Career Advice

How to Become a Data Scientist (What to Do to Get Hired)

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Learn how to become a data scientist step-by-step. Discover education options, skills, and the steps to start your career in data science.

Written by:

Astley Cervania

Edited by:

To become a data scientist, you first need to learn the core skills, including Python, SQL, statistics, and machine learning. Next, build projects that demonstrate your ability to solve real-world problems. Create a portfolio, take a certificate or structured course, and get practical experience. Once you’ve built up your professional background and key skills, apply for data science jobs.

Data science can feel intimidating from the outside. All the buzzwords, math, machine learning, Python memes… I get it, but this career is actually more accessible than most people might think.

You don’t need to be a Silicon Valley genius or a PhD-level mathematician. It might be tough, but it’s certainly possible to break into this field with enough curiosity, persistence, and willingness to learn.

In this guide, we’ll walk you through what to do to land your next role as a data scientist.

And if you want to get straight into creating a professional resume to land your next role in data science, use our AI resume builder. Our AI tools help you build a targeted resume in minutes.

What Does a Data Scientist Do?

You can see data scientists almost like translators since they take messy, confusing data and turn them into insights people can actually use.

Companies collect absurd amounts of information: customer behavior, sales trends, product usage, support logs, sensor data, you name it. However, raw data by itself is basically just digital dust. A data scientist makes sense of it.

Here’s what that usually looks like:

Cleaning and organizing data.
Analyzing data to find patterns or problems.
Building machine learning models.
Creating dashboards and reports.
Presenting insights to teams who really don’t want to see a single line of code.
Working with engineering, product, or business teams to make data-driven decisions.

If you like solving problems and using data to tell stories, this career can feel very satisfying.

Learn how to use the best career tools that can help you land your next role:

How to Become a Data Scientist

Here’s how to become a data scientist:

Learn the core skills: Python, SQL, statistics, and machine learning basics.
Build projects that show real-world problem solving.
Create a portfolio on GitHub or a personal website.
Take a certificate or structured course (optional, but helps).
Get practical experience via internships, freelance work, or Kaggle competitions.
Apply for entry-level data roles, then move towards the data scientist job title.

In practice, most data science roles revolve around data engineering, analytics, and machine learning, as shown by one Reddit user below.

That might sound like a lot (and, honestly, it is), but once you’ve built the key skills, the next challenge is learning how to communicate them clearly on your resume, in your portfolio, and during interviews. That’s often what separates strong candidates from the ones who get overlooked.

Let’s break down the steps to getting hired as a data scientist below.

1. Learn the core skills

Before you worry about fancy AI models or building the next ChatGPT, you need the data science fundamentals.

These are the “non-negotiables” every data scientist relies on daily:

Python

Python is the bread and butter of data science. It’s readable, flexible, and loaded with libraries that handle everything from data cleaning to deep learning.

Start with:

pandas (data cleaning)
NumPy (math + arrays)
Matplotlib/Seaborn (visualization)
scikit-learn (classic ML models)

Once you understand these, the rest of the ecosystem starts making sense.

SQL

SQL is how you actually get data. Every company stores data in a database, and SQL is what extracts it.

Get familiar with the following:

SELECT, JOIN, GROUP BY
Filtering and sorting
Basic window functions (bonus points)

SQL is also often tested in interviews, so don’t skip this.

Statistics

Focus on the following:

A/B testing
Probability basics
Regression
Sampling + distributions
Statistical significance

If you can explain your results without confusing the room, then you’re already ahead.

Machine learning basics

You don’t always need to jump into neural networks first. Start with classic algorithms:

Linear/logistic regression
Decision trees
Random forests
KNN
Clustering

The goal here is to understand when and why to use each model. You can also use this AI and data scientist roadmap for a more structured view on what to learn.

Read more on adding relevant skills to your resume:

2. Build projects that show real-world problem solving

Projects are your “proof of work.” They show employers you can actually apply what you know, not just watch tutorials.

Great projects:

Solve an actual problem
Use real, messy data
Walk through your thought process
Produce actionable insights

Here are some ideas to build your portfolio as a data professional:

Predicting housing or Airbnb prices
Customer churn prediction
Stock trend exploration (careful with claims!)
Fake review detection
Spotify listening analysis

3. Create a portfolio on GitHub or a personal website

Here’s what to include in your portfolio:

Your code (clean + commented)
A project README written like a case study
Visualizations
Key findings
Links to datasets or notebooks

And here’s where you could host it:

GitHub
A personal site
LinkedIn posts summarizing your work (very underrated)

If someone can scan your portfolio and quickly understand your skills, you’re golden. Add these to your resume header and LinkedIn profile.

4. Take a certificate or structured course

Courses aren’t always required for a data scientist job application but they give you structure and help reinforce your credibility. They also help fill in gaps you may not know you have.

Here are some examples:

Google Data Analytics Certificate
IBM Data Science Professional Certificate
Coursera, edX, Udacity Nanodegrees
MITx or HarvardX statistics courses

Bootcamps can also help, however, take into account the ones with solid job placement support. Plus, if you’re self-taught, a certificate can help reassure employers that you’ve built a real foundation.

5. Get practical experience

Companies want proof that you can work with real data in real environments. Fortunately, you don’t need a full-time job to build experience.

Here are ways to get hands-on practice:

Internships (ideal if you can swing it)
Freelance projects (small businesses have tons of data problems)
Kaggle competitions (great for sharpening your modeling skills)
Volunteering your analytics skills (nonprofits, student orgs, local businesses)
Open-source contributions (shows teamwork and coding discipline)

Even 1–2 small real-world projects can separate you from other candidates.

6. Apply for entry-level data roles, then move towards the data scientist job title

You don’t usually walk straight into “data scientist” as your first tech job. Instead, most people start in roles that build your analytical and technical muscles.

Here are some entry-level positions to consider targeting:

Data Analyst
Business Intelligence (BI) Analyst
Analytics Engineer (junior)
Machine Learning Assistant
Operations/Data Specialist roles

Once you have 1–2 years of consistent experience handling data, building dashboards, or working with models, the transition to a full data scientist role becomes much smoother.

Relevant guides on job hunting:

How Long Does It Take to Become a Data Scientist?

It’s possible to get job-ready for a data scientist role in 6–12 months. That said, here’s a sample timeline:

0–3 months: Python, SQL, and basic stats
3–6 months: Projects + GitHub portfolio
6–9 months: Machine learning + advanced projects
9–12 months: Applications, interviews, and polishing your resume

If you’re transitioning from a related field (like data analytics), the process can be much faster.

Data Scientist Salary

The median pay for data scientists in 2024 was $112,590 per year, according to the U.S. Bureau of Labor Statistics. Your pay rate will vary by company, location, and experience but the earning potential is strong across the board. Data science is one of the highest-paying career paths in tech, and it’s also considered a high-income skill.

Summary

Here’s a recap of how to become a data scientist:

Learn the essentials: Python, SQL, statistics, machine learning.
Build practical, real-world projects.
Create a portfolio that shows your thought process.
Choose your education path.
Get experience via internships, Kaggle, or entry-level analytics roles.
Tailor your resume and start applying.

If you’re curious, analytical, and enjoy solving problems, becoming a data scientist might just be for you.

FAQs

Do I need a degree to become a data scientist?

Many data scientist job postings may include a degree as a requirement. Therefore, it certainly helps to have a degree. However, some data scientists out there come from non-technical backgrounds like marketing, finance, biology, or even journalism. What tends to matter most is your portfolio, skills, and ability to work with real data. Certificates and structured courses can also fill the gap if you’re self-taught.

Do I need to know advanced math or deep learning?

No, you don’t need advanced Ph.D. level knowledge in math and deep learning. However, you will need practical statistics, problem-solving skills, and a good grasp of basic machine learning. Advanced math or deep learning is optional unless you’re targeting heavy research or AI engineering roles.

Is data science still in demand or is the field too saturated?

Data science is still in demand. Companies are collecting more data than ever, and they need people who can make sense of it. Entry-level roles can feel competitive, but a strong portfolio can set you apart from the sea of generic applicants.

How do data scientists differ from data analysts?

Data analysts focus more on reporting, dashboards, and business insights. In contrast, data scientists go deeper by building models, running experiments, and working with larger, messier datasets. A lot of people start as analysts and then move into data science after building more technical skills.

Will AI or automation replace data scientists?

In short, no. It may automate tasks, but likely cannot automate things like business context, asking the right questions, interpreting results, designing experiments, and communicating insights. Companies still need humans who understand nuance and can guide decisions.

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Astley Cervania

Astley Cervania is a career writer and editor who has helped hundreds of thousands of job seekers build resumes and cover letters that land interviews. He is a Rezi-acknowledged expert in the field of career advice and has been delivering job success insights for 4+ years, helping readers translate their work background into a compelling job application.

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