What Is a Data Scientist? Average Salary, Job Description & Education Requirements

Data scientist has become one of the most talked-about job titles in the tech world, and for good reason. Organizations across healthcare, finance, retail, and tech collect more data than ever, and they need people who can turn that raw information into decisions that actually move the business forward.

If you're weighing this specialized data analytics career path, you're probably wondering what the job looks like day to day, what it pays, and what kind of education gets you in the door. This article walks through all three, along with the skills employers look for and the steps most people take to break into the field.

What Does a Data Scientist Do?

At the core, a data scientist gathers data, makes sense of it, and turns that analysis into recommendations other people can act on. The work spans the full pipeline, from figuring out which data sources are worth collecting to building the models that extract patterns from that data and presenting the findings in a way both technical and nontechnical audiences can understand.

Day-to-day duties typically include:

• Determining which data sources are available and useful for a given project
• Collecting, cleaning, and organizing raw data so it's usable
• Building, testing, and refining algorithms and machine learning models
• Using visualization tools to present findings as charts, dashboards, or reports
• Making data-driven recommendations to stakeholders and business leaders

Some data scientists lean heavily into the engineering side, building machine learning systems and improving how a company's products or websites function. Others focus on research for academic or industry publications, and still others apply their skills to business functions like marketing, sales, or user engagement. The specific mix of duties varies quite a bit depending on the industry and the size of the organization.

Data Scientist vs. Data Analyst vs. Data Engineer

These three titles get used interchangeably fairly often, but each one covers a distinct part of the data pipeline. Understanding the differences can help you figure out which role actually matches the kind of work you want to do.

Data Analyst

A data analyst typically works with data that's already been collected and organized, focusing on identifying trends, building reports, and answering specific business questions. The role leans on tools like SQL, Excel, and business intelligence dashboards more than heavy programming or machine learning, which makes it a common entry point for people newer to the data field.

Data Scientist

A data scientist goes a step further, working with messier and often unstructured data to build predictive models and algorithms rather than just reporting on what already happened. The role combines statistics, programming, and machine learning to answer more open-ended questions, like what's likely to happen next or how a system should be optimized.

Data Engineer

A data engineer builds and maintains the infrastructure that makes the other two roles possible, designing pipelines that move, store, and process data at scale. This role is more software-engineering-focused, involving databases, cloud platforms, and the systems that keep data flowing reliably to analysts and scientists downstream.

Average Data Scientist Salary

Data scientists earn a median annual wage of $120,230, according to the Bureau of Labor Statistics. Pay varies quite a bit based on experience, industry, and location, with the lowest-paid 10% earning less than $67,240 and the highest-paid 10% earning more than $199,130.

Industry makes a substantial difference in take-home pay, even for people doing similar work. Median annual wages by top-paying industry include:

• Publishing, broadcasting, and content providers: $142,240
• Computer systems design and related services: $132,380
• Credit intermediation and related activities: $129,490
• Management of companies and enterprises: $128,050
• Insurance carriers and related activities: $108,650

Geography plays a role as well, with data scientists in major tech hubs and high cost-of-living metro areas generally earning more than the national median. Experience level and education also factor in heavily, since candidates with a graduate degree or several years of hands-on modeling experience often qualify for roles at the higher end of the pay scale.

Education Requirements for Data Scientists

Most data scientists need at least a bachelor's degree to enter the field, though the exact educational path varies by employer and specialty. Some roles accept a broader range of degrees than others, and the level of education expected often depends on how research-heavy or technical the position is.

Bachelor's Degree

A bachelor's degree in mathematics, statistics, computer science, business, or engineering is the most common entry point into data science. Because the role relies heavily on algorithms and statistical techniques, coursework in linear algebra, calculus, and probability and statistics builds the foundation most data science jobs require, alongside computer science courses that teach programming and database skills.

Graduate Degrees

Some employers require or prefer a master's or doctoral degree, particularly for research-focused roles or positions at companies competing for advanced technical talent. A graduate data science degree program typically deepens a student's understanding of machine learning, statistical modeling, and algorithm design beyond what an undergraduate curriculum covers, which can make candidates more competitive for senior or highly specialized roles.

Industry-Specific Experience

Some employers also look for candidates with background knowledge in their specific industry, on top of a data science education. For example, a data scientist applying to an asset management firm may need coursework or experience in finance, banking, or investments to understand the context behind the data they're analyzing.

Skills and Qualities Data Scientists Need

Strong technical training matters, but a handful of core skills separate data scientists who thrive from those who struggle to apply what they've learned. Employers consistently look for the following:

• Analytical skills: the ability to research questions thoroughly and interpret findings accurately
• Computer skills: proficiency writing code, developing algorithms, and using data visualization tools
• Communication skills: the ability to translate technical analysis into recommendations nontechnical stakeholders can act on
• Logical-thinking skills: the capacity to design and evaluate statistical models methodically
• Math skills: a strong grasp of statistical methods for collecting and organizing data
• Problem-solving skills: the persistence to work through messy data and unexpected obstacles in model development

Beyond these core qualities, most job postings also expect familiarity with specific tools and languages, including Python or R for programming, SQL for database work, and machine learning frameworks for building predictive models. Candidates who can point to hands-on experience with these tools, rather than just classroom exposure, tend to stand out during the hiring process.

How to Become a Data Scientist

There's no single path into data science, but most professionals follow a similar general sequence. Here's what that typically looks like.

Build a Foundation in Math and Statistics

Strong quantitative skills form the backbone of data science work, so a solid grounding in algebra, calculus, and statistics matters well before you touch your first dataset. High school and early college coursework in these subjects makes later, more advanced material far easier to absorb.

Earn a Bachelor's Degree in a Quantitative Field

Most data scientists complete a bachelor's degree in mathematics, statistics, computer science, or a related quantitative field. This coursework builds the technical foundation employers expect while also exposing students to programming languages and data-oriented software they'll use throughout their career.

Learn Programming and Data Tools

Formal coursework alone rarely covers every tool a working data scientist needs, so building hands-on skills in Python, R, and SQL is an essential next step. Many aspiring data scientists supplement their degree with online data science courses, bootcamps, or self-directed projects to round out their technical toolkit.

Build a Portfolio Through Projects or Internships

Practical experience helps candidates stand out, whether that comes through internships, research projects, or independent work analyzing publicly available datasets. A portfolio that shows real problem-solving, not just coursework, gives hiring managers concrete evidence of what a candidate can actually do.

Consider a Graduate Degree or Specialized Certificate

Because some employers prefer or require advanced credentials, many professionals pursue a master's degree or a specialized certificate to strengthen their qualifications, particularly if they're transitioning from an adjacent field. This step is optional for many entry-level roles but can open doors to more competitive or research-heavy positions.

Apply for Entry-Level or Junior Data Roles

Many data scientists start in adjacent roles, such as data analyst or junior data scientist, before moving into more senior positions. These early roles build the practical, real-world experience that rounds out a formal education and often lead directly into broader data scientist responsibilities over time.

Career Outlook for Data Scientists

Employment of data scientists is projected to grow 35% through 2035, much faster than the average for all occupations, according to the Bureau of Labor Statistics. That growth translates to roughly 24,800 openings projected each year on average, a mix of newly created positions and the need to replace workers who move into other roles or leave the workforce entirely.

Much of this projected growth stems from organizations generating and collecting more data than ever, combined with the continued integration of artificial intelligence into business workflows. Companies increasingly need data scientists to help them apply AI and other emerging technologies to product development, decision-making, and marketing, which points toward strong, sustained demand for the role well into the next decade.

FAQs About Becoming a Data Scientist

Here are answers to some of the most common questions people ask before pursuing a career in data science. These cover practical considerations that go beyond the basics already outlined above.

Is Data Science a Good Career Path?

For people who enjoy solving open-ended problems with data and don't mind ongoing technical learning, data science offers strong pay, high demand, and work that spans nearly every industry. The field does require continuous upskilling, since tools and techniques evolve quickly, so it suits people who genuinely enjoy staying current with new methods rather than those looking for a role that stays static year to year.

Which Industries Employ the Most Data Scientists?

Publishing, broadcasting, and content providers employ the largest share of data scientists, followed closely by insurance carriers, computer systems design firms, and companies focused on management and administration. Credit intermediation, which includes banks and lending institutions, also employs a meaningful share of the field's workforce, reflecting how central data analysis has become to financial decision-making.

Can I Become a Data Scientist Without a Computer Science Degree?

Yes, though it typically takes more deliberate effort to build the necessary technical skills outside a traditional computer science curriculum. Many data scientists come from math, statistics, business, or even scientific research backgrounds and fill in programming and machine learning knowledge through online courses, bootcamps, or self-directed projects paired with a strong portfolio.

What Certifications Help Data Scientists Advance Their Careers?

Certificates from platforms like Coursera, edX, and DataCamp can help demonstrate specific technical skills, particularly for career changers who don't have a data science degree to point to. Certifications tend to carry the most weight when they're paired with real project work, since employers generally want proof you can apply the skills rather than just evidence you completed a course.

How Is a Data Scientist Different From a Machine Learning Engineer?

A data scientist typically focuses on exploring data, building models, and generating insights that inform business decisions, while a machine learning engineer concentrates on taking those models and deploying them into production systems at scale. The two roles overlap significantly, and in smaller organizations, one person often handles both sets of responsibilities.

Explore Data Science Programs

Browse data science degrees and courses through Learn.org and connect directly with schools to learn more about admission requirements, program formats, and costs. Comparing a few programs side by side is the best way to find the right fit for your background, budget, and career goals.