How to Become a Data Analyst: A 7-Step Roadmap

To become a data analyst, learn four core skills in this order: spreadsheets (Excel or Google Sheets), SQL, one dashboard tool such as Power BI or Tableau, and basic statistics. Then prove those skills with two or three portfolio projects that answer real business questions, and apply for entry-level analyst roles with a résumé that shows results, not just tools. You don't need a computer science degree or advanced math. For a beginner studying part-time, a realistic plan is about 6 to 9 months of learning, plus the job search.

This guide breaks the path into seven steps, shows which tools to learn first (and which can wait), gives you five portfolio project ideas and explains when a certification is worth your time.

What does a data analyst actually do?

A data analyst turns raw data into answers a business can act on. A typical week might include pulling last month's orders with SQL, cleaning a messy export from a sales system, updating a dashboard and explaining to a manager why returns jumped in one region. The questions are practical: Which products are growing? Where are we losing customers? Did the spring promotion actually work?

The role is often confused with two neighbors. Here's how they differ:

Analyst work is a natural entry point into the data field because the core tools can be learned in months, not years. For a closer look at an analyst's day and the four types of analytics, read the first lesson of our free course, What Is Data Analytics and What Does a Data Analyst Do?

The skills you need

Job postings can look intimidating, with long lists of tools. In practice, entry-level analysts are hired for a short list of skills done well:

The skills nobody lists, but everybody notices

Curiosity (asking "why?" one more time), business sense (knowing which number matters to the person in front of you) and healthy skepticism toward your own results. Good analysts check whether totals add up before they share a chart. That habit alone prevents the most embarrassing mistakes in the job.

Which tools to learn first

Pick one tool per category and go deep. The concepts transfer: if you know one SQL dialect or one BI tool well, switching to another takes weeks, not months.

Not sure which BI tool to choose? Search job postings in your city or target industry and count which one appears more often. That's your answer.

The 7-step roadmap to become a data analyst

Step 1: Learn how analysis works

Before any tool, learn the process: ask a clear question, prepare the data, process (clean) it, analyze it, share the findings and help the business act on them. Beginners often start with the data and look for something interesting. Analysts start with a decision someone needs to make. "Show me the sales data" is a request; "Should we keep running free shipping in the Midwest?" is a question you can answer.

Step 2: Get strong in spreadsheets

Spreadsheets teach you analytical thinking with instant feedback: you change a filter and see the result. Aim to be comfortable with conditional formulas, lookups, pivot tables, charts and cleaning tasks like removing duplicates and fixing inconsistent text. Our guide to Excel formulas for data analysis covers the 18 formulas you'll use most, each with a worked example.

Step 3: Learn SQL

Much of a company's data lives in databases, and SQL is how you get it out. Start with SELECT, WHERE and ORDER BY, then move to GROUP BY, HAVING and JOIN. Practice daily, even for 20 minutes: SQL is a skill you build by writing queries, not by watching videos. You can practice for free with SQLite on your own computer, as we do in Introduction to SQL: SELECT, WHERE and ORDER BY.

Step 4: Add statistics and data cleaning

Work on these alongside steps 2 and 3. You need descriptive statistics (mean, median, standard deviation, percentiles), a feel for outliers and skewed data, and the basics of correlation and A/B tests. Just as important is cleaning: real data has duplicates, blanks, typos and dates stored as text. Keep a cleaning log of every change you make; interviewers love asking how you handled messy data.

Step 5: Learn one BI tool and the rules of good charts

Build at least one dashboard in Power BI, Tableau or Looker Studio. Learn to choose the right chart (bars for comparisons, lines for trends), cut the clutter and write chart titles that state the takeaway, like "West drove half of Q1 revenue" instead of "Revenue by region."

Step 6: Build two or three portfolio projects

This is the step that gets you hired, and the one most people postpone. A portfolio project shows the whole workflow: a business question, the data, the cleaning, the analysis, a dashboard and a recommendation. Our portfolio project lesson walks you through a complete year-end sales review with a README template and a grading rubric. Ideas for more projects are in the next section.

Step 7: Package your work and apply

Publish your projects (GitHub, a dashboard link or a simple portfolio page), write a one-page résumé with bullet points that show results, and update your LinkedIn profile. Search beyond the exact title "data analyst": business intelligence (BI) analyst, reporting analyst, operations analyst and marketing analyst roles use the same skills. Expect a technical screen (often SQL), a case or take-home exercise and behavioral questions. We cover résumé bullets, interview stages and a 90-day job search plan in How to Get Your First Data Analyst Job.

Portfolio projects that get you interviews

A good project answers a question a business would pay to have answered. Use real public data or a realistic dataset, and make every project end with a recommendation. Here are five ideas that show different skills:

Before you publish a project, run it through this checklist:

• The README opens with the business question and a three-sentence summary of the answer.
• It names the data source and includes a short data dictionary (what each column means).
• A cleaning log lists every change you made to the raw data and why.
• Three to five findings, each with one clear chart and a takeaway title.
• A recommendation section says what the business should do next, and what you're unsure about.
• Links work: dashboard, spreadsheet or notebook, and the raw files.

Do you need a certification?

No, but one can help, especially if you have no degree or are changing careers. A certificate adds structure to your learning and keywords to your résumé. It doesn't replace a portfolio: hiring managers want to see what you can do with data. These are three well-known options:

Prices, exam formats and renewal rules change, so check each provider's official page before you commit. And if budget is tight, start free: our course ends with a certificate of completion you can add to LinkedIn next to your portfolio project.

How long does it take? A realistic timeline

It depends on where you start and how many hours you can study each week. These are planning estimates, not guarantees:

Here's how the roadmap fits into months for a beginner studying part-time:

1. Months 1–2The process and spreadsheetsFormulas, lookups, pivot tables and charts on one practice dataset.
2. Months 2–4SQL, plus statistics and cleaningDaily query practice; descriptive statistics and a cleaning log on the side.
3. Month 5One BI toolBuild your first dashboard and learn the rules of good charts.
4. Months 5–7Portfolio projectsTwo or three end-to-end projects, published with READMEs.
5. Month 7 onwardRésumé, LinkedIn and applicationsStart applying while you finish your last project. Budget extra time for the search itself: applying, networking and interviewing is a project of its own.

Common mistakes to avoid

Frequently asked questions

Can I become a data analyst without a degree?
Yes, it's possible. Some employers require a bachelor's degree and others accept equivalent skills or experience. Without a degree, your portfolio, a recognized certificate and analysis work at your current job carry even more weight, so invest in them early.

Is data analytics hard to learn?
The core tools are learnable without a technical background, and the math is mostly high school level plus a few statistics concepts. For many beginners the harder part is not the syntax but learning to ask sharp questions and explain findings clearly to non-technical people.

Do I need to know Python to become a data analyst?
Not to get started. Many entry-level postings list Python as "preferred" rather than required, while SQL and spreadsheets are expected. Learn SQL and one BI tool first, then add Python with pandas once you've finished your first project. When you get there, our free Python course starts from zero, and our free Power BI course covers the BI side.

How much do data analysts earn?
It varies a lot by city, industry and experience. The U.S. Bureau of Labor Statistics doesn't track "data analyst" as a single occupation; the first lesson of our free course summarizes the BLS pay and growth figures for the closest related occupations, such as data scientists and operations research analysts.

Should I learn Excel or SQL first?
Excel (or Google Sheets) first. You'll see the data change in front of you, which makes concepts like filtering, grouping and summarizing click. SQL then feels familiar: a GROUP BY query is a pivot table written in words.

Spreadsheets are where nearly every analyst starts, so start there too. Our guide to the 18 Excel formulas for data analysis shows each formula on the same small sales table, with the exact result.

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Ready to start? Our free data analytics course takes you from your first formula to SQL, Power BI, Python and a portfolio project in 16 lessons, using one realistic sales dataset, with exercises and a certificate of completion.