What Can You Do With a Data Science Degree?

Key Takeaways

A data science degree opens doors to careers as a data scientist, analyst, engineer, or machine learning specialist across industries like healthcare, finance, tech, and retail.
Degree holders can pursue work in business intelligence, data engineering, AI research, or strategic analytics—making this one of the most versatile and in-demand skill sets today.
Graduates typically enter the field through entry-level roles such as data analyst or data science intern, and can quickly move into more specialized or senior positions with experience and continued learning.
Education options include bachelor’s degrees, master’s degrees, and certificates in data science, computer science, or applied analytics, all of which provide training in essential tools and skills like Python, SQL, and data visualization.
The job outlook for data science professionals remains strong, with high salary potential and growing demand across both private and public sectors.

What Can You Do With a Data Science Degree? Career Paths, Salaries, and Skills

Nearly everything today relies on big data. From healthcare to finance to public policy, the ability to extract new and exciting insights from reams of data is critical to improving our modern world. Data science is the field that makes all of this possible.

Studying data science puts you at the cutting edge of AI, advanced analytics, and everything big data. Plus, the demand for data science professionals is only growing as new emerging technologies come onto the scene. Roles like data scientists, machine learning engineers, and analysts have risen by 25% year over year.¹

In this article, we’ll help you understand the potential of a data science degree, including possible career paths, salaries, industry applications, and essential skills you’ll need to succeed.

What is Data Science?

Data science is the study of extrapolating actionable insights from raw data through algorithms, statistical models, and other computational techniques.

To get a better grasp of what data science really involves, it helps to start with a few core concepts:

Data:

Data consists of the raw numbers that are used for analysis.
Data Structures:

Data structures are tools and systems scientists use to organize and store data.
Data Analysis:

Data analysis is the process of reviewing raw numbers and drawing insight from them to identify trends and make decisions.
Machine Learning:

This subset of artificial intelligence enables systems to analyze large amounts of data and make predictions with minimal involvement from humans.²

Although some of these concepts may seem complex at first, the good news is that there are plenty of online resources to help you get started.

Data science itself can be broken down into several different specializations. For example, Data engineering focuses on building architecture and databases to store and process data.³ Computer science, on the other hand, hinges more on software development and algorithms.

As you become more familiar with data science as a whole, you may find that one or more of these fields grabs your attention, opening up a range of potential career paths depending on your interests and strengths.

Common Data Science Career Paths

People who pursue a career in data science have a number of paths to take when they’re ready to start their careers. See below for a list of common job titles for data science graduates that could help give you an idea of where you might want to go in the future.

Analyst & Intelligence Roles

Data Analyst: Data analysts interpret raw data to identify trends and develop actionable insights to make better business decisions. They need to be proficient in tools like Excel, Tableau, and SQL. Data analysts can earn salaries starting at $76,293, with more-experienced professionals earning up to $109,880.⁴
Business Analyst: While business analysts also interpret data, they pair that with a strategy to help improve processes and outcomes. They’re often on teams and work closely with data scientists and data engineers. Salaries for business analysts can range from $78,890 to $100,861 per year.⁵
Business Intelligence Analyst: Similar to data analysts, these professionals analyze operational data and communicate findings to stakeholders to help them make more informed decisions. Business intelligence analysts can earn, on average, from $75,694 to $99,864 per year.⁶

Scientist & Specialist Roles

Data Scientist: Data scientists use statistics and machine learning to translate complex datasets into clear information and actionable insights. The average annual salary for data scientists across the United States is $112,590.⁷
Machine Learning Scientist: Professionals in machine learning create the algorithms that systems use to learn and improve their outputs. Machine learning scientists can earn an average annual salary of $142,418.⁸
Deep Learning Specialist: Unlike machine learning specialists, deep learning specialists focus on advanced AI practices, like using neural networks. These advanced positions can earn between $100,000 and $180,000 annually.⁹

Engineering & Architecture Roles

Data Engineer: A data engineer designs data systems and ensures that they deliver clean and reliable data to those who need it, typically through dashboards. Salaries in the United States can average around $129,716.¹⁰
Data Architect: Architects design data storage systems and infrastructure. Focusing on long-term, scalable system design.

Leadership & Strategy

Analytics Manager: An analytics manager oversees data science teams to ensure they’re meeting an organization’s technological goals.
Director of Data Strategy: Directors have both technical expertise and leadership skills to shape data science strategy.

These roles show just how many opportunities there are for students who choose to pursue a data science degree. Next, we’ll look more closely at the industries hiring these professionals.

Industries That Hire Data Science Graduates

Data science graduates can enter a variety of industries, all of which need data expertise to help them make better decisions, increase innovation, and compete in today’s global market. Here is a quick look at some of the most common industries data scientists enter:

Healthcare:

The healthcare sector accounts for more than 30% of the world’s data volume, making data scientists indispensable to the industry.¹¹ Healthcare data scientists can analyze raw data to help medical professionals develop and optimize treatment plans. Through medical imaging and predictive modeling, they can also help improve the accuracy of diagnoses to ultimately improve patient outcomes. Disease tracking and prevention is another possible job duty of a healthcare data scientist.¹²
Finance:

Finance data scientists help set up systems that monitor for things like fraud or risk. Through algorithmic processing, data scientists can spot patterns, allowing financial organizations to operate more efficiently.
Technology:

Big tech companies like Google and Amazon rely on data scientists to use data to optimize product listings and search results. These changes can help personalize product listings for consumers. Data scientists can also identify opportunities to streamline software development processes, whether it’s for their own company or others.
Retail and eCommerce:

Using consumer behavior analytics, data scientists can deliver recommendations on the best way to catch shoppers’ attention and increase sales. Data scientists can also forecast sales and analyze consumer spending habits to enable personalized marketing materials for subsets of a company’s targeted audience.
Government and Public Policy:

Local governments use data to plan transportation infrastructure, track energy consumption, and make zoning and construction decisions. On a larger scale, data scientists can keep track of public trends, enable fraud detection, and detect possible threats to cybersecurity.
Media and Entertainment:

Streaming platforms like Hulu and Netflix depend on audience segmentation to suggest entertainment options to viewers. Data scientists also work with social media companies, where they can help with product and information dissemination.

Entry-Level Data Science Jobs

Once you’ve acquired your data science degree, how can you get your foot in the door and start growing your career? To start, recent graduates can look for the following types of positions:

Junior Data Analyst:

Analysts are required to do basic analysis, data cleaning, and visualization duties. They need to be proficient in tools like Excel, SQL, and Tableau to process raw data and create dashboards, ensuring data accuracy and clarity.
Data Science Associate or Intern:

Associates help with data mining, analytics workflows, and model development to build data pipelines. They rely on statistics and use programs like Python to perform functions like optimizing recommendation engines and analyzing customer behavior.
Machine Learning Associate:

Machine learning associates focus their attention on developing algorithms and creating AI-driven insights. They might excel at tasks like fraud detection or customer segmentation.

Recent grads can also apply for internships to gain hands-on experience with real-life data science projects. Getting experience any way you can is the fastest and most efficient way to kick-start your career.

Career Advancement Opportunities

After you’ve been working in a junior position for a little while, your next step is to pursue more advanced roles, such as senior data scientist or business intelligence analyst. To reach these levels, professionals need to gain expertise in machine learning, cloud platforms, and data visualization tools like Tableau.

To get these skills, you can enroll in certification programs, such as Google’s Data Analytics Certificate or the Data Science Council of America (DASCA)’s ABDA^TM. These certifications equip you with the knowledge and technical skills you’ll need to stay competitive in the field and advance in areas like AI or deep learning.¹³

Additionally, data science professionals can pursue master’s degrees in topics like applied data science or business analytics. These advanced studies will give you the skills you need while also giving you a chance to network with professors and other students joining the field.

Another way to build your connections is to join professional organizations and commit to regular networking opportunities. These activities can provide constant connection within the community as well as growth and training opportunities.

Data Science Academic Pathways: From Certificates to College Degrees

You know by now that there are dozens of ways to use a data science degree across a variety of industries. Next, we’ll review the educational pathways that can help you get that first role and start you on your journey to a data science career.

There are three main pathways to obtaining the education you need for a job in data science:

Bachelor’s Degree: A bachelor’s degree in data science or computer science teaches students the foundations of data science principles, such as programming, statistics, and data management. Some programs take their curriculum a step further by introducing elements of AI, machine learning, and data structures. Computer science disciplines might integrate more software engineering topics as well as analytics.
Master’s Degree: A master’s degree in applied data science or business analytics offers deeper discovery and discussion of topics like AI ethics, natural language processing (NLP), and cloud-based analytics. It also prepares graduates for leadership roles.
Certificate Programs: Those who want a faster and cheaper way to get a job in data science might consider certificate programs like Google’s Data Analytics Certificate. These accelerated programs provide certifications like Certified Data Scientist (CDS), building credibility for job applicants in fields like data analytics and intelligence.¹⁴

Regardless of the chosen academic pathway, data science education typically covers the following topics:

Data Visualization:

Learning how to translate insights into actionable strategies.
Machine Learning:

Frameworks like TensorFlow and PyTorch to learn predictive modeling.
Data Mining:

Techniques to identify patterns in large datasets.
Applied Data Science:

How to address real-world problems in industries such as healthcare, finance, or media.

The depth and focus of your training may vary depending on the courses or certification you take, but all paths aim to equip you with the practical skills and technical know-how needed to thrive in a data-driven role.

Data Science Skills and Technologies

Success as a data scientist requires the knowledge of an array of skills and technologies that you’ll use every day on the job. See below to learn more about the languages of data science.

Technical Skills

Students of data science will learn systems like Python, R, and SQL to practice manipulating data, building predictive models, and querying databases. These languages can help students learn how to clean messy datasets and develop machine-learning pipelines.

Programming skills are also highly important for data scientists. With programming knowledge, data scientists can design visualizations and even deploy solutions.

Advanced Techniques

Mastery of data practices includes learning a number of advanced techniques necessary for your specific niche or specialization. Here are a few advanced techniques you might consider working on:

Data Mining: Identifying patterns from large datasets
Data Analysis: Using statistics to interpret data
Data Visualization: Communicating data and insights through visual dashboards and other display methods
Data Structure:Storing data and ensuring easy methods of retrieval to handle large data workflows

Specialized Tools

To get the most out of their technical skills and advanced techniques, students will also need to master the specialized tools data scientists use every day.

For instance, students will need a firm grasp of tools like Tableau and Power BI as they help data scientists translate analyses into visualizations that other stakeholders can understand. By being able to visualize data intuitively, you can help steer the direction of your organization and inform its broader business strategy.

Soft Skills

As crucial as these technical skills are for data scientists, other skills, known as soft skills, are of equal importance. Soft skills go beyond technical capabilities and instead hinge on a person’s ability to communicate, collaborate, and think critically about projects. Building these skills helps set up professionals for careers in any field and any industry, as they are easily transferable.

By working on these technical, analytical, and interpersonal skills, you can form the foundation necessary for a successful data science career.

Salary, Job Outlook, and Demand for Data Scientists

Data science graduates are in high demand. With the wide rise of AI adoption and big data, data scientists are needed to organize, analyze, and interpret the mounds of data that become available every day.

Data scientists can earn average annual salaries between $95,000 and $230,000, with entry-level roles starting around $70,000.¹⁵ Senior roles that are more specialized in areas like AI or machine learning tend to earn toward the higher end of this range. The median salary for data scientists in the United States in 2023 was $108,020.¹⁶

The job outlook for data scientists remains high: The U.S. Bureau of Labor Statistics predicts a 36% growth in data scientist roles between 2023 and 2033, a total of 73,100 jobs.¹⁷ These roles will likely cover areas like business intelligence, marketing, and operations. Certifications, mastery of data science tools, and hands-on experience will give job applicants an advantage as they pursue data science roles.

Data Science Research Areas and Emerging Opportunities

The field of data science is constantly evolving, and innovations are emerging every day.

Some of these advancements involve cutting-edge research in things like deep learning, AI ethics, and NLP. Those who are proficient in these skills might face on-the-job challenges such as algorithmic bias mitigation, real-time language translations, and whether to deploy AI in fields like healthcare and finance.

Data scientists are also needed in research. At companies like Google and IBM, research data scientists study how to develop AI systems for scale, while machine learning scientists might work on developing autonomous vehicles.

Further, the field of data science is constantly changing. Today, AI is a major focus, but tomorrow, there may be a new specialization that needs eager data scientists to jump in, learn the ropes, and push the field forward.

The Future is Bright for Data Science

With a data science degree, graduates can access a vast world of learning and career opportunities that can take them from roles like data analyst and machine learning scientist to data engineer and research scientist.

As long as data is used in industries like healthcare, finance, technology, and government, data scientists will remain in high demand. Job postings requiring a data science degree have increased from 47% in 2024 to 70% in 2025, an increase of 23%.¹⁸ High salaries and job stability with big data companies make data science a worthy career path, especially for those who enjoy analytics and desire a chance to make a real difference in the world.

The first step in exploring or growing a career in data science is to look into a certificate, bachelor’s, or master’s program, depending on your goals and where you are in your career. Investing in a data science education and building practical skills through internships and real-world projects will lead students toward a lifetime of learning in this exciting and growing field.