How to Become a Data Analyst (What to Do & Where to Start)

To become a data analyst, start by learning the core data skills. Next, choose your learning path (college degrees, online courses, or self-taught), build a portfolio, and then start getting real-world experience. From there, start applying for entry-level data positions. 

If you’re curious, detail-oriented, and love making sense of information, data analytics could be one of the smartest career moves you can make right now. 

It’s creative, analytical, and surprisingly people-focused because the job also involves translating data into insights that help teams make better decisions. 

I’ll be honest, when I first started learning about this field, it was quite intimidating. 

Everyone seemed to be fluent in Python and SQL, while I was just figuring out what a database even was. However, most data analysts didn’t start as experts. They learned step by step and often from completely different backgrounds.

In this guide, we’ll cover all you need to know about how to become a data analyst, including: 

• What a data analyst actually does.
• What skills you need (and how to learn them).
• Education and certifications worth getting.
• How to build a portfolio that gets you hired.
• Salary expectations and job outlook.

If you want to skip ahead and focus on landing interviews for data roles, you can create a professional resume using Rezi AI Resume Builder. 

What Does a Data Analyst Do?

At its core, data analysts turn information into understanding.

Data analysts collect, organize, and interpret data to help businesses with decision-making. You might work with spreadsheets, databases, dashboards, or even AI tools, but the main goal stays the same: to find patterns, tell stories through data, and recommend actions based on what you discover.

Here’s what a typical day in the life of a data analyst could look like:

• Pulling data from databases using SQL
• Cleaning and organizing information in Excel or Python
• Building dashboards in Tableau or Power BI
• Presenting insights to help teams improve performance

Also, expect to work closely with other departments like marketing, finance, or operations. You’ll share input on what the data says and why it matters.

How to Become a Data Analyst

Here’s how to become a data analyst:

• Learn the core data skills (Excel, SQL, Tableau, Power BI, Python or R, statistics, and critical thinking). 
• Choose your learning path (college degrees, online courses, or self-study). 
• Build a portfolio, even if you don’t have formal work experience. 
• Start getting real-world experience where you can apply your data skills.
• Apply for entry-level data job positions.

As you gain experience and expand your technical skills, you can move into more advanced roles like data scientist and machine learning engineer.

And some of the skills you develop along the way, such as data science and data visualization, are considered high-income skills. These can significantly increase your earning potential and open doors across multiple industries.

We’ll dive into each step in more detail below.

1. Learn the core data skills

Although being a math prodigy isn’t necessary, you will need a strong foundation in a few tools.

Start with the essentials:

• Excel or Google Sheets: Learn formulas, pivot tables, and charts.
• SQL: This is your bread and butter for extracting data from databases.
• Python or R: Great for automating tasks and analyzing large datasets.
• Tableau or Power BI: For turning your findings into interactive visuals.
• Statistics & Critical Thinking: Understand averages, correlations, and how to spot trends.

Don’t try to learn everything at once. Start with Excel and SQL, and then continue from there.

That said, SQL is one of the most important data skills to know. If you’re curious about how long it takes to learn this, here’s what one Reddit user shared:

If you’re looking for structured learning, programs like the Google Data Analytics Certificate or IBM Data Analyst Professional Certificate can be a good place to start.

Relevant guides:

• AI Skills to Put On a Resume
• Examples of Resume Computer Skills
• Technical Skills to Include on a Resume
• Programming Skills for Your Resume
• Best Transferable Skills to Put On a Resume

2. Choose your learning path

There’s no single “right” way to become a data analyst. Some people go through a university degree, others through bootcamps or self-study.

Here are your main options:

• College degrees (optional but helpful). Common majors include data science, statistics, economics, computer science, or business analytics. These are great to include in your resume education section when applying for data roles. This path is great for those who want a strong academic foundation or plan to move into advanced analytics later. 
• Bootcamp or online certifications. Programs like CareerFoundry, DataCamp, or Coursera’s Google Certificate can help you get job-ready. This path works for career changers or self-learners who want practical hands-on projects. 
• Complete self-study route. You can learn for free through platforms like YouTube and GitHub. You can also do lots of reading online, as there are many articles and guides out there available for free. This route may be for you when you’re an independent learner on a tight budget. 

Overall, however, employers tend to care more about your skills and portfolio than where you learned them. 

Side Note: You might also find this data analyst roadmap helpful. 

3. Build a portfolio (even without formal work experience)

Your portfolio serves as proof that you can do the work required. 

You don’t need to have worked in a company to start building your portfolio. Just start by showing what you can do with real or public data. Give employers a sample of what you’re capable of.

Here are a few beginner-friendly project ideas:

• Analyze a dataset from Kaggle (like Netflix movies or Spotify tracks).
• Create a Tableau dashboard.
• Build a small business report using Excel and Power BI.

Host your projects on GitHub or Tableau Public so that hiring managers can see your process. And don’t just post charts, explain the story behind your findings. 

When you’ve built a portfolio, add them to your LinkedIn profile and the header section of your resume.

4. Start getting real-world experience

Once you’ve built a few projects, look for ways to apply those skills. Here are a few ideas:

• Volunteer to help a nonprofit with data reporting.
• Take part in online hackathons or Kaggle competitions.
• Offer to analyze data for a friend’s small business or startup.
• Start freelancing and offer to help organizations manage their data. 

These experiences will help you write a strong resume and give you stories to talk about in interviews. 

Further resources:

• How to List Projects on a Resume
• How to Write a Professional Resume
• How to Write a Strong Resume With No Experience‍
• How to Get a New Job Quickly‍
• How to Write a Career Change Resume‍
• Freelance Work Experience on a Resume

5. Apply for entry-level data positions

When you’re ready, start looking for roles like:

• Junior Data Analyst
• Business Analyst
• Reporting Analyst
• Operations Analyst

Tailor your resume to show measurable results, for example: “Cleaned and analyzed 1,000+ sales records using SQL and Excel, identifying patterns that improved reporting accuracy by 15%.”

For inspiration, I’d suggest looking at proven data analyst resume examples. 

And if you’re looking for career tools to help you break into a specific field or industry, check out these guides:

• Rezi for Jobs in Tech
• Rezi for Engineering Job Applications
• Rezi for Marketing Job Applications
• Rezi for Project Management Job Applications
• Rezi for a Career Change

How Long Does It Take to Become a Data Analyst?

If you’re learning part-time and starting from scratch, expect around 6–12 months to build job-ready skills. 

Here’s an example timeline:

• Month 1–3: Learn Excel, SQL, and basic stats.
• Month 4–6: Add Python and visualization tools.
• Month 7–12: Create portfolio projects, start networking, and apply for jobs.

If you’re studying full-time (through a bootcamp or degree), you can move even faster.

Data Analyst Salary & Career Outlook

The estimated average pay for a data analyst is $92,586 per year in the United States, according to Glassdoor. However, data analyst salaries vary depending on your experience, industry, and location. For example, experienced data analysts in tech, finance, and healthcare tend to have high earning potential. 

Summary

Let’s recap on how to become a data analyst:

• Learn the core tools: Excel, SQL, Python (optional), and a visualization platform.
• Choose your learning route — degree, bootcamp, or self-taught.
• Build 3–5 strong projects that show real-world problem-solving.
• Get hands-on experience through volunteering, freelance work, or competitions.
• Apply for entry-level roles and keep learning on the job.

Every graph, query, and dataset teaches you something new. And so, if you’re the kind of person who loves finding patterns and helping people make smarter choices, this field has endless potential for you.

FAQs

Do you need a degree to become a data analyst?

Not necessarily. Many data analyst roles list a bachelor’s degree (in data science, statistics, computer science, business analytics, etc.) as preferred. But plenty of people enter via online courses, bootcamps, or self-study plus a strong portfolio. The key is showing you can do the work, not necessarily the specific credential. However, it’s still nice to add a relevant qualification to your resume education section.

What are the most important technical skills I should learn first to become a data analyst?

Common essentials include:

• Excel or Google Sheets (for data cleaning and quick analysis).
• SQL (for querying databases).
• A visualization tool like Tableau or Power BI.
• (Optional but very helpful) A programming language like Python or R.
• Basic statistics and data-thinking.

Can I become a data analyst if I have no prior data or analytics experience?

Absolutely. Many people transition from unrelated fields by learning the core skills, building projects to show what they can do, and applying for entry-level roles. The trick is to demonstrate that you’ve done relevant work (even if it was self-directed).

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

A data analyst focuses on analyzing historical or current data to extract insights and inform business decisions. On the other hand, a data scientist often works on more advanced modelling, predictive analytics or building machine-learning systems.

Which industries hire data analysts and which ones should I aim for?

Many industries hire data analysts: technology, finance, healthcare, marketing, retail, manufacturing, and more. Because data analysis is broadly useful, you don’t have to limit yourself to one field. Picking one sector that interests you can help you tailor your projects and learning.

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