Kedeisha Bryan

I help career changers become data analysts without going back to school

[Kedeisha Bryan @kedeishaindata]

Most people skip level one, then wonder why nobody hires them.

There are exactly 5 levels between where you are now and a data analyst job, and you don't need a CS degree or another master's for any of them.

Level 5 is the one nobody expects, and it's where your old career stops being a weakness

This is exactly what my playbook walks you through in order, so comment DATA and I'll send you the free 0 to $100K roadmap.

P.S. If you're a teacher, nurse, or anyone who's been told your background doesn't count, watch all the way to the end.

[Kedeisha Bryan @kedeishaindata]

The people with the most money to pay for data skills aren't posting jobs on LinkedIn.

Think about how almost everyone makes this career change:

- Learn spreadsheets
- Learn databases
- Build a few dashboards
- Pick up some programming
- Search "data analyst" on LinkedIn

Then they wonder why they're competing with thousands of people who did the exact same thing.

Meanwhile there's a whole market sitting right next to that one, and almost nobody is looking at it

This is exactly what my playbook walks you through, so comment DATA and I'll send you the free 0 to $100K roadmap.

P.S. If you've sent 100+ applications and heard nothing back, it might not be your skills. You might just be standing in the most crowded line.

[Kedeisha Bryan @kedeishaindata]

"I already know Power BI. Do I need to learn Fabric too?"

Short answer, you're asking the wrong question, and it's costing people interviews.

Career changers, what tool are you most confused about right now? 👇

Comment DATA for my free 0 to $100K data analyst playbook.

[Kedeisha Bryan @kedeishaindata]

Two data analysts with the same skills BUT... $22,000 apart in salary.

Interview Stack looked at 3,000+ job postings and found roles asking for AI skills posted a median of $102K, while the ones that didn't sat at $80K.

But the takeaway isn't "go learn ChatGPT," because that's what everyone's already doing, and it isn't what's being paid for...

Career changers, are you using AI or building with it? Be honest in the comments!

P.S. If you're worried AI is going to make you obsolete before you even break in, that fear is aimed at the wrong thing. Comment DATA and I'll send you my free 0 to $100K data analyst playbook

[Kedeisha Bryan @kedeishaindata]

I'm on a mission to work with 10 people who finished the Google Data Analytics Certificate but still haven't landed a single interview.

I'll help them land their first $75K–$100K data analyst role through my Data Career Launch Program.

I'm taking all 10 this month, and applications close at the end of the month.

DM me "DATA" if you want to learn more

[Kedeisha Bryan @kedeishaindata]

Most analysts suck at data storytelling.

Not just career changers.

People who already have the title.

And it makes sense when you look at where everyone puts their time.

Everybody is grinding SQL and Python, because that's what gets tested and that's what gets talked about.

So that's where all the competition is.

Nobody is competing on storytelling.

Most people build the dashboard, get the numbers right, and call it done.

They never ask whether the thing actually says anything.

The color is whatever the tool gave them.

The white space is an accident.

So when your projects are A1 on storytelling, you stand out immediately.

Not because you're doing something rare.

Because the person reviewing you has already looked at 40 dashboards this week that all look the same.

Everyone is fighting over the skill that's tested.

Almost nobody is building the one that gets remembered.

Comment "STORY" and I'll send you what strong storytelling looks like in a portfolio.

[Kedeisha Bryan @kedeishaindata]

Your degree isn't the proof anymore.

We're in the proof economy now.

Six years ago, ten, twenty, thirty years ago, your degree carried heavy weight.

It was the credential and it opened the door on its own.

That's just not how it works today.

I had a student with a bachelor's and a master's from Emory, my alma mater, one of the top schools in the country.

He was working security jobs.

He built a real portfolio with us.

Walked it into a data analyst interview. Landed a six figure role.

That portfolio did more heavy lifting than the six figure degree did.

Tangible, visual evidence of skill is what wins now. Nobody's asking to see your transcript.

Comment "PROOF" and I'll send you what belongs in that portfolio.

[Kedeisha Bryan @kedeishaindata]

You're sleeping on government data jobs.

Everybody's fighting over the same handful of tech company postings.

Meanwhile local government, state government, federal, and government contractors are always hiring.

I still get a LinkedIn DM every single week about government contracting analyst roles. Every week.

Then there's consulting.

Boutique firms and the big ones too.

Most people are terrible consultants because they're not great at asking questions, so most people avoid it.

That's exactly why the door is open.

And then the industries nobody romanticizes.

Healthcare.
Health insurance.
Supply chain.
Transportation.
Logistics.

None of these are on anybody's vision board.

That's the point.

Less competition isn't lower quality, it's just less crowded, and the paychecks clear the same.

Comment "OPEN" and I'll send you the full list of where I'd be looking.

[Kedeisha Bryan @kedeishaindata]

The job description matters more than the title.

I say this to my students all the time.

If you want to be a nurse, the job says nurse. Registered nurse, travel nurse, whatever it is.

The title tells you the whole story.

Data is not like that.

There are so many data analyst jobs that don't say data analyst anywhere in the title.

You scroll right past it.

Then you actually read the description and it's like oh, this is a data analyst job.

So when people tell me they can't find jobs, that's usually not true.

They're searching narrow.

They typed three words into LinkedIn and took whatever came back.

Learn boolean search.

Use it on LinkedIn and on Google, because plenty of postings live on boards outside LinkedIn.

Set up your own AI job board so you're seeing roles within 24 to 48 hours of them going up.

Do that and I guarantee you find 10 to 15 more jobs a week you could have applied to.

Finding jobs is its own skill. It's separate from doing the job, and almost nobody trains for it.

Comment "SEARCH" and I'll send you the boolean strings I'd start with.

[Kedeisha Bryan @kedeishaindata]

The data analyst job went full-stack.

And it happened for a reason nobody explains to you.

Companies aren't overhiring anymore.

That means teams got smaller. And when the team gets smaller, the work doesn't disappear, it just lands on the analysts who are left.

That's why the role keeps stretching into engineering.

Somebody has to move the data before anybody can analyze it, and there's no longer a whole team standing between you and the source.

I'm not saying become a crazy data engineer who knows MongoDB and 20 different tools.

I'm saying you see a data source and you can reach for Python, maybe Power Query, maybe Databricks, and build a simple pipeline.

Extract it, transform it, move it where it needs to go.

The job didn't get harder. It got wider.

And the people still studying the narrow version are the ones getting passed over.

Comment "FULLSTACK" and I'll send you where I'd start.

[Kedeisha Bryan @kedeishaindata]

Stop applying to 500 jobs. Apply to 25.

I know that sounds backwards.

Stay with me.

Before, you could write one generic resume and use it for everything, and that was fine.

AI wasn't around.
Nobody was auto-applying to 100 jobs in 100 seconds.
There weren't AI-generated resumes flooding every single portal.

Now there are.

Which means volume is the one thing you can't win at anymore.

There is always somebody applying to more jobs than you, faster than you, with less effort than you.

So you win on targeting instead.

20 to 30 applications a week, each one tweaked for that specific posting.

One job leans Tableau, the other leans Power BI.

You adjust your BI keywords to match.

Small change, big difference.

That's it. You're not rewriting your whole resume.

You're not applying to be a data engineer and a software engineer.

The game today is quality volume. Not volume.

Comment "TARGETED" and I'll show you how I'd tweak one resume for two different jobs.

[Kedeisha Bryan @kedeishaindata]

The way you got into data during COVID is dead.

I'm not being harsh.

I'm telling you what I watched happen.

Back when I was pivoting, breaking into tech was hot.

You could do such minimal work and still land interviews.

Grab a Coursera certificate.
Copy a tutorial project on a weekend.
Put the skills on your resume and go apply.

That was actually encouraged back then.

And the wild part is it worked.

It worked because there was an overflow of jobs.

Companies were overhiring.

There were more roles than there were people to fill them, so the bar sat on the floor.

That overflow is over.

Companies are still hiring, but at a normal pace now, and the number of candidates chasing those roles went way up.

Nothing is wrong with you. You're running a 2021 playbook in a 2026 market.

Comment "DEAD" and I'll send you what replaced it.

[Kedeisha Bryan @kedeishaindata]

No online personal brand means no data job.

Period.

I'm not being dramatic. That's just where we are.

Nobody is finding you off a resume alone anymore.

If there's nothing to see when they look you up, you're already out.

And if you're coming from a non-traditional background, there's a secret sauce, a particular sauce, that has to be there.

It is really a skill to articulate work that did not have an analyst title into a data analyst job.

Especially a high paying one.

Warehouse.
Nursing.
Teaching.
Retail management.

That experience counts.

But only if you can translate it, and nobody teaches that part in a course.

Your resume isn't weak. Your translation is.

Comment "BRAND" and I'll send you how I'd position a non-traditional background.

[Kedeisha Bryan @kedeishaindata]

Your 3-month pivot into data is now 3 years old.

I know exactly how that happened.

Most people want to take the cheapest way possible.

So you stack free videos.
Then a cheap course.
Then another cheap course.

A year goes by. Then two.

And you're in the same place you started.

Still watching.
Still studying.
Still telling people you're getting into data.

Meanwhile the salary you never got still isn't showing up in your account. Three years of it now.

You're not lazy.

You picked the slowest possible route and called it saving money.

Comment "3 YEARS" if this is the year you stop doing it the long way.

[Kedeisha Bryan @kedeishaindata]

Career changers don't have an information problem.

Everything is free now. That was never the hard part.

You can Google anything.

You can ask ChatGPT anything.

The tutorials are sitting right there and most of you have already watched them twice.

The hard part is that landing a $100K data analyst job as a career changer has so many layers to it that personal touch is necessary.

How you position 10 years of work that never had an analyst title.

What you say when they ask why you're switching.

Which of your skills actually transfer and which ones you need to stop talking about.

Most colleges and online training schools don't provide any of that.

They give you generic stuff.

And generic information plus some AI chatbot is not going to get you where you want to go.

You don't need more information. You need somebody to look at your situation.

Comment "LAYERS" and I'll break down what those layers actually are.

[Kedeisha Bryan @kedeishaindata]

A top data bootcamp taught us HTML and CSS.

For a data role. For whatever reason.

This was back when I was pivoting in, and it was one of the most popular programs out there.

People were paying real money for it. Just nonsense stuff.

That's what happens when the people writing the curriculum aren't the ones doing the work.

They're reading off slides they built 5 years ago and nobody in the room knows enough yet to catch it.

So before you pay anybody, ask one question.

Are the instructors still on the ground level?

Still doing this stuff day to day?

Or are they teaching from memory?

Each one teach one. That's the whole thing.

Somebody who is in it right now, telling you what they're actually doing right now.

Comment "TEACH" if you want to know what to ask before you enroll anywhere.

[Kedeisha Bryan @kedeishaindata]

You're training for the 2022 version of a data analyst job.

That version is gone.

The day to day of a data analyst looks nothing like it did two years ago.

Not a little. Completely. There's something new implemented in this space every couple months.

But the people teaching analytics in schools and bootcamps are handing you perspectives from 5, 10 plus years ago.

Pre-GPT. Pre-AI.

Colleges are not keeping up. Most online training schools are not keeping up either.

So you finish the whole program, walk into the job, and you're behind on day one.

You did everything they told you to do.

The material has to be current from the top down.

The people teaching it too. If it's not, you paid to learn the old version of the job.

Comment "SHIFT" and I'll show you what the role actually looks like now.

[Kedeisha Bryan @kedeishaindata]

A master's in data science won't get you $100K.

Neither will a master's in business analytics. And I'll say what nobody else will.

Well over 95% of these programs are low quality.

Not that much different from self-learning on your own.

There are full-blown master's programs right now where the curriculum is a bunch of public YouTube videos.

Outdated ones.

Look at who's teaching it. Professors with PhDs and little relevant hands-on experience, in a field that shifts every couple months.

A lot of these programs are just straight cash grabs.

So you spend two years and real money to come out knowing what a free playlist would have taught you.

Then you apply anyway and you're competing with people who never went.

You're not behind because you didn't go back to school.

You're behind because what you'd be learning is from 10 years ago.

Comment "OUTDATED" and I'll send you what I'd do instead.

[Kedeisha Bryan @kedeishaindata]

The data analyst role you know is changing.

2026 will demand more.

Gartner predicts that 80% of analytics tasks will be automated.

I coach career changers into $100K+ data careers, here's what I see coming 👇🏽

The "pull a report and send it over" analyst? That's gone. AI handles those tasks in seconds now.

The analyst who only knows SQL and Excel? They'll struggle. Companies expect more.

Here are my 5 predictions for data analytics in 2026:

𝟭. 𝗔𝗜 𝗳𝗹𝘂𝗲𝗻𝗰𝘆 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲

You won't compete with AI.

You'll compete with analysts who USE AI.

Prompt engineering, AI-assisted analysis, automated workflows.

Learn them or get left behind.

𝟮. 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 𝗯𝗲𝗮𝘁𝘀 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝘀𝗸𝗶𝗹𝗹𝘀

Anyone can pull numbers.

Few can make executives care.

The analysts who translate data into decisions will run the room.

𝟯. 𝗧𝗵𝗲 "𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗔𝗻𝗮𝗹𝘆𝘀𝘁" 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱

SQL + Python + Visualization + Communication.

Not "nice to have."

Expected.

One-trick analysts will struggle to compete.

𝟰. 𝗥𝗲𝗺𝗼𝘁𝗲 𝗿𝗼𝗹𝗲𝘀 𝗴𝗲𝘁 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲

Companies figured out they can hire globally.

Your competition isn't local anymore.

Stand out or blend in.

𝟱. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗰𝘂𝗺𝗲𝗻 \> 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝗽𝘁𝗵

Knowing the business matters more than knowing every Python library.

The best analysts understand revenue, margins, and what keeps the CEO up at night.

Here's the truth:

The bar is rising.

But for those who adapt? The opportunities are bigger than ever.

I've watched career changers land $100K+ roles by focusing on what actually matters.

Not degrees. Not certifications.

Skills that solve problems.

Which prediction hits hardest for you?

Drop a number below. Let's talk about it.

[Kedeisha Bryan @kedeishaindata]

I read through thousands of data analyst job postings to find out what employers actually ask for.

Three skills came up over and over, and one of them shows up in 78% of postings while most beginners treat it as optional.

None of them are what people spend their first six months learning 🎥

Career changers, which of the three are you strongest in? 👇

Comment ROADMAP and I'll send you my free 30-day roadmap to getting hired.