The demand for data scientists is booming, expected to grow over 35% through 2032. But, if you’re just starting out, the path can feel overwhelming.
Do you need a degree? Is a bootcamp enough? What skills actually matter? Whether you’re fresh out of college or changing careers, this guide breaks down exactly what you need to land an entry-level role in data science.
We’ll walk through education options, certifications, technical and soft skills, hands-on experience, and how to build a resume and portfolio that gets noticed without the fluff or confusion.
Let’s clear things up and help you take the next step.
Getting into data science doesn’t mean you need a Ph.D. But, some kind of formal or structured learning helps, especially when you’re starting from zero.
For many roles, a bachelor’s degree is the baseline. Here’s how the levels break down:
Most data scientists start with degrees in:
A strong math foundation is helpful, but it’s not everything. What matters more is your ability to learn technical tools and think analytically.
Some go further with a master’s to dig into machine learning, big data, or applied analytics. Programs like:
Having a master’s can give you a leg up for research-heavy roles or higher-paying jobs, but it’s not always required.
Very few entry-level jobs need a Ph.D. It’s mostly useful if you’re heading into academia or highly technical roles like AI research or algorithm development.
If traditional school isn’t an option, you still have solid alternatives.
Bootcamps are fast-paced, focused, and practical. Many are built around real-world projects. Good ones will teach:
They can be intense, but if you’re motivated, they can work well, especially if paired with self-study and internships.
Sites like Coursera, edX, and Udemy offer complete courses in everything from Python programming to deep learning. Combine a few of these and you can cover as much ground as a university semester at your own pace.
Certifications won’t replace real experience, but they can help you prove your skills, especially if you’re coming from a different field.
They work best when they:
Here’s a quick breakdown of common categories:
Stick to well-known platforms or companies. These are widely accepted:
Let’s talk tools and knowledge. These are the hard skills hiring managers actually look for and ones that bootcamps and degree programs often focus on.
These are the languages that power most data science projects:
You won’t get far without a solid understanding of the math behind the models:
Hiring teams love to see real machine learning skills; not just theory, but projects that show you’ve applied them.
Bootcamps often cover these hands-on with real datasets and feedback loops.
It’s not just about analyzing data…you have to explain it, too. That’s where visual tools come in:
Some companies deal with data at a scale you can’t process on a laptop. Knowing these tools helps:
Many data science pipelines now run on the cloud. At a minimum, get familiar with:
Even a basic understanding of how cloud platforms work can give you a leg up during interviews.
Having real-world experience matters, but that doesn’t always mean years on the job. What counts is how you show what you can do, especially when you’re just starting out.
You don’t need a decade of experience to get your foot in the door. What helps most:
If you’re switching careers, a solid GitHub with clean code and detailed write-ups can say more than your résumé ever could.
For higher-level roles, companies look for:
Contributing to open source is one of the best ways to get noticed, especially if you don’t have formal experience. You can:
Having an active GitHub profile with real contributions shows initiative and consistency.
Knowing the right people opens doors. Whether it’s someone reviewing your résumé or referring you for a job, relationships matter.
Try:
Many bootcamps even help with networking directly through job boards, alumni channels, or project showcases.
Your resume and portfolio are two of the most important tools you have when breaking into data science.
Whether you’re just out of school or switching careers, these are what hiring managers will use to judge your potential. Done right, they show that you not only know the tools but can actually apply them to solve real problems.
Your resume should show more than just job titles or tools. Focus on results. Instead of saying “built a forecasting model,” say “created a model that boosted accuracy by 22%.”
That one line tells a stronger story and gives your work real weight. It also helps to tailor your resume to each job posting.
Many companies use Applicant Tracking Systems (ATS) that scan for keywords. If yours doesn’t include the right ones, it may never be seen by a human.
Keep your resume clean, focused, and tailored. Highlight relevant tools like Python, SQL, and Tableau. Use metrics when possible, and always aim to show the outcome of your work, not just the task.
Your portfolio is where you show how you think and work with data. It should walk someone through your process, from identifying the problem to finding data, cleaning it, analyzing it, and presenting the results.
Code is important, but clarity is just as valuable. Include visualizations, clear explanations, and summaries in plain language.
If you’ve completed a bootcamp, you likely already have projects you can feature. Take the time to polish them. Host your work on GitHub and make it easy to understand. Even two or three well-explained projects can help you stand out and land that first role.
Knowing how to code or build models will get your foot in the door, but what really keeps you there is how well you work with people, solve problems, and explain your insights. These non-technical skills are often what set one applicant apart from another.
A good data scientist doesn’t just analyze numbers…they understand what those numbers mean in the context of a business problem.
Being able to translate vague goals into clear data questions and knowing what matters to stakeholders makes you far more effective on the job.
You’ll often be the bridge between raw data and decision-makers. That means you need to explain complex ideas clearly, whether you’re writing a summary, giving a presentation, or just answering questions.
Active listening also matters. Understand what others really need before diving into the data.
Every project brings unexpected challenges. Whether it’s messy data, unclear goals, or changing requirements, being able to think critically and adapt on the fly is essential. Creative thinking, patience, and logical reasoning go a long way here.
Data science isn’t a solo sport. You’ll likely work with product managers, engineers, analysts, and designers.
Teamwork matters, and being easy to work with often makes the difference between good and great. Bootcamp group projects are a great way to practice this before you’re on the job.
The tools and techniques in data science change fast. Staying curious and willing to learn new things on your own keeps you sharp.
Whether it’s trying out a new library, reading papers, or just asking better questions, learning never really stops.
Raw results don’t always speak for themselves. The ability to shape your analysis into a clear, memorable narrative helps others understand the “so what” behind the data.
This is especially important when talking to non-technical teams or execs who need to make fast decisions.
Getting into data science can feel like a lot, but focusing on the right areas makes it manageable.
Build a solid foundation through education or bootcamps, learn practical technical skills, and develop your soft skills, especially communication and problem-solving.
Experience doesn’t have to mean a full-time job; solid projects, internships, or open-source contributions go a long way. A strong resume and portfolio can set you apart, even at the entry level.
Keep learning, stay curious, and focus on applying what you know. With the right mindset and consistent effort, a data science career is well within reach.
