Several years ago, we received a call from a man who wanted to take a course in SQL or Tableau, earn a certificate and use it to break into information technology.

He was employed full-time outside of technology and wanted to know how long it would take before he knew enough to become employable.

It sounded like a straightforward question: Which course should I take?

But that wasn't really the question.

He was trying to enter an enormous industry containing occupations as different as software development, data analytics, cybersecurity, cloud infrastructure, database administration, business analysis, technical support and project management. He didn't yet know which kind of work interested him, which of his existing abilities might transfer or what employers in those fields actually expected him to know.

Choosing between SQL and Tableau was premature.

Before choosing the course, he needed to choose a direction.

Technology Isn't a Career

People often talk about wanting to "get into technology" as though technology were a profession.

It isn't.

A software developer and a cybersecurity analyst may spend their days doing completely different things. So might a data analyst and a cloud engineer. A business analyst may spend more time talking with people and understanding processes than writing code. A database professional may spend years developing expertise that has relatively little to do with front-end software development.

Someone can be exceptionally good at one of these jobs and miserable doing another.

That's why the first question shouldn't be:

What technology should I learn?

A better question is:

What kind of problems do I enjoy solving?

Do you like analyzing information and discovering patterns?

Do you enjoy building things?

Do you like troubleshooting problems when something isn't working?

Are you fascinated by how systems communicate?

Do you enjoy organizing complicated processes?

Do you naturally notice security risks?

Do you enjoy working with people to understand what they need?

Those preferences can tell you considerably more about a promising technology career than choosing whichever certification happens to be popular this year.

Start With the Jobs

Before paying for training, spend some time studying the jobs you think you might want.

Find 20 or 30 actual openings.

Don't worry initially about whether you're qualified. You're investigating.

Read the responsibilities before reading the requirements.

What would you actually be doing every day?

A job description may ask for Python, SQL, AWS, Azure, Kubernetes, Power BI or any number of other technologies, but those are tools. Look beyond them and determine what problem the employee is being hired to solve.

Then compare several positions.

If one sounds fascinating, find ten more like it.

If reading the responsibilities makes you think, "I would hate doing this eight hours a day," you've just learned something extremely valuable without purchasing a course.

Today You Have a Career Counselor Sitting on Your Computer

When we originally wrote about this subject in 2020, someone contemplating a technology career could search the web, browse job boards, participate in forums and talk with people already working in the industry.

You should still do all of those things.

But today you have another extraordinarily useful research tool: artificial intelligence.

Suppose you're an accountant who wants to move into technology. Or a teacher, engineer, salesperson, administrative professional or operations manager.

Give an AI system a version of your résumé with any personal information you don't want to share removed. Then ask:

"Based on my existing experience and skills, identify six realistic technology career paths I should investigate. Don't recommend courses or certifications yet. Explain what each job actually involves, which of my existing skills would transfer, what important skills I'm missing and how difficult the transition would likely be."

Don't stop there.

Find actual job descriptions for the two or three paths that interest you and give those to the AI as well.

Then ask:

"Compare my experience with these job descriptions. Which requirements appear repeatedly? Which do I already satisfy? Which gaps could reasonably be addressed through training, and which appear to require substantial professional experience?"

Now the conversation becomes specific.

You're no longer asking the internet whether cybersecurity, AI, SQL or cloud computing is "a good career."

You're asking what makes sense for you.

Don't Let AI Choose Your Career

There is an obvious danger here.

Artificial intelligence can sound remarkably confident while giving mediocre advice.

It may recommend a fashionable career because there's a tremendous amount written about it. It may underestimate how difficult an entry-level market has become. It may treat completing a certification as equivalent to professional experience. And it cannot know whether you'll enjoy doing the work.

Use AI to generate questions and investigate possibilities.

Then verify the answers.

Look at real job postings. Talk with people doing the work. Read professional discussions. Watch someone perform the job. Try introductory exercises.

AI is an excellent research assistant.

It shouldn't be the person deciding what you do with the next ten years of your life.

Before Spending Thousands of Dollars, Spend a Weekend

Once you've identified a few possible directions, experiment.

Interested in data analytics? Download a public dataset and try answering questions with Excel, SQL or a visualization tool.

Interested in software development? Build a small application. Today, AI can help you get started, explain unfamiliar concepts and troubleshoot errors.

Interested in cloud computing or DevOps? Deploy a simple application and learn what actually happens between source code and a running system.

Interested in cybersecurity? Work through an introductory security lab and see whether investigating systems and vulnerabilities holds your attention.

Interested in business analysis? Take a familiar business process, map how it currently works, identify problems and write requirements for improving it.

The objective isn't to become employable over a weekend.

It's to discover whether you find the work interesting enough to spend the next several months learning it.

There's an enormous difference between liking the idea of a career and liking the work the career requires.

Find that out as cheaply as possible.

Your Previous Career Didn't Disappear

People changing careers sometimes make the mistake of assuming they're starting over.

Usually they aren't.

A teacher may understand communication, presentation and learning better than many technologists.

An accountant may bring valuable knowledge to data analytics, financial systems or enterprise software.

An engineer may already possess analytical and problem-solving skills applicable to software, infrastructure or automation.

Someone from operations may understand workflows, process improvement and organizational problems.

A salesperson may understand customers and requirements extraordinarily well.

Domain knowledge matters because organizations don't hire technology professionals merely to operate technology. They hire them to solve problems in banking, healthcare, manufacturing, construction, government, insurance, transportation, education and thousands of other fields.

Sometimes the shortest route into technology runs directly through the industry you already understand.

Don't discard ten or twenty years of experience simply because you're changing direction.

Build on it.

A Certificate Is Evidence of Learning, Not Proof of Ability

Certifications can be useful.

Some are respected within particular industries. Some provide a structured way to learn unfamiliar material. Some employers specifically request them.

But a certificate and an ability to perform the work are not the same thing.

If two candidates have completed the same course, but one can explain a project they built, the problems they encountered, the decisions they made and what they would do differently next time, that person has something else to offer.

Evidence.

A certificate tells an employer what you studied.

Evidence shows what you can do.

That evidence doesn't necessarily need to come from paid employment.

Build something.

Analyze something.

Automate something.

Deploy something.

Document what happened.

Explain why you made the decisions you made.

If something failed, explain what you learned fixing it.

You're not trying to manufacture fake professional experience. You're demonstrating curiosity, initiative and the ability to apply what you've learned.

Learn Enough to Ask Better Questions

There was one piece of advice in our original 2020 article that I still believe strongly.

At the beginning of learning something new, you often don't know enough to know what you don't know.

That's normal.

The purpose of your first few weeks isn't necessarily mastery. It's orientation.

Learn enough that the terminology stops sounding foreign. Learn enough to recognize the major technologies and concepts. Learn enough to understand a job description. Learn enough to have an intelligent conversation with someone already doing the work.

Most importantly, learn enough to ask better questions.

Once you can do that, choosing training becomes much easier.

Instead of saying:

"I want to get into IT. What course should I take?"

you might say:

"I have ten years of financial experience, I'm interested in data analytics, and the jobs I'm researching repeatedly require SQL and Power BI. I know Excel well but have never worked with a relational database. What's the best way to close that gap?"

Now we have something useful to work with.

Training Should Come After the Question

Professional training can accelerate learning enormously. A good instructor can organize a complicated subject, explain difficult concepts, answer questions, identify misunderstandings and save someone weeks of struggling alone.

But training works best when you know why you're taking it.

Don't take SQL because someone told you SQL is valuable.

Don't take cybersecurity because cybersecurity salaries sound attractive.

Don't learn Python because everyone says you should know Python.

Don't pursue a certification simply because the certification exists.

First understand the work.

Then understand what you already bring to it.

Identify what you're missing.

Experiment enough to determine whether you actually enjoy the subject.

Look at what employers are asking for.

Then use training to close a specific gap.

Ask a Better Question

The gentleman who called us years ago wanted to know whether he should learn SQL or Tableau.

Today, I would probably answer his question differently than I did then.

I wouldn't begin by recommending a programming language, a certification or a course.

I'd ask him to investigate himself.

What have you already spent years learning?

What kinds of problems are you good at solving?

What kind of work holds your attention?

Which technology careers make use of abilities you already possess?

What are employers actually asking for?

What could you try this weekend before committing months of your life and thousands of dollars?

Artificial intelligence makes answering those questions easier than it has ever been.

Use it.

Talk to people.

Study the jobs.

Try the work.

Then decide what you need to learn.

Because the objective isn't to collect technology courses.

It's to find work you're capable of becoming very good at—and then learn what you need to get there.