How to become a Data Analyst in 2025: 5 key steps you need to take
Looking for a high-paying, in-demand career that doesn't require a computer science degree? Becoming a Data Analyst in 2025 could be the right path for you. Here are the steps to take to land your first Data Analyst job.
5 Steps to becoming a Data Analyst
Data Analyst jobs continue to be high demand in 2025. The global Data Analytics market is projected to grow from $74 billion in 2024 to $482 billion by 2033. That is an average growth of 23% per year
No, the Data Analyst career isn't getting replaced by AI. Yes, the best Data Analysts know how to use AI to augment their work.
The median salary for a Data Analyst in the US is $111,000. This means that more than half of Data Analysts make over 6-figures a year. That's a comfortable lifestyle, which unlocks stability, savings, and career growth opportunities.
You know what else is exciting about the Data Analyst career? Job satisfaction is high at 3.8 out of 5, based on Payscale data. This means, on average, Data Analysts are highly satisfied with their jobs.
So how do you become a Data Analyst in 2025? Here are the five key steps that you need to take:
Learn the essential tools and skills
Start building your network
Build a portfolio
Understand how Data is used in the real-world
Apply for entry-level Data Analyst positions
Before we dive into this roadmap, there's one thing I want to say upfront: You don't need a Data Analytics degree or bootcamp to become a Data Analyst. It certainly helps your chances if you do... but it's not a requirement.
Step 1: Learn the essential skills
I analyzed 100 current Data Analyst job postings. These were the top in-demand skills in 2025.
Top Data Analyst Skills in 2025
Use this chart as a road map to determine which skills to learn first. The more of these top skills you can master, the better your chances are of landing a data analyst job.
Given the data above, I recommend learning SQL first. Since 63% of Data Analyst jobs require SQL, you're opening doors to potential roles across industries, like finance, healthcare, e-commerce, and tech. Many companies list SQL as a must-have skill, so mastering it will boost your chances of getting hired. In fact, here is the recommended order in which to learn technical tools:
SQL
A data visualization tool (e.g. Tableau)
Excel
By the way, focus on one skill and become really good at that one skill. After you've mastered that, then move on to the next skill or tool. There's no point learning many tools at once, and being a beginner in all of them.
There are many reasons for focusing on one skill at a time:
Fewer distractions = more focus = faster learning speed
Skills are transferrable between tools. If you know SQL, it's easy to learn Excel. If you know Excel, it's easy to learn a data visualization tool like Tableau.
Being an expert will help you stand out from other candidates during your interview. Hiring managers would rather hire someone who is a master at (say) Excel than someone who is a beginner at multiple tools.
One more thing, the best way to learn is by doing. These technical tools (SQL, data viz and Excel) are hands-on, practical tools.
For SQL, we recommend using InterviewMaster.AI for practice. We have 200+ practice questions and continuing to add more each month!
Step 2: Start building your network
A strong professional network isn't built overnight. It is built brick by brick (or genuine connection by genuine connection). If you are serious about becoming a Data Analyst, start building your network as soon as possible.
This is why I have this as step 2. Because if you only start building your network when you're ready to apply to jobs... you are already too late.
Blehhhhh, networking is gross. Right? No, it doesn't have to be. When done correctly, networking benefits both parties.
So how do you go about building your professional network:
Start with your existing connections – Do you have friends, family, or colleagues in the data industry? Or know someone who can introduce you to a Data Analyst? Start having coffee chats with those folks right away.
Ask everyone you have a chat with for recommendations. At the end of every networking meeting, ask “Are there 1-3 people in data you think I should connect with? Would you mind making an introduction?”
Stay in touch – Follow up regularly to keep relationships warm. Even a quick message or check-in can help you stay relevant in people's minds.
Remember, every genuine relationship has both give and take. To build a genuine professional relationship, you need to offer value first. Give what you can. It doesn't have to be in the Data Analytics space. For example, you might
Watch someone's kids for an evening so they can attend a networking event
Send them articles or job leads that might interest them
Offer up advice from areas that you have expertise in. For example, if you know marketing, you might advice them on how to optimize their latest ad campaigb.
If you've never had a professional coffee chat before, don’t overthink it. Think about it as a casual conversation where you learn from someone’s experience. However, you should come prepared. Make sure you prepare thoughtful questions about their career path and advice for breaking into the field. Focus on listening, showing genuine curiosity. Do not ask for a job on the first meeting (see above about offering value first).
Step 3: Build a portfolio
It's not enough to have the skills, you need to SHOW hiring managers that you can do the work. The best way to do this is by a having a portfolio of projects.
Include a link to your portfolio on your resume and be ready to discuss your projects in interviews. Since you're early in your career, you may not have much professional experience yet. Your portfolio is proof that you can do the work of a Data Analyst.
What types of projects should you include in a Data Analyst portfolio? Well, this goes back to chart showing the top skills for Data Analysts.
Have SQL projects extracting insights (paired with recommendations) from large datasets
Have a data visualization project demonstrating your ability to tell stories with data
Have an Excel project, with advanced techniques like VBA and power query.
Most common mistakes when it comes to building a portfolio:
❌ Dumping all your projects in Github
Github is a great place for sharing code, it's not a good place to showcase your projects. Instead, I recommend putting your projects into a Notion document. Notion is similar to Microsoft Word or Google Docs, but with better formatting options.
❌ Not having clear takeaways and recommendations
The mark of a good Data Analyst? The always translate data insights to business impact. Every one of your portfolio projects should answer: Why does this analysis matter? What should the business do with this information?
❌ Having too many projects, that lack depth
Quality over quantity, please. Instead of have 15 projects that skim the surface, have 3 solid end-to-end projects.
One last important tip... Try to do projects relevant to your target industries. For example, if you're aiming to be a Healthcare Data Analyst, do projects related to public health or medicine trial outcomes. If you're interested in e-commerce, analyze customer behavior or sales trends.
To find datasets for your portfolio, check out Kaggle.com. They have the most extensive repository of datasets (that I know of).
Step 4: Learn how Data is used in the real-world
Many aspiring Data Analysts focus only on technical skills; but they struggle to apply them to business problems.
In your interviews, you can stand out by having a clear understanding of how to use data in the real-world.
Practice on InterviewMaster.AI. No generic SQL exercises here. Interview Master provides SQL questions about real companies and actual products. You’ll get hands-on experience solving problems, like those faced by Data Analysts in the industry.
Read case studies & books. Study real-world examples of how companies use data to drive decisions. Many tech companies have public blogs that share case studies on the problems they solve. Check out Netflix, Airbnb, Pinterest and Uber's blogs to start.
It takes time to build the intuition around business & data. Being consistent here is more important than trying to cram all that knowledge in one week.
Step 5: Apply for entry-level Data Analyst positions
You're almost there!! You've spent the past months or years learning SQL, creating a portfolio and building your network. Now you're ready to apply for jobs and land your dream job.
Applying for jobs can feel overwhelming. So we've broken it down to this checklist to guide you through applying for your first Data Analyst job.
Job application checklist
🔲 Have a professional resume tailored to Data Analyst jobs
Your resume is important. It's the first impression a recruiter, hiring manager or interviewer have of you.
Your resume should be easy to read, and highlights your unique value as a Data Analyst.
What skills should your resume showcase?
Must-have Data Analytics skills: SQL, Excel and a data visualization tool, like Tableau.
Business sense: Data cleaning, exploratory data analysis (EDA), metric reporting
Soft skills:
Communication: Ability to translate data insights into business recommendations
Collaboration: Ability to work well with team members and stakeholders
If you're unsure where to start, use this template as a starting point.
🔲 Leverage referrals to increase your chances
Referrals significantly increase your chances of landing interviews and job offers. They are like a "stamp of approval" from a current employee.
In case you are wondering... yes, this is legal and most companies have some kind of referral process!
Use your network to get referrals for jobs. Remember step 2, where you started to build out your network? Now it the time to tap into your network.
Some tips on how to best leverage your network for referrals:
Don't be afraid to ask. People love helping others. It is part of our human nature. But you need to be proactive about asking for help. Ask and you shall receive -- if you will.
Be specific in your ask. When asking for a referral, mention a specific job ID or link that you're interested in. Do not make a generic ask, like "If you know something that is a good fit for me, please refer me".
Offer something in return. Networking is a two-way street. Always consider how you might provide value BEFORE asking for a referral. For example, you might
Share an interesting article related to their work
Offer a genuine compliment about their recent accomplishment
Express genuine appreciation and keep the relationship warm for the future
🔲 Have a polished, professional, public portfolio page
We talked about this in much detail in Step 3: Build a portfolio, so you already know how to create a good portfolio.
A few quick tips to help with your job search:
Share your portfolio in your resume and on your LinkedIn profile
Make sure your portfolio is accessible, so recruiters and hiring managers can see it
Keep your portfolio updated as you build more projects or improve on existing ones
Your portfolio will help you stand out from the candidates. Hiring managers love to see real-world data projects because they prove you can do the job.
🔲 Find jobs to apply to
There are (seemingly) infinite number of platforms on which to find and apply for jobs. You don't need to be on every single platform. We recommend sticking to 1-2 platforms based on your career goals:
For startup jobs, AngelList.
Most of these platforms have automated alerts. They will send you an email or notification when a job related to your search gets posted. This will save you lots of time, and ensures that you'll always know of the latest opportunities.
🔲 Practice for interviews
Do not wait until you land your first interview to start practicing. It's too late. Start practicing the moment you send out your first job application.
Why? Because interviewing is a skill you can develop, and every interview is a precious opportunity. You want to maximize your chances of turning each one into a job offer.
What does the interview process for Data Analysts look like? This is an in-depth question that we will write about in a future article. But for a quick overview, here are the 3 types of interviews that you should expect:
Behavioral interviews: Tell me about yourself, Why this company?
Technical interviews: SQL problems, Excel tasks
When practicing for SQL interviews, we recommend using www.InterviewMaster.AI. We designed and built Interview Master to help you ace your SQL interviews.
Case study or take-home project: How to use data in a real-world business problem
The job search process can be a tough one, especially with the current market. In order to succeed, you must
Stay consistent — even if you face rejections, keep your head up and keep moving forward
Keep learning — every interview, especially the failed ones, is a learning opportunity
Final note
And there you have it! The 5 steps you need to take to become a Data Analyst.
One last note: Everybody's journey is different. It could take one person 3 months to land their first Data Analyst job, and another person 3 years. Go at a pace that is comfortable and sustainable for you.
We are rooting for you and we believe in you. 🩷