Cybersecurity vs Data Science Career Comparison

What the Comparison Shows

Both federal occupations have strong projected growth, but the current BLS data do not support framing cybersecurity and data science as essentially identical labor markets.

BLS projects Data Scientist employment to grow 33.5% between 2024 and 2034 , compared with 28.5% for Information Security Analysts . Data Scientists also have more projected annual openings—approximately 23,400 compared with 16,000 for Information Security Analysts.

Those numbers provide useful national context, but they should not determine the career decision by themselves.

The more fundamental difference is the work.

Information Security Analysts monitor networks for security breaches, investigate incidents, check for vulnerabilities, use security protections such as firewalls and encryption, develop security standards, and help organizations improve their security practices.

Data Scientists determine which data are useful, collect and analyze data, develop and test algorithms and models, use data-visualization techniques, and make recommendations based on their analysis.

In practical terms:

What the Numbers Don’t Tell You

The federal data can compare employment, wages, projected growth, and occupational characteristics. They cannot establish that one career is universally better, more secure, more prestigious, or has a higher long-term career ceiling .

They also do not show that a master’s degree is required for either occupation. BLS lists a bachelor’s degree as the typical entry-level education for both . Education and experience requirements can vary by employer and position, which we’ll address separately in the education section.

Which Career Has the Higher Median Wage?

In the May 2025 national BLS data, Information Security Analysts have the higher median wage .

The median hourly wage is $62.11 for Information Security Analysts compared with $57.80 for Data Scientists . On an annualized basis, that is approximately $129,190 versus $120,220 , a difference of about $8,970 .

The mean annual wages are closer: $132,510 for Information Security Analysts and $126,800 for Data Scientists .

That comparison supports a narrow conclusion:

Cybersecurity includes jobs outside the Information Security Analyst occupation, and compensation within either field can vary substantially by employer, industry, location, responsibilities, experience, and specialization.

Don’t Turn Wage Percentiles Into Career Stages

BLS wage percentiles should not be interpreted as career stages. Wage percentiles describe where workers fall within an occupation’s wage distribution. For example, the 10th percentile is the wage below which 10% of workers in the occupation earn, while the 90th percentile is the wage below which 90% earn. They do not identify years of experience, seniority, or career stage.

For that reason, OMC does not label lower percentiles as entry-level salaries or upper percentiles as senior-career salaries.

What Can Affect Earnings in Either Career?

National occupational wages provide a useful benchmark, but individual compensation can vary based on factors such as:

• Industry
• Geographic location
• Employer
• Job responsibilities
• Experience
• Technical specialization
• Scope of responsibility

The job title also matters. A security engineer, penetration tester, security architect, or security executive is not automatically represented by the Information Security Analyst wage distribution. Likewise, data-science-adjacent roles can fall under different occupational classifications.

For students comparing these careers, the national BLS figures are best used as labor-market benchmarks rather than predictions of personal earnings .

Data Science Has the Faster Projected Growth Rate

BLS projects Data Scientist employment to increase from approximately 245,900 jobs in 2024 to 328,300 in 2034 , an increase of about 82,500 jobs .

That represents 33.5% projected growth , compared with 28.5% for Information Security Analysts . Data Scientists are projected to have about 23,400 openings annually , compared with approximately 16,000 for Information Security Analysts .

The difference should be kept in perspective. Both occupations rank among the fastest-growing occupations in the current BLS projections: Data Scientists rank fourth and Information Security Analysts fifth by projected percentage growth among detailed occupations.

So the supported conclusion is not simply that “data science has better job prospects.” It is:

Why BLS Expects Information Security Analyst Employment to Grow

Here we can make a stronger causal statement because BLS itself provides the explanation .

BLS connects projected demand for Information Security Analysts to factors including the frequency of cyberattacks, the need to secure new technologies, increased use of artificial intelligence and e-commerce, and organizations’ continuing need to protect computer networks and systems.

That gives us a defensible basis for saying that continued security threats and expanding technology use are contributing to projected demand. We should not , however, turn that into broader claims that cybersecurity is recession-proof, guarantees job security, or will always have stronger demand than other technology fields.

Why BLS Expects Data Scientist Employment to Grow

BLS also gives a direct explanation for its Data Scientist projection.

The agency expects demand to be supported by organizations’ increasing use of data-driven decision-making , along with the growing volume of available data and the ways organizations can use that information.

BLS states that organizations are expected to need Data Scientists to analyze large amounts of information and use the findings to support decisions, improve business processes, develop products, and improve marketing.

That supports describing data science as a rapidly growing occupation tied to expanding organizational use of data. It does not establish that every data-related role will grow at the Data Scientist rate.

Growth Rate vs. Annual Openings

Projected growth and projected annual openings measure different things.

Employment growth estimates how much the total number of jobs in an occupation is expected to change over the projection period.

Annual openings include opportunities created by growth as well as openings created when workers transfer to other occupations or leave the labor force.

For this comparison, Data Scientists lead on both measures:

• 33.5% vs. 28.5% projected employment growth.
• 82,500 vs. 52,100 projected jobs added.
• 23,400 vs. 16,000 projected annual openings.

Those differences provide useful national labor-market context, but they do not establish which career will be easier for an individual to enter. Hiring requirements, prior experience, technical skills, location, industry, and employer needs can all affect an individual’s opportunities.

What the Outlook Means for Your Decision

The current federal outlook is positive for both occupations, so labor-market growth alone provides little reason to rule out either career .

The more useful distinction is what is driving you toward the field:

• Cybersecurity may be the stronger direction if you want to protect systems and information, investigate security problems, identify vulnerabilities, and reduce technology risk.
• Data science may be the stronger direction if you want to analyze large datasets, use statistics and programming, develop models, and turn data into predictions or recommendations.

The 5-percentage-point difference in projected growth is real, but it is less important to the career decision than whether you want to spend your working life primarily defending technology or extracting knowledge from data .

What Information Security Analysts Do

BLS describes Information Security Analysts as professionals who plan and carry out security measures to protect an organization’s computer networks and systems.

Their responsibilities can include:

• Monitoring networks for security breaches and investigating incidents when they occur.
• Using and maintaining security protections such as firewalls and data-encryption programs.
• Checking computer systems for vulnerabilities.
• Researching security trends and emerging threats.
• Developing security standards and recommended practices.
• Helping develop disaster-recovery plans.
• Preparing reports documenting security breaches and the damage they caused.
• Recommending security improvements to management or technology teams.

The work can combine monitoring, investigation, prevention, technical implementation, documentation, and communication .

Cybersecurity as a field is broader than the Information Security Analyst occupation. Roles such as penetration tester, security engineer, security architect, digital forensics specialist, and other security positions may emphasize different responsibilities. The BLS occupation therefore provides a consistent federal benchmark without defining every cybersecurity job.

What Data Scientists Do

BLS describes Data Scientists as professionals who use analytical tools and techniques to extract meaningful insights from data.

Their responsibilities can include:

• Determining which data are useful for a particular project or question.
• Collecting, categorizing, and analyzing data.
• Creating, validating, testing, and updating algorithms and models.
• Using data-visualization tools to communicate findings.
• Making business recommendations based on analytical results.

Data Scientists may work with statistics, programming, databases, algorithms, models, visualization, and machine learning , although the exact mix depends on the employer and position.

The occupation can also vary by specialization. Some Data Scientists focus more heavily on statistical analysis and modeling, while others work closer to machine learning, business analytics, product development, or research.

Which Career Requires More Programming?

Data science generally places more consistent emphasis on programming , but programming requirements vary within both fields.

Data Scientists commonly use programming and analytical tools to manipulate data, develop algorithms, test models, and perform statistical analysis.

Cybersecurity professionals may use scripting or programming for automation, security testing, detection, analysis, or other technical tasks, but the amount required varies substantially by role. Some security positions are highly code-intensive; others emphasize networks, systems, monitoring, risk, incident response, or security operations.

For that reason, it would be too broad to say that cybersecurity requires little programming or that every Data Scientist spends most of the day writing code.

Which Career Requires More Math and Statistics?

Data science generally has the stronger mathematics and statistics emphasis.

BLS identifies mathematics as an important part of Data Scientist preparation and describes the occupation as using statistical and analytical techniques to analyze data and develop models.

Cybersecurity also requires quantitative and analytical reasoning, but advanced statistics and mathematical modeling are not universal requirements across security roles.

Students who particularly enjoy statistics, probability, modeling, and quantitative analysis may therefore find the technical foundation of data science more aligned with their interests.

Which Career Is More Reactive?

Some cybersecurity work can become time-sensitive when a security incident or potential breach occurs . Information Security Analysts may need to investigate incidents, assess vulnerabilities, and respond to security problems.

That does not mean cybersecurity professionals spend all of their time reacting to attacks. Preventive work—including monitoring, security planning, vulnerability assessment, implementing protections, and developing standards—is also an important part of the occupation.

Data-science work is generally organized more around analytical questions, datasets, models, experiments, and projects than security incidents, although deadlines and operational demands vary by employer.

The distinction is therefore better framed as:

Where the Careers Overlap

Despite their different objectives, both careers rely on several transferable capabilities:

• Analytical problem-solving
• Working with complex technical information
• Programming or scripting in many roles
• Identifying patterns and anomalies
• Communicating technical findings
• Continuous learning as technologies change
• Translating technical analysis into recommendations

There can also be direct overlap. Security teams increasingly use data analysis and automated analytical methods, while data-intensive systems create security and privacy considerations.

However, that overlap does not make cybersecurity and data science interchangeable careers. The primary question remains whether you want to use technical skills mainly to protect systems and information or to analyze data and develop analytical models.

Education for Cybersecurity Careers

BLS lists a bachelor’s degree as the typical entry-level education for Information Security Analysts and reports that workers commonly need related work experience.

Relevant undergraduate preparation can include computer science, information technology, cybersecurity, engineering, or other computing-related fields. The appropriate background depends partly on the type of security work involved.

Cybersecurity also differs from data science in the role professional credentials can play. BLS notes that employers may prefer candidates who hold information-security certifications.

That does not mean a certification is universally required or that certifications are more valuable than academic degrees. Certification requirements vary by employer, role, technology environment, and level of responsibility.

When Might a Master’s in Cybersecurity Make Sense?

A master’s degree is not required simply to enter cybersecurity .

Graduate study may be worth considering when you want deeper education in areas such as:

• Information and network security
• Security architecture
• Digital forensics
• Cybersecurity management
• Risk and security policy
• Advanced technical security topics

The value of a master’s depends on the role you’re targeting and the capabilities you already have.

For someone who needs a specific technical skill or professional credential, targeted training or certification may address the gap more directly than a graduate degree. Someone seeking broader or deeper study across security disciplines may have a different reason for considering graduate education.

The key point is that degree and certification decisions should follow the target role , rather than assuming that a master’s degree is necessary for advancement across cybersecurity.

Students considering graduate study can explore OMC’s online master’s in cybersecurity programs .

Education for Data Science Careers

BLS also identifies a bachelor’s degree as the typical entry-level education for Data Scientists , commonly in mathematics, statistics, computer science, or a related field.

However, BLS adds an important qualification: some employers require or prefer candidates to have a master’s or doctoral degree .

That makes graduate education relevant to the data-science discussion, but it does not establish that a master’s degree is generally required to become a Data Scientist.

Data-science education commonly develops capabilities across areas such as:

• Statistics and probability
• Programming
• Data management
• Algorithms
• Statistical modeling
• Machine learning
• Data visualization
• Quantitative problem-solving

The balance varies substantially across programs and employers.

When Might a Master’s in Data Science Make Sense?

Graduate study may be worth considering when the positions you’re targeting require deeper preparation than your current background provides, particularly in statistics, mathematics, programming, modeling, or machine learning .

It may also be relevant when specific employers or positions explicitly prefer graduate education.

That is different from claiming that a master’s degree automatically improves advancement or compensation. The BLS evidence supports saying that some Data Scientist employers require or prefer advanced degrees ; it does not establish a universal career advantage from earning one.

Students considering this path can explore OMC’s online master’s in data science programs .

Cybersecurity Certifications vs. Graduate Degrees

Certifications and graduate degrees serve different purposes, so they should not be treated as interchangeable credentials.

A professional certification generally validates knowledge or capabilities associated with a particular security domain, technology, or professional standard. A graduate degree provides a broader academic program of study across a defined discipline.

Which one is more useful depends on the career objective.

For example, someone trying to demonstrate competence in a particular security area may have a different educational need from someone seeking advanced academic study in cybersecurity architecture, management, policy, or technical security.

Rather than asking whether a degree or certification is universally “better,” compare:

• Credentials requested in target job postings
• Skills you already possess
• Skills or knowledge you need to develop
• Experience requirements
• Cost and time
• How closely the curriculum or credential aligns with the work you want

Do You Need a Master’s Degree for Either Career?

Not universally.

BLS identifies a bachelor’s degree as the typical entry-level education for both occupations.

The distinction is that BLS specifically notes that some Data Scientist employers require or prefer a master’s or doctoral degree , while its Information Security Analyst profile emphasizes related experience and notes that employers may prefer professional certification.

That gives students useful context, but it still doesn’t create a simple rule:

Cybersecurity: Don’t assume you need a master’s. Evaluate the experience, technical skills, and credentials required for the specific security roles you want.

Data Science: Don’t assume you need a master’s, but check target positions carefully because some employers may require or prefer graduate education.

In either case, graduate school makes the most sense when the curriculum addresses a meaningful gap between your current preparation and the work you want to pursue .

How a Cybersecurity Career Can Develop

Cybersecurity professionals can develop deeper expertise in a particular security domain or broaden their responsibility across an organization’s security operations.

Potential directions include areas such as:

• Incident detection and response
• Vulnerability assessment
• Penetration testing
• Security engineering
• Cloud security
• Digital forensics
• Security architecture
• Security risk and governance
• Security program or team leadership

Not every Information Security Analyst will move into these roles, and some require capabilities that extend beyond the BLS Information Security Analyst occupation used as the benchmark on this page.

A technically oriented professional may deepen expertise in security engineering, architecture, testing, or incident response. Someone more interested in organizational security may move toward risk, governance, security programs, or management.

How a Data Science Career Can Develop

Data Scientists can likewise deepen their technical specialization or move toward broader responsibility for analytical work.

Potential directions include:

• Advanced statistical modeling
• Machine learning
• Specialized applied data science
• Experimentation and quantitative analysis
• Data or analytics strategy
• Technical leadership
• Data science or analytics management

Career direction can also depend heavily on the domain. A Data Scientist working in healthcare may develop different expertise from one working in finance, technology, marketing, scientific research, or another industry.

Some professionals may move toward more specialized machine-learning or AI-related work, but Data Scientist should not be presented as a guaranteed pathway into machine-learning engineering or AI roles . Those positions can require different software-engineering, mathematical, systems, or research capabilities.

How the Careers Can Diverge Over Time

The biggest long-term difference is generally what expertise compounds as experience grows .

In cybersecurity, professionals can accumulate deeper knowledge of threats, vulnerabilities, security controls, systems, networks, incident response, architecture, and organizational risk .

In data science, professionals can accumulate deeper knowledge of statistics, modeling, algorithms, machine learning, experimentation, data systems, and applying quantitative methods within a particular domain .

That can produce substantially different career identities even though both started as technical occupations.

A cybersecurity professional may increasingly be asked:

How should this organization protect its technology, information, and operations from security risk?

A data scientist may increasingly be asked:

How can this organization use its data and analytical methods to understand, predict, or improve outcomes?

That is a more defensible way to describe long-term divergence than claiming one career has a higher “ceiling.”

Does Cybersecurity Lead to CISO Roles?

Cybersecurity experience can be relevant to security leadership positions, including roles such as Chief Information Security Officer (CISO) , but there is no standardized progression from Information Security Analyst to CISO.

Executive security positions can require substantial experience in areas beyond technical security, including organizational leadership, risk management, governance, budgeting, communication, and business strategy.

Does Data Science Lead to AI and Machine Learning Careers?

Data science can provide relevant preparation for work involving machine learning and other advanced analytical methods because the field already draws on statistics, programming, algorithms, and modeling.

However, data science, machine-learning engineering, and AI research are not interchangeable career categories .

A transition into a more specialized role may require additional capabilities in areas such as software engineering, advanced mathematics, machine learning systems, or research methods, depending on the position.

Which Career Has the Higher Long-Term Ceiling?

The available federal occupational data do not establish a winner.

BLS wage distributions tell us what workers in the two occupations currently earn. Employment projections tell us how those occupations are expected to change. Neither dataset tells us the maximum compensation or seniority a cybersecurity or data-science professional can reach over a 10- or 15-year career.

Both fields can lead to:

• Deep technical specialization
• Team or organizational leadership
• Broader strategic responsibility
• Movement into adjacent technical occupations

The better long-term path depends on which expertise you want to deepen .

Choose the cybersecurity direction if you want your experience to compound around security, systems, threats, vulnerabilities, and technology risk .

Choose the data-science direction if you want your experience to compound around data, statistics, modeling, machine learning, and quantitative problem-solving .

Cybersecurity May Fit You Better If…

Cybersecurity may be the stronger fit if you are most interested in how technology can be protected, where systems are vulnerable, and how organizations can reduce security risk .

Consider the cybersecurity direction if you:

• Like understanding computer systems and networks.
• Are interested in security threats, vulnerabilities, and defensive controls.
• Enjoy investigating why something happened and determining how to prevent it from happening again.
• Want to work on protecting systems, applications, networks, or information.
• Are comfortable with work that can sometimes become time-sensitive when security incidents occur.
• Want to develop deeper expertise in areas such as security engineering, incident response, cloud security, penetration testing, digital forensics, or security architecture.

Cybersecurity is broader than the Information Security Analyst occupation used as the federal benchmark on this page, so the exact technical requirements can vary substantially across security roles.

Data Science May Fit You Better If…

Data science may be the stronger fit if you are most interested in using quantitative methods to understand data, identify patterns, develop models, and solve analytical problems .

Consider the data-science direction if you:

• Enjoy mathematics, statistics, and quantitative reasoning.
• Want programming to be an important part of your analytical work.
• Like working with large or complex datasets.
• Are interested in developing and evaluating algorithms or models.
• Enjoy investigating patterns, relationships, and anomalies in data.
• Want to use analysis to make predictions, recommendations, or support decisions.
• Are interested in developing deeper expertise in statistical modeling, machine learning, experimentation, or other advanced analytical methods.

Data science can vary by employer and domain. Some positions emphasize modeling and machine learning, while others place greater weight on statistics, experimentation, analytics, or communicating findings.

Choose Based on the Work, Not Just the Growth Rate

The current federal projections favor Data Scientists on several headline labor-market measures. BLS projects 33.5% employment growth and about 23,400 annual openings for Data Scientists , compared with 28.5% growth and about 16,000 annual openings for Information Security Analysts from 2024 to 2034.

At the same time, the current May 2025 BLS wage data show the Information Security Analyst benchmark with the higher national median wage .

Those differences are useful context, but they do not produce a universal winner.

A 5-percentage-point difference in projected growth should not push someone who loves systems, networks, and security into data science. Likewise, the higher Information Security Analyst median wage should not push someone who prefers statistics, modeling, and machine learning into cybersecurity.

The more useful distinction is:

What If You Like Both?

The two fields are not completely isolated from each other.

Cybersecurity work can involve analyzing large amounts of security data, identifying unusual patterns, automating detection, and using analytical methods to investigate threats. Data-science techniques can therefore be relevant to some security problems.

But liking both fields does not require finding a hybrid career.

First determine which problem you want to own:

• “How do we detect, prevent, or respond to a security threat?” points more naturally toward cybersecurity.
• “What can these data tell us, and can we build a model that helps us understand or predict an outcome?” points more naturally toward data science.

Specialization can come later.

Still Unsure? Ask Yourself These Six Questions

• Would I rather investigate a compromised system or investigate an unexpected pattern in a dataset?
• Am I more interested in networks, systems, vulnerabilities, and security controls—or statistics, algorithms, models, and machine learning?
• Would I rather build something that helps detect or prevent a threat, or something that helps predict or explain an outcome?
• How much do I enjoy mathematics and statistics? If you want them to be central to your work, data science generally provides the more direct path.
• How do I feel about incident-driven work? Some cybersecurity positions can require responding to security events, while data-science work more commonly revolves around analytical projects, models, experiments, and business or research questions.
• Which expertise would I rather deepen for the next several years: security and technology risk, or data and quantitative modeling?

If most of your answers consistently fall on one side, that is a stronger career-fit signal than choosing based solely on salary or projected growth.

Quick Decision

Lean Cybersecurity if: You are drawn to systems, networks, threats, vulnerabilities, defensive technology, investigation, and protecting information.

Lean Data Science if: You are drawn to statistics, programming, datasets, algorithms, modeling, machine learning, and extracting insights from information.

Either could fit if: You enjoy technical problem-solving, programming, continuous learning, and working with complex information—but you need to decide whether you would rather apply those abilities primarily to security problems or data problems .

Degree Guides for Cybersecurity

If you are leaning toward cybersecurity, consider whether you want your graduate education to specialize directly in security or provide broader computing preparation.

• Online Master’s in Cybersecurity — the most directly aligned option for graduate study centered on information and technology security.
• Online Master’s in Computer Science — a broader computing path that may make sense if your interests extend beyond cybersecurity into areas such as software, systems, algorithms, or other computer-science topics.

A master’s degree is not a universal requirement for cybersecurity careers . Before enrolling, compare the curriculum with the skills, experience, and credentials requested for the particular security positions you want.

Degree Guides for Data Science

If you are leaning toward the data side, the main distinction is how deeply you want to move into statistics, modeling, programming, and advanced analytical methods.

• Online Master’s in Data Science — the most directly aligned graduate path for deeper study in statistics, programming, modeling, machine learning, and data-science methods.
• Online Master’s in Data Analytics — may fit better if your interests center more on analyzing, managing, visualizing, and communicating data than on advanced modeling or machine learning.
• Online Master’s in Computer Science — may be worth considering if you want broader computing preparation alongside interests in algorithms, software, or technically advanced data work.

BLS notes that some Data Scientist employers require or prefer a master’s or doctoral degree , but graduate education is not an occupation-wide requirement. The appropriate path depends on your existing preparation and the requirements of the positions you are targeting.

Do You Need Graduate School?

Not necessarily.

Before enrolling in a master’s program, compare your current preparation with the requirements of the jobs you actually want.

Graduate study may make sense when you need broader or deeper preparation in areas such as:

• Cybersecurity and information assurance
• Computer systems and networks
• Statistics and probability
• Programming and algorithms
• Data science and machine learning
• Advanced analytical methods

If your primary gap is narrower—for example, a particular security technology, programming language, analytical tool, or professional credential—a full master’s degree may be more education and expense than you need.

The decision should depend on the target career, skills gap, curriculum, cost, and expected value of the degree , rather than an assumption that graduate education automatically improves advancement or compensation.

Explore Related Career and Degree Comparisons

If you’re still deciding among adjacent technology careers, these comparisons can help narrow the direction:

• Software Engineer vs. Data Scientist — compare building software systems with using data, statistics, and modeling to solve analytical problems.
• Data Scientist vs. Data Analyst — compare data science with a more analysis-focused data career.

You can also explore all OMC career and degree comparisons to evaluate other paths side by side.