ability to design and analyze experiments, calculate confidence intervals, and understand causality is a real and scarce differentiator.

What to learn in this stage:

• Descriptive and inferential statistics

• Probability distributions

• Hypothesis testing (A/B testing)

• Correlation and linear regression

• Basic Bayesian statistics

Milestone of this stage: Designing and analyzing a complete A/B test, from the hypothesis to the conclusion with calculated statistical significance.

Estimated time: 6 to 8 weeks.

The big myth that stops thousands of people: "You have to learn Python first"

This myth is probably the one that does the most damage to those who want to enter the world of data. It comes from two places: the machine learning courses that proliferated between 2018 and 2022, and the LinkedIn profiles of data scientists who generalize their experience as if it were the only valid path.

The reality is that data science and data analysis are distinct careers, with distinct profiles, distinct tools, and distinct learning paths. A data scientist needs Python from the start because their work revolves around predictive models. A data analyst first needs to understand the data, the business metrics, and how to communicate insights. Python comes later.

Starting with Python when you don't yet know SQL has a real cost: you learn syntax without context, you don't have data to practice with in a meaningful way, and the learning curve becomes much steeper than it should be. In contrast, when you reach Python after mastering SQL and Excel, you immediately understand what each thing is for and you can practice with real problems you already know how to solve.

The most recognized open source learning ecosystem in data also structures its curricula prioritizing data fundamentals before advanced programming tools, which reinforces this perspective from the technical community itself.

Total estimated time and a realistic plan

If you dedicate between 1 and 2 hours daily consistently, this is the realistic projection:

• Stage 1 (SQL): 4-8 weeks

• Stage 2 (Excel/Sheets): 3-5 weeks

• Stage 3 (BI Tool): 4-6 weeks

• Total to be employable: 3 to 5 months

• Stage 4 (Python): 6-10 additional weeks

• Stage 5 (Statistics): 6-8 additional weeks

• Total for a complete profile: 7 to 10 months

These times assume constant practice with real projects, not just watching videos or reading theory. The portfolio is as important as the knowledge: companies hire those who can demonstrate they know how to do the job, not just those who say they know how.

What to put in your portfolio at each stage

The portfolio is the most underestimated element of the data analyst learning path. These are concrete projects you can build at each stage:

• SQL stage: Sales analysis using public datasets from Kaggle or open government data (Argentina has datos.gob.ar, Mexico has datos.gob.mx)

• Excel stage: Dashboard tracking the metrics of a fictional or real business (you can use data from your current job if you have it)

• BI stage: Dashboard published on Power BI Service or Tableau Public with an analysis of a topic that interests you: sports, economics, public health

• Python stage: Exploratory data analysis (EDA) documented in a Jupyter Notebook published on GitHub

• Stats stage: A/B test analysis or regression model with clear business interpretation

Ready to take the first step with structure and support?

Learning alone is possible, but learning with a clear path, guided projects, and a community that supports you means you get there much faster and with fewer detours. At Coderhouse you'll find SQL, Excel, Power BI, and Python courses designed specifically for the Latin American market, with mentors active in the industry and a community of thousands of graduates who already work in data roles. If you want to become a data analyst in 2026, this is the best time to start with the correct order.

If you're interested in exploring this topic further, you can also read Power BI vs Tableau: which to choose based on your profile and goals.

Recommended Coderhouse courses

If you want to go deeper into data analysis and applied artificial intelligence, Coderhouse has training for every level:

• Data Analytics Course: to learn to analyze data, build dashboards, and make decisions based on real information.

• AI Engineering Course: to incorporate AI and machine learning models into your data projects.

• Introduction to Artificial Intelligence Course: to understand the AI ecosystem and complement your data profile with knowledge about language models.

Frequently asked questions about the data analyst learning path

How long does it take to become a data analyst from scratch?

With a dedication of 1 to 2 hours daily, you can be eligible for junior data analyst positions in 3 to 5 months. This time covers the first three stages of the path: SQL, Excel, and a BI tool like Power BI or Tableau. For a more complete profile that includes Python and statistics, the total time is 7 to 10 months.

Do you need to know Python to get a job as a data analyst?

Not for most positions. According to the analysis of job postings in LATAM, Python appears as a requirement in just 38% of analyst positions, and in many cases these are mid or senior positions. The three most in-demand skills are SQL, Excel, and BI tools. Python is a valuable differentiator, but not an entry requirement into the market.

Which BI tool is best to learn: Power BI, Tableau, or Looker?

For the Latin American market, Power BI is the most recommended option due to its high demand in medium and large companies in the region, its integration with the Microsoft ecosystem (very widespread in LATAM), and its relatively accessible learning curve. Tableau is an excellent second option if you're aiming for startups or multinationals. Looker has a greater presence in large technology companies.

Do I need a university degree to work as a data analyst?

Not necessarily. The data market in LATAM increasingly values demonstrable skills over formal degrees. A solid portfolio with real projects, recognized certifications, and practical experience can be enough to access junior positions. That said, having training in areas like business administration, economics, engineering, or mathematics can be a positive differentiator.

What is the difference between a data analyst and a data scientist?

A data analyst focuses on analyzing existing data to answer business questions, create reports and dashboards, and identify trends. A data scientist works with predictive models, machine learning, and advanced statistics to build systems that anticipate future behaviors. They are complementary but distinct roles, with different profiles and learning paths. Most companies need more analysts than scientists.

Is SQL still relevant in 2026 with so much of an AI boom?

Yes, more than ever. Artificial intelligence generates more data and more need for people who can analyze it. In addition, many AI tools for data —like LLMs applied to analysis— rely on SQL or generate it automatically, which makes understanding SQL fundamental to validate and use those tools effectively. The Stack Overflow Survey 2024 confirms that SQL remains the most used technical skill among data professionals worldwide.