Data is everywhere nowadays, with companies collecting it like crazy but not always sure how to use it.
That's where Data Scientists come in. They take all this seemingly unimportant information and turn it into something a business can actually act on. Whether that's spotting a trend nobody noticed, building a model that predicts customer behaviour, or figuring out why a product isn't selling the way it should.
As you can imagine, this is in high demand and pays well. And the best part is you don't need a degree to get hired in this role. You just need to learn the right things in the right order.
Which is exactly what we're going to cover in this guide...
How to become a Data Scientist without a degree!
So just a quick heads up. The steps I'm about to give you are based on our Data Scientist, AI, and Machine Learning roadmap:
Become a AI & Machine Learning Engineer
16 milestones 11 courses
Step-by-step roadmap where you'll learn to code and build a portfolio.
Curated curriculum of courses, workshops, challenges, projects, and action items.
Become a AI & Machine Learning Engineer from scratch and actually get hired.
Earn on average per year:
$195,425
US salary data collected from Indeed, LinkedIn, and Web3.career 2026.
Wait, it covers 3 different careers?
Yep! See, the thing is, although these 3 careers are some of the biggest areas in tech right now, they're all based on the same core skillset and tools, just applied differently.
To give you a quick idea of how they differ:
Data Scientists focus on analysing data to find patterns, build predictive models, and turn raw information into insights that help businesses make better decisions. That's what this guide is all about
Machine Learning Engineers take those models and focus on building, training, and optimising them at scale. More engineering, less analysis
While AI Engineers go broader still, covering the full lifecycle of AI-powered applications, often using pre-built models and APIs to build products on top of them
This overlap is why they share a roadmap. Learn the core skills once, and you're genuinely set up for all three. (You just need to learn some additional skills for whichever career you decide to specialize in).
Not bad for one learning path right!?
So with that out of the way, let's break this all down, what to learn, and the best order to learn it all.
Optional step. Speed up your learning
Because you're going to be learning a lot of new skills to become a Data Scientist, I recommend taking a slight detour and checking out this guide and this course:
![Learning to Learn [Efficient Learning]](https://images.ctfassets.net/aq13lwl6616q/6VWcUgLgG0SU55ORlILe2S/e186361aeb48561bcd19ae6486577022/Learning_to_Learn.jpg?w=1200&h=672&q=50&fm=webp&bg=transparent)
Average time to complete: 5 days
This course will teach you how to learn using concepts you've never heard of before.
Why care?
Because it'll help you learn faster, which will then reduce the total time it takes you to learn all these other skills you'll need. (It's kind of like stopping the car to fix a flat tire, because you know it will make the whole journey much quicker and smoother).
Like I say, it’s optional but definitely worth it.
Once you've done that, it's time to learn the core skills to become a Data Scientist.
Step #1. Learn the core required skills
Learn Python
Almost every tool, library, and workflow you'll use as a Data Scientist is built around the Python programming language:
Pandas for working with datasets
NumPy for numerical computing
Matplotlib and Seaborn for visualisation
Scikit-learn for machine learning
The list goes on, but almost all of it uses Python. That's why, before you start applying any of those tools to real data problems, it's worth knowing the language properly. Not just enough to copy and paste code from tutorials, but well enough to write your own scripts, understand what's happening when something breaks, and adapt what you've learned to problems you haven't seen before.
The good news is we have a Python course that can teach you all of this:
Average time to complete: 60 days. However, this is based on the average students' feedback on how long it took them to complete. You can do this faster; but will depend on how much time you can spend learning each week. (This is true for all the resources on this list).
You'll go from the absolute basics so you can learn with no prior skills, and then learn all the way up to object-oriented programming, working with APIs, and building real projects. So that by the end, you'll have a solid foundation that makes everything that comes next significantly easier to pick up.
Speaking of which, it's time to learn the tools...
Learn Data Science
So now that you have Python under your belt, it's time to put it to work and learn the core Data Science tools that we mentioned in the last section: Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.
So what are they?
Well, each one plays a specific role in a Data Scientist's workflow:
Pandas is what you use to load, clean and manipulate data. This matters because real-world data is messy — missing values, inconsistent formatting, columns that don't make sense. Before you can find any insights or build any models, you need to be able to wrangle the data into a usable state. Pandas is how you do that.
NumPy handles the numerical and mathematical operations that sit underneath almost everything else you'll do. You won't always use it directly, but it's what a lot of the other tools are built on, so understanding it makes everything else click faster.
Matplotlib and Seaborn are your visualisation tools. Data on its own is hard to interpret — these are how you turn it into charts and graphs that make patterns obvious, both to you when you're exploring a dataset, and to stakeholders when you're presenting your findings.
Scikit-learn is where machine learning comes in — it's the library you use to actually build predictive models. If Pandas gets your data ready and Matplotlib helps you understand it, Scikit-learn is what lets you do something with it.
Remember how I mentioned earlier that a lot of the core skills across Data Science, Machine Learning and AI overlap? Well that's especially true here. As a Data Scientist you'll be using machine learning for predictive modelling — going beyond just "what's happening?" to "what's likely to happen next?" What trends are hiding in this data? Which customers are about to churn? Which product is going to underperform? That's why ML is part of the skillset, not just a nice to have.
The good news is, I cover all of it in this course:
Estimated time to learn: 90 days.
You'll work through each of these tools hands-on, applying them to real problems that will not only help you learn faster, but give you things to talk about in interviews. More on that later but for now, I've gone ahead and shared the first 10 hours of the course below for free:
Just be aware that the course is updated regularly each year, whereas this video is a few years old now.
So at this point you have Python under your belt and you know the core tools you'll use to analyse and model data. But there's a step we haven't covered yet and thats how to actually get that data in the first place.
Learn SQL
Almost every company in the world stores its data in databases. Customer records, sales transactions, product information, user behaviour, etc.
But as a Data Scientist, you're rarely handed a perfectly packaged dataset to work with. Most of the time, you need to go and get the data yourself, pulling it from one or more databases, filtering it down to what's relevant, and combining different sources together before you can even begin your analysis.
SQL (pronounced "sequel") is the language you use to talk to those databases and connect you to the data you'll then bring into your Python tools and work with:
Average time to complete: 45 days.
You'll start from the very basics of what a database is and how they work, and build up to writing queries that can pull, filter, and combine data from real-world databases.
Learn Excel
Here's one that might surprise you — but stick with me.
You've just learned Python, a suite of Data Science tools, and SQL. So why on earth are we talking about Excel? It feels like a step backwards, right?
Here's the thing though. Around 750 million people use Excel worldwide, and the vast majority of companies — regardless of how technically advanced they are — still use it daily for reporting, sharing data, and quick analysis. Which means at some point, someone is going to drop a spreadsheet in front of you and expect you to know what you're doing with it.
It's also worth knowing that even experienced Data Scientists use Excel regularly — not for the heavy lifting, but for quickly eyeballing a dataset before pulling it into Python, or for sharing findings with people who aren't technical. Your colleagues, managers, and stakeholders are likely living in it, and being able to meet them there makes you significantly more useful on the job. And unlike Python, it's already installed on virtually every work computer and requires no setup at all.
A lot of Data Science courses skip it entirely because it's not as exciting as building machine learning models. But that's exactly the problem — you can have all the advanced skills in the world and still get caught off guard the moment someone shares a spreadsheet in a meeting.
This course makes sure that never happens:
Average time to complete: 34 days.
You'll cover everything from formulas and functions to Pivot Tables and data visualisation — so that Excel is never the thing that trips you up.
Learn Statistics
You're going to be working with a lot of data in this role, which is why you also need to understand math. More specifically, statistics.
Why?
Well, statistics is what tells you whether what you're seeing is actually real. Is that trend genuine, or just a coincidence in the data? Is your model actually good, or did you get lucky? Without statistics, you're essentially guessing, but with it, you can back up your findings with confidence.
Now the good news is you'll have already learned some of this during the Data Science course. However, it's worth taking a deeper dive into this so that you can also learn how to use this alongside Python so that it can do some of the heavy lifting for you.
The key though is to make sure you understand what the results mean and what to do with them which is why I recommend this course next:
Average time to complete: 42 days.
You'll learn techniques like hypothesis testing and confidence intervals, — which is just a formal way of asking "is this result real or did it happen by chance?" — which let you say not just what you found, but how sure you are about it.
All stuff that can make your future employer love you and your work.
Speaking of which, at this point you now have the core skills required to start applying for entry level Data Science roles.
Step #2. Build a portfolio to prove you can do the work
Another slight detour, but 100% required.
So, the really cool thing about working in tech is you don't need a degree to get hired. However, you do need to prove you can do the work, and that’s where portfolios come in. You set one up and share your work there so that prospective employers can see what you’ve done and then possibly hire you.
You can learn how in the course below:
Average time to complete: 10 days
Once you've built your portfolio, make sure to go ahead and add in any projects that you've built so far!
Step #3. Apply for Data Scientist jobs
Alright, now it's time to apply for jobs and get hired! However, if this is your first-ever tech job, you need to understand that the hiring and interview process at tech companies is different from those you might have worked before.
It can vary based on the company, but the process is usually:
An online quiz when you first apply (although not everyone will do this). It’s a simple filter to see if you’re worth spending one-on-one time with, as they get thousands of applicants
A basic technical assessment done from home
An in-person technical interview where they ask you how to solve specific AI-focused questions
Potentially a final project to complete that would replicate your daily work. This will give them an idea of how you work, as they want to hire people who are not only capable but can deliver on time
A behavioral interview to see if you would make a good team fit
Yes, it's more effort than a normal job, but for 6-figures in salary and no degree required, I think you can agree it's worth the effort and money saved!
So, how do you pass the interview?
Well, if you're a ZTM member, then I HIGHLY recommend you check out Andrei's course on getting hired at your dream job:
Average time to complete: 12 days.
He covers the entire application and interview process in detail, including his technique, where he gets a 90% interview success rate! That being said, it can take a few months of applications to get a job locked in, so let's look at some further skills you can learn....
Step #4. Skill up further
Don't get overwhelmed by the number of skills here. As I said just before, you already have enough to get hired. These are just additional skills that can help you be more appealing to employers or even start in more senior roles.
So let's break them down.
Learn Business Analytics
Business Analytics is essentially the applied side of Data Science. Instead of just finding patterns in data, you're using those patterns to answer real business questions.
Should we run this marketing campaign?
Is this product change actually making a difference?
Which customers are most likely to churn and what can we do about it?
That's the kind of thinking that makes a Data Scientist genuinely valuable to a business, not just technically capable and this course teaches you how to do exactly that:
Average time to complete: 20 days.
You'll go beyond the analysis and learn how to use your data skills to test ideas, measure impact, and make recommendations that actually influence decisions, using techniques like A/B testing, predictive analytics, and causal inference, all in Python.
Learn Advanced Excel and Business Intelligence
You already know Excel from earlier in this roadmap — but this course takes it somewhere completely different.
At the basic level, Excel is a spreadsheet tool. But at the advanced level it becomes a fully fledged business intelligence platform. And that's what this course is about. You'll learn Excel's built-in power tools — Power Query, Power Pivot, and the DAX programming language — which let you pull data in from external sources, model it, and build dynamic dashboards that update automatically.
Why does this matter?
Because being able to build a dashboard that gives a business a live view of its key metrics — sales performance, customer trends, campaign results — is exactly the kind of thing that gets noticed. It takes your Data Science skills and turns them into something non-technical people can actually see and use.
This course teaches you how to do all of that:
Average time to complete: 15 days.
Learn Power BI (Optional)
Power BI is Microsoft's dedicated business intelligence tool, and it's the industry standard for building dashboards and data visualisations in a professional setting. Think of it as everything you just learned in the Advanced Excel course, but in a tool built specifically for that purpose and with a lot more horsepower behind it.
Average time to complete: 25 days.
However, not everyone needs to learn this, simply because although a lot of companies rely heavily on it, not every company uses it. But if you see it appearing in job listings for roles you're interested in, it's definitely worth learning.
Become a Data Scientist today!
So there you have it - the entire roadmap to become a Data Scientist within the next 6-12 months, or sooner. As well as the path to move up into more senior positions later on.
Not bad for a complete career change with zero prior experience and a six-figure salary, right!?
Data Science is one of the best careers you can get into right now. Demand is high, the skills are genuinely learnable without a degree, and the number of companies that need people who can make sense of their data is only going in one direction. Whether that's a startup trying to understand their customers, or a global company trying to get ahead of the competition — they all need Data Scientists.
You now have the exact roadmap to become one.
P.S.
Want some great news?
All of the courses I’ve mentioned in this guide are included in a single Zero To Mastery Academy membership. That means if you become a member, you have access to all of these courses right away and will have everything you need in one place.
Plus, as part of your membership, you'll get to join me and 1,000s of other people (some who are alumni mentors and others who are taking the same courses that you will be) in the ZTM Discord:
Ask questions, help others, or just network with other Data Scientists and tech professionals.
Make today the day you took a chance on YOU. There's no reason why you couldn't be applying for Data Science jobs in just a few months from now if you just follow the steps I outlined and put in the hard work.
So what are you waiting for 😀?
Come join me and get started on becoming a Data Scientist today!
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