FULL Roadmap to Becoming a Data Analyst in 2026

Tools, AI, and job hunt strategies from a recently unemployed professional

The data job market is changing.

It’s become more competitive, easier to get lost, and harder to stand out. But even though the market has become more saturated in recent years, there also more data jobs than ever before. That is good news.

It is still 100% possible to go from 0 to data analyst, even now. I’m seeing people at every level and age break in and often work with such people closely.

So today, I’m want to give you a simple outline of how you can do it too.

We’ll talk through:

1. The skills you need - including where AI fits in

2. How to put together a portfolio and what to include

3. How to approach the application process and stand out

My goal here is to give you simple, but effective steps to landing your next data job.

While I’ve already been a data analyst for a few years now, I was laid off in 2025 and had to implement much of my own advice. But I also learned some new things along the way that either had to figure out or learned from speaking with my peers.

Let’s get into it.

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Skills

Obvious one, right? You need technical skills to land a job in data. I’m not sharing anything new or revolutionary here, but I do want to cover the basics along with my thoughts on where AI fits in.

1 - Excel

A foundational tool. Some roles require it extensively while others hardly use it at all. Either way, you WILL need it at some point. It’s integrated into every stack at some point and in some way, shape, or form.

Excel is also just a great place to start in general. It’s the swiss army knife of data, and while it’s not a very sharp knife, it gets the job done, and so do the rest of it’s parts.

There are a couple of layers to Excel:

Basic: Be sure to understand basic formula logic, pivot tables, and visuals.

Intermediate: If you want to take it further, learn Power Query, Power Pivot, and DAX in Excel. These are all elements of Power BI (more on that later), but they’re a bit less intuitive in Excel if I’m being honest. Macro building fit into this category as well.

Advanced: VBA (visual basic applications); to be honest, I don’t even know this one. While I do know how to create macros, I’ve never taken the time to learn VBAs.

2 - SQL

SQL stands for structured query language and it is the language of databases. It’s how you communicate with databases and pull information from them. This is another highly foundational skill. Probably the most foundational for the majority of data analyst roles.

It’s not a difficult language to learn, but it takes time to become very comfortable with it. Most beginner-to-advanced courses will teach you everything you need to know. Beyond courses, you’ll want to practice practice practice, and there are plenty of platforms for this as well.

3 - BI Tool

BI stands for Business Intelligence. These tools are known primarily for creating dashboards, but they extend far beyond that. They’re tools that help you pull data, manipulate it, model it, and re-shape it. And then you have report building and self-serve analytics which is what the end users see.

Companies build their entire data stacks around these platforms so they are often very pivotal to business operations. I’d recommend learning either Tableau or Power BI. Doesn’t really matter which one, but I am partial to Power BI - but be aware that it is a pain in the butt if you’re not using a company email for it, however, there are workarounds.

Either way, learn a BI tool and learn to build pretty and functional dashboards. These also add massive “wow factor” to your portfolio.

4 - Python

A programming language. Honestly, I wouldn’t even touch this one until you’ve learned the first 3 I mentioned above. Most data analyst roles will not require it. You see it more so in data science, but even then, most data science roles are using like 95% SQL. Python tends to be a more advanced skill. You might even consider holding off on it until you’re already in the field. With that said, if you see plenty of jobs out there asking for it, then by all means go for it. Just don’t make it the first tool you try to learn.

5 - AI

Alright, here we go. AI. This one sounds intimidating. Everyone wonders HOW exactly you should be integrating AI into data analytics right now. But let me tell you this: for 90% of data analysts, you only need to leverage AI for helping you do the following:

1. Problem solving, data exploration, and writing code. That’s it. It sounds simple, but this alone will make you a WAY better data analyst.

2. Here’s what I recommend: Pick a tool like Chatgpt or Claude, and just get used to walking it through the data you’re using, problem solving logic with it, and using it to help you with syntax and code logic.

3. Treat it like a junior analyst that reports to you, but is also smarter than you. Be very comprehensive and specific with it. Practice things like walking through roadblocks with it and ask it for suggestions. In terms of AI, this is all you need right now to effectively incorporate AI into your basic workflow.

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Portfolio

A portfolio is how you stand out in today’s market. It’s a way to demonstrate the skills that you have. Having a portfolio is helpful at quite literally every level, but it is probably most important for those just starting their data journey. This is because you likely don’t have working experience with the technical skills we talked about, (maybe Excel, and if you do, leverage the hell out of it). Portfolios help you bridge that gap and demonstrate actual ability.

As far as how to build one, just watch this video I made on how to build one in Notion.

Spend 30-60 minutes putting yours together and boom you’re done. Then you just need to insert the actual projects.

I recommend 1-3 projects that demonstrate multiple skills. Even one project will get the job done if you stack tools like Excel, SQL, and Power BI together. This is preferable to having lots of little projects. A portfolio with 1 or 2 strong projects beats a portfolio with 9 surface level projects every day of the week.

Then you’ll want to create a write-up for each project. I cover this in the video above as well, but basically you’re just creating a page within your portfolio website for each project and laying out:

1. Key questions you’re trying to answer

2. Your process and your data

3. Conclusions and insights

Do this for every project you build.

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Job Search

This is a fun one. I’ve been through a job search recently so my knowledge on this is very fresh and very relevant. Here’s what I recommend:

1. Resume - Create an ATS-friendly, easy to use resume. You can get the template for the exact resume I used to land my most recent job if you subscribe to the newsletter.

2. Narrow your search - “Data Analyst” is tough. It’s broad. Do your best to niche down as much as you can based on your previous roles or what you’re interested in. For example, target roles like People analyst, Business analyst, Power BI Developer, etc. I did this and saw WAY more responses on applications once I doubled down on my experience as a Power BI Developer. I framed my entire resume around that title since it’s what I’m most experienced in and also most interested in. You might even consider having more than one resume that are highly targeted in a few different areas of interest.

3. Network - Meet people. A great place for this is LinkedIn. Just start getting involved, reaching out to people, setting up coffee chats, and asking for advice. Give more than you get and be consistent. I’ve gotten game-changing advice as well as referrals over the years as a result of building my network.

4. Applications - Tailor as much as you can. Keep track of your progress with some sort of application tracker. It’s better to spend 10 minutes one 1 application versus blasting the same resume off to 100 postings. But I get it, some days you’re busy and just want to fire a few off. Do that. But when you have time, try to tailor it a bit, or at the very least, use a targeted, niche resume for a targeted, niche role, like I explained above.

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Those are the main beats! Hope that was all helpful. Know this - getting a job in data is difficult. It’s difficult for anyone at any stage. If it’s taking you some time, just know you’re not alone. Keep at it, keep working hard, and you’ll get there. Be relentless.

Last thing I’ll mention is that I recently soft-launched a new paid community called “Data Career Makers.” It has resources for everything thing I mentioned above, as well as courses I’ve build and weekly group calls for Q&A.

—> Check it out here

The launch price right now is $29/mo and will never change if you sign up now.

Thanks again for reading today my friends. I hope this was helpful!

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