- Start Here
- How OMC Compared Data Scientists and Data Analysts
- Data Scientist vs Data Analyst at a Glance
- What Each Role Actually Does
- Salary Comparison: Data Scientist vs Data Analyst
- Job Growth and Labor Market Outlook
- Skills and Tools Compared
- Education and Degree Pathways
- Career Trajectory: Where Each Path Can Lead
- OMC Analysis: The Careers Compound Different Types of Expertise
- Industries and Work Settings
- Which Career Fits You?
- Data Scientist vs Data Analyst: Final Comparison
- Frequently Asked Questions about Data Scientist Vs. Data Analyst Careers
- Edited By Jeevan D'Souza
- Expert Reviewed By Soumen Chatterjee
Start Here
Data scientists and data analysts both turn data into useful information, but the roles are not interchangeable. Data analysts typically focus more on examining existing data, identifying trends, building reports and visualizations, and helping organizations answer defined business questions. Data scientists generally work more deeply with statistics, programming, algorithms, predictive models, and machine learning to investigate complex problems and develop data-driven solutions.
There is also an important limitation to understand before comparing their salaries and job outlook.
The U.S. Bureau of Labor Statistics defines Data Scientists as a standalone occupation (SOC 15-2051) . “Data Analyst,” however, does not have an equivalent standalone BLS occupational classification. Jobs carrying the Data Analyst title can be classified differently depending on their responsibilities and industry.
That means OMC can use consistent federal data to report national wages, employment, projected growth, and typical entry-level education for Data Scientists. We cannot responsibly present a single BLS salary or growth rate for all Data Analysts as though the federal government measures them as one occupation.
Rather than force an artificial comparison, this page separates the evidence:
- Data Scientist labor-market figures use the defined BLS Data Scientist occupation where applicable.
- Data Analyst evidence is identified by its actual source and is not presented as directly comparable BLS occupational data when it is not.
- OMC career analysis compares responsibilities, technical emphasis, education, career direction, and fit while clearly distinguishing interpretation from federal findings.
This creates a less symmetrical comparison than simply placing two BLS occupations side by side, but it is a more accurate representation of the available evidence.
The practical career distinction is still clear. Data analysis generally centers on understanding and communicating what the data show, while data science more often extends into statistical modeling, algorithms, prediction, and machine learning. The boundary can overlap substantially by employer, so the job title alone should never determine how you evaluate a position.
This page is part of OMC’s career and degree comparison library , where we compare related career and education paths using consistent evidence and explicitly identify where the available data have limitations.
How OMC Compared Data Scientists and Data Analysts
This comparison uses federal occupational data where the two careers can be measured consistently and does not create equivalent statistics where the underlying federal classifications do not support them .
For Data Scientists, OMC uses the federal Data Scientists occupation (SOC 15-2051) . Current wage figures come from the Bureau of Labor Statistics Occupational Employment and Wage Statistics (OEWS), May 2025 , while employment and outlook figures use the BLS 2024–2034 Employment Projections . BLS occupational information also informs descriptions of Data Scientist responsibilities and typical entry-level education.
Data Analyst requires a different approach. “Data Analyst” is a widely used job title, but it does not correspond to one standalone BLS occupation representing everyone employed under that title. Depending on their responsibilities, workers called Data Analysts may fall within different federal occupational classifications.
OMC therefore uses three evidence rules throughout this comparison:
| Evidence Type | How OMC Uses It |
|---|---|
| Federal occupational data | Used directly when a statistic or characteristic applies to the defined Data Scientist occupation |
| Data Analyst career evidence | Used to describe the range of responsibilities, skills, and career directions associated with Data Analyst roles without treating them as one federally measured occupation |
| OMC analysis | Used to compare the careers and explain what the evidence means for someone choosing between them |
This distinction matters most for salary, employment, and job-growth figures . OMC does not select a related occupation, rename it “Data Analyst,” and present its statistics as though they represent every Data Analyst job. We also do not average several occupations together to manufacture a national Data Analyst benchmark.
The same standard applies to interpretation. A BLS statistic is presented as a federal finding . A conclusion OMC draws from multiple pieces of evidence is presented as OMC analysis , not as a BLS conclusion.
The result is intentionally asymmetric in places. Where comparable data exist, we compare them. Where they do not, we show the limitation rather than create a more precise-looking answer than the evidence supports .
OMC Methodology Principle
Comparable evidence should be compared. Non-comparable evidence should be identified—not forced into an artificial side-by-side statistic.
Data Scientist vs Data Analyst at a Glance
Data scientists and data analysts both work with data, but the roles typically differ in the depth of technical analysis, the kinds of questions they address, and the outputs they produce .
There is also an important difference in the available labor-market data. Data Scientist is a defined federal occupation (SOC 15-2051), while Data Analyst is a job title that can map to multiple occupational categories depending on the work performed. OMC therefore does not assign Data Analysts a single BLS salary, employment total, or growth rate that the federal data do not actually provide.
| Dimension | Data Scientist | Data Analyst |
|---|---|---|
| Federal occupational classification | Data Scientists — SOC 15-2051 | No single standalone BLS occupation represents all Data Analyst jobs |
| Primary focus | Use statistical, computational, and analytical methods to extract insights, develop models, and solve complex problems with data | Analyze and interpret data to answer questions, identify trends, monitor performance, and support organizational decisions |
| Typical outputs | Statistical analyses, predictive models, algorithms, experiments, visualizations, and analytical recommendations | Reports, dashboards, visualizations, recurring analyses, trend findings, and decision-support insights |
| Technical emphasis | Statistics, programming, modeling, algorithms, machine learning, and data analysis | Data querying, cleaning, analysis, visualization, reporting, and business or domain interpretation |
| May 2025 national median wage | $120,230 | No single BLS Data Analyst wage |
| 2024 employment | 245,900 | No single comparable BLS employment total |
| Projected growth, 2024–2034 | 33.5% | No single comparable BLS projection |
| Projected annual openings | 23,400 | No single comparable BLS projection |
| BLS typical entry-level education | Bachelor’s degree | No single occupation-wide BLS requirement for the Data Analyst job title |
| Career emphasis | Deeper quantitative and computational modeling | Applying analysis to defined organizational, operational, or business questions |
Sources: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025; BLS Employment Projections, 2024–2034; O NET occupational classifications.*
What Stands Out
The cleanest difference is not salary because the available federal data do not provide an equivalent national Data Analyst wage benchmark.
The stronger comparison is the nature of the work.
Data scientists generally go deeper into statistical modeling, algorithms, programming, and machine learning. Data analysts generally focus more on querying, organizing, interpreting, visualizing, and communicating data to answer defined questions and support decisions.
The boundary is not absolute. A technically advanced Data Analyst may use Python, statistics, predictive techniques, or machine learning, while some Data Scientist positions may include substantial reporting and visualization work. Employers also use these titles differently.
That variation is precisely why OMC does not treat the two titles as perfectly standardized occupational categories.
What Each Role Actually Does
Data scientists and data analysts both turn raw information into something organizations can use, but they typically operate at different points in the analytical process.
Data analysts generally focus on examining data to answer defined questions, identify patterns, monitor performance, and communicate findings. Data scientists typically extend further into statistical modeling, algorithms, prediction, experimentation, and machine learning.
These are useful distinctions, not rigid boundaries. Employers use both titles differently, and the responsibilities of an advanced Data Analyst can overlap substantially with those of a Data Scientist.
What Data Scientists Do
The Data Scientist side can be anchored directly to a defined federal occupation. BLS describes Data Scientists as professionals who use analytical tools and techniques to extract meaningful insights from data.
Their work can include:
- Determining which data are useful for a project or question.
- Collecting, categorizing, and analyzing data.
- Creating, validating, testing, and updating algorithms and models.
- Using data-visualization software to present findings.
- Making recommendations based on data analysis.
- Using machine learning and other analytical techniques depending on the role and problem.
The occupation therefore combines data analysis with deeper statistical and computational work . Depending on the position, a Data Scientist may spend substantial time preparing data, writing code, developing models, evaluating results, communicating findings, or working with other technical and business teams.
What Data Analysts Do
“Data Analyst” is less standardized as an occupational category, so there is no single federal job description that applies to every position carrying the title.
Across organizations, Data Analysts commonly work with existing data to help answer defined business, operational, financial, product, marketing, or other organizational questions. Their work can include:
- Querying and extracting data.
- Cleaning and organizing datasets.
- Examining trends, relationships, and performance measures.
- Creating reports, dashboards, and visualizations.
- Tracking metrics and key performance indicators.
- Investigating unexpected changes or patterns.
- Communicating findings to managers, clients, or other stakeholders.
- Helping translate organizational questions into measurable analyses.
The exact technical depth varies substantially. Some Data Analyst positions emphasize SQL, spreadsheets, dashboards, and reporting , while more technical positions may also require Python or R, statistical analysis, experimentation, data modeling, or other advanced analytical methods .
For that reason, OMC does not define Data Analyst as simply a “less technical Data Scientist.” The better distinction is that Data Analyst roles generally center more heavily on using data to answer defined questions and support decisions, while Data Scientist roles more consistently extend into advanced statistical, computational, and predictive methods.
How the Work Differs Side by Side
| Work Dimension | Data Scientist | Data Analyst |
|---|---|---|
| Primary objective | Extract insights and develop analytical or predictive solutions from data | Analyze and interpret data to answer questions and support decisions |
| Typical questions | What can we predict, model, classify, optimize, or discover from the data? | What happened, what is changing, why might it be happening, and what should stakeholders know? |
| Data preparation | Common | Common |
| SQL and data querying | Common, depending on role | Common |
| Statistical analysis | Generally a major component | Varies from basic/descriptive to advanced analysis |
| Programming | Commonly important | Varies substantially by position |
| Predictive modeling | Common in many roles | May occur, but is not universal |
| Machine learning | Common in many Data Scientist positions | Used in some advanced analyst positions but not defining across the title |
| Dashboards and recurring reports | May be part of the work | Common in many analyst positions |
| Data visualization | Common | Common |
| Stakeholder communication | Important | Important |
| Primary output | Models, analyses, algorithms, experiments, predictions, visualizations, and recommendations | Analyses, dashboards, reports, visualizations, metrics, and recommendations |
Where Data Scientist and Data Analyst Roles Overlap
The boundary between the careers is not fixed.
Both may:
- Query and clean data.
- Use SQL.
- Work with Python or R.
- Perform statistical analysis.
- Build visualizations.
- Investigate patterns and anomalies.
- Communicate findings.
- Work with databases and analytical platforms.
- Translate organizational problems into questions that can be investigated with data.
A Data Analyst working on experimentation, advanced statistics, or predictive analytics may perform work that resembles data science. A Data Scientist may also spend substantial time cleaning data, producing visualizations, or performing exploratory analysis that could appear in an analyst role.
Job titles therefore provide only part of the picture.
The Better Way to Compare Two Job Offers
If you are evaluating actual Data Analyst and Data Scientist positions, compare the responsibilities rather than relying on the titles .
Look at:
- What percentage of the work involves reporting versus modeling.
- Whether the role requires statistical modeling or machine learning.
- Which programming languages and analytical tools are required.
- Whether you will primarily answer defined stakeholder questions or develop new analytical approaches.
- How much responsibility you will have for experimentation, prediction, or model development.
- Who consumes your work and how your analysis influences decisions.
Two jobs called “Data Analyst” can require very different technical capabilities. The same is true of Data Scientist positions.
For career planning, the useful distinction is therefore NOT :
Data Analyst = basic data work Data Scientist = advanced data work
But the distinction is:
Data analysis generally emphasizes using data to understand and communicate what is happening. Data science more consistently extends that analytical foundation into statistical modeling, algorithms, prediction, and machine learning.
Salary Comparison: Data Scientist vs Data Analyst
Because Data Analyst does not have a standalone BLS occupation, there is no directly comparable federal salary benchmark for the two careers. OMC therefore reports the current federal wage distribution for Data Scientists without manufacturing an equivalent Data Analyst figure.
Current Federal Wage Benchmark for Data Scientists
The latest BLS Occupational Employment and Wage Statistics data provide the following national wage benchmarks for Data Scientists:
| Wage Measure | Data Scientists |
|---|---|
| Median annual wage | $120,230 |
| Mean annual wage | $126,800 |
| 10th percentile annual wage | $63,650 |
| 25th percentile annual wage | $84,910 |
| 75th percentile annual wage | $153,550 |
| 90th percentile annual wage | $194,410 |
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, Data Scientists (SOC 15-2051).
These figures describe the national wage distribution for workers classified as Data Scientists. They are not starting, mid-career, or senior-level salaries .
For example, the 10th percentile means 10% of workers in the occupation earn below that amount. It does not mean that workers at the beginning of their careers earn that amount.
So, Do Data Scientists Earn More Than Data Analysts?
The federal occupational data alone cannot establish a clean national salary gap between the two job titles .
What we can establish is that Data Scientists are a defined BLS occupation with a May 2025 national median annual wage of $120,230 .
We can also reasonably distinguish the work: Data Scientist positions more consistently extend into advanced statistics, programming, algorithms, modeling, and machine learning, while Data Analyst positions span a wider range of analytical responsibilities and technical depth.
But those occupational differences do not give us permission to invent a national Data Analyst wage.
If a private salary source reports compensation for jobs carrying the Data Analyst title, that can provide additional job-market context , but it should be identified as employer- or market-reported salary information rather than treated as an equivalent federal occupational statistic.
How to Compare Salary for Your Own Career Decision
If compensation is important to your choice, the most useful comparison is more specific than Data Scientist vs Data Analyst nationally .
Compare actual positions based on:
- Location.
- Industry.
- Required technical skills.
- Experience requirements.
- Statistical and programming depth.
- Whether machine learning or predictive modeling is required.
- Scope of responsibility.
- Employer.
- Total compensation, not salary alone.
For Data Scientist roles, the BLS national wage distribution provides a useful external benchmark.
For Data Analyst roles, actual job responsibilities and the labor market relevant to the positions you are targeting matter more than a single national salary number attached to a broad job title .
OMC Finding
Data Scientists have a clear federal wage benchmark: a $120,230 national median annual wage in May 2025. Data Analysts do not have an equivalent standalone BLS occupation, so the available federal evidence does not support presenting a precise national Data Scientist-vs-Data Analyst salary gap.
Job Growth and Labor Market Outlook
Data Scientist has a clear federal employment outlook. Data Analyst does not have an equivalent standalone BLS occupation, so the same limitation that applies to salary also applies to national employment and growth comparisons.
For Data Scientists (SOC 15-2051) , the current BLS Employment Projections cover 2024–2034 .
| BLS Employment Measure | Data Scientists |
|---|---|
| 2024 employment | 245,900 |
| Projected 2034 employment | 328,300 |
| Projected employment change | +82,500 |
| Projected growth, 2024–2034 | 33.5% |
| Projected annual openings | 23,400 |
Source: U.S. Bureau of Labor Statistics, Employment Projections 2024–2034, Data Scientists (SOC 15-2051).
BLS projects Data Scientist employment to increase by approximately 82,500 jobs over the decade , representing 33.5% growth . For context, BLS projects total U.S. employment across all occupations to grow about 3.1% during the same period.
Data Science therefore has a strong federal employment outlook. But those figures should not be extended to Data Analyst jobs simply because both careers involve working with data.
Why BLS Expects Data Scientist Employment to Grow
BLS provides context for the Data Scientist projection, allowing us to go beyond simply reporting the growth rate.
The agency expects organizations to continue needing Data Scientists as the volume of available data increases and organizations rely more heavily on data-driven decision-making .
BLS describes demand for Data Scientists to analyze information and use their findings to support decisions, improve business processes, develop products, and improve marketing.
That provides a defensible basis for connecting projected Data Scientist growth to organizations’ expanding use of data and analytical methods.
It does not establish that every data-related occupation—or every job carrying an analyst title—will grow at the same rate.
Growth Rate and Annual Openings Measure Different Things
Projected employment growth and projected annual openings are related but different measures.
Employment growth estimates the change in the total number of jobs in an occupation over the projection period.
Annual openings include opportunities created by employment growth as well as openings expected when workers transfer to other occupations or leave the labor force.
For Data Scientists, BLS projects:
- Approximately 82,500 additional jobs between 2024 and 2034.
- 33.5% employment growth over the decade.
- Approximately 23,400 openings per year , on average.
Those measures collectively show a strong national outlook for the defined Data Scientist occupation. They do not predict how easy it will be for an individual applicant to get hired.
Does Data Science Have Better Job Prospects Than Data Analysis?
The available federal data do not support a clean national winner.
We can say that Data Scientists have a strong measured outlook: 33.5% projected employment growth and approximately 23,400 annual openings from 2024 to 2034 .
We cannot compare those figures with an equivalent national Data Analyst projection because no single federal occupation represents all Data Analyst jobs.
That means statements such as “Data Science is growing faster than Data Analysis” would require a consistently defined Data Analyst labor market that the federal occupational system does not provide.
For someone choosing between the careers, the more useful question is whether the type of work matches the direction you want to develop.
- Data science may align more closely if you want your work to deepen around statistics, programming, algorithms, predictive modeling, and machine learning.
- Data analysis may align more closely if you want to use data to investigate defined questions, monitor performance, identify patterns, build reports or visualizations, and communicate findings to decision-makers.
Labor-market conditions still matter. But when one side of the comparison lacks a directly equivalent federal occupation, inventing symmetry would be less useful than acknowledging the limitation and focusing the decision on evidence we can actually compare.
OMC Finding
BLS projects strong growth for Data Scientists: 33.5% from 2024 to 2034, with approximately 23,400 annual openings. There is no equivalent standalone BLS Data Analyst occupation, so those figures cannot support a direct national growth-rate comparison between the two job titles.
Skills and Tools Compared
Data Scientists and Data Analysts share many foundational skills, but the depth and purpose of those skills often differ .
Both may work with databases, SQL, spreadsheets, programming languages, visualization tools, and statistical methods. Data Scientist roles, however, more consistently extend into statistical modeling, algorithms, machine learning, and computational methods , while Data Analyst roles more commonly emphasize querying, analysis, reporting, visualization, and communicating findings to stakeholders .
Because Data Analyst is not one standardized federal occupation, the comparison below is OMC career-role synthesis , not a BLS skills ranking.
| Skill Area | Data Scientist Emphasis | Data Analyst Emphasis |
|---|---|---|
| Data querying / SQL | Common | Common |
| Data cleaning and preparation | Core | Core |
| Spreadsheets | Useful; importance varies by role | Common in many roles |
| Programming | Commonly important | Varies substantially by role |
| Statistics and probability | Core | Important; depth varies |
| Exploratory data analysis | Core | Core |
| Data visualization | Common | Core in many roles |
| Dashboards and recurring reporting | May be part of the role | Common in many roles |
| Predictive modeling | Common in many roles | Role-dependent |
| Machine learning | Common in many Data Scientist roles | Used in some advanced analyst roles |
| Algorithms | Important, particularly for modeling and computational work | Role-dependent |
| Business/domain knowledge | Important | Important |
| Communicating findings | Core | Core |
| Stakeholder interaction | Important | Often central |
Programming: How Much Coding Does Each Career Require?
Programming is generally more consistently central to Data Scientist roles , although the amount of coding varies within both careers.
Data Scientists may use programming to:
- Prepare and manipulate data.
- Perform statistical analysis.
- Develop and test algorithms.
- Build predictive models.
- Implement machine-learning methods.
- Automate analytical workflows.
- Evaluate and refine analytical results.
Python and R are commonly associated with data-science work, while SQL is widely useful for retrieving and working with structured data.
Data Analysts can also use Python or R, particularly in more technical positions, but programming requirements vary more widely. Some analyst roles rely heavily on SQL, spreadsheets, business-intelligence platforms, and visualization tools , while others involve substantial scripting, statistical analysis, or automation.
For someone choosing between the careers, the better question is not “Which one requires coding?” Both can.
Ask instead:
Do I want programming to be a central tool for developing models and analytical methods, or primarily a tool for accessing, transforming, analyzing, and communicating data?
The first points more strongly toward data science. The second is common across data-analysis roles.
Statistics and Mathematics
Data science generally places the stronger and more consistent emphasis on mathematics and statistics.
BLS identifies mathematics as an important part of Data Scientist preparation and describes Data Scientists as using analytical techniques to extract insights from data and developing and testing algorithms and models.
Relevant areas can include:
- Probability
- Statistics
- Linear algebra
- Calculus
- Statistical modeling
- Experimental methods
- Machine-learning methods
Data Analysts also use quantitative reasoning and statistical methods, but the required depth varies substantially.
An analyst responsible primarily for dashboards and recurring business reporting may use descriptive statistics and performance metrics. An analyst working with experimentation, forecasting, product analytics, or other quantitatively advanced problems may need considerably deeper statistical knowledge.
So the distinction is NOT “Data Scientists use math; Data Analysts don’t.”
But the distinction is:
Advanced mathematics, statistics, and modeling are more consistently central to data science, while the quantitative depth of Data Analyst positions varies considerably with the work being performed.
Machine Learning Is a Major Point of Separation
Machine learning is associated much more directly with data science than with the broad Data Analyst title.
BLS specifically describes Data Scientists as creating, validating, testing, and updating algorithms and models. Depending on the position, those methods can include machine-learning approaches used for classification, prediction, pattern recognition, or other analytical problems.
Some advanced Data Analysts also work with predictive models or machine-learning tools. That overlap does not make machine learning a universal Data Analyst responsibility.
If you want developing and evaluating models to become a significant part of your work, data science provides the more direct career direction.
If you are more interested in using data to measure performance, investigate questions, identify trends, build visualizations, and help stakeholders make decisions , data analysis may align more closely.
Communication Matters in Both Careers
Neither career consists only of technical work.
Analysis has limited value if the people making decisions cannot understand what the results mean.
Data Scientists may need to explain:
- What a model or analysis found.
- How reliable the result is.
- Which assumptions or limitations matter.
- How an organization could use the finding.
Data Analysts may need to explain:
- What changed.
- Which metrics matter.
- What patterns appear in the data.
- What the analysis can and cannot establish.
- What stakeholders should investigate or consider next.
The audience can also differ by organization. Analysts are often closely connected to business, operational, product, marketing, financial, or other functional teams. Data Scientists may work more closely with engineering, research, product, analytics, or specialized data teams.
Those are common patterns rather than universal organizational structures.
Which Skill Set Fits You Better?
| If You Prefer… | Career Direction to Explore |
|---|---|
| Working extensively with statistics and probability | Data Science |
| Developing predictive models | Data Science |
| Machine learning | Data Science |
| Programming-intensive analytical work | Data Science |
| Building and testing algorithms | Data Science |
| SQL and querying organizational data | Data Analysis or Data Science |
| Dashboards and recurring reporting | Data Analysis |
| Tracking business or operational metrics | Data Analysis |
| Data visualization | Either |
| Investigating patterns in data | Either |
| Communicating analytical findings | Either |
| Using analysis to support organizational decisions | Either |
The strongest dividing line is how far you want to move from analyzing data into developing statistical and computational models .
Data analysis can provide substantial technical depth, and individual analyst positions can overlap with data science. But if you specifically want statistics, programming, predictive modeling, algorithms, and machine learning to become central parts of your career , data science is generally the more direct path.
Education and Degree Pathways
Data Scientist and Data Analyst careers do not have a simple master’s-versus-bachelor’s divide.
For Data Scientists (SOC 15-2051) , BLS identifies a bachelor’s degree as the typical entry-level education . BLS also notes that some employers require or prefer candidates with a master’s or doctoral degree.
Data Analyst is not a single BLS occupation, so there is no equivalent federal education requirement that applies to every job carrying that title. Requirements vary with the work itself—from reporting and business intelligence to statistical analysis, experimentation, and more technically advanced analytics.
| Education Dimension | Data Scientist | Data Analyst |
|---|---|---|
| BLS typical entry-level education | Bachelor’s degree | No single BLS occupation-wide requirement |
| Commonly relevant undergraduate fields | Mathematics, statistics, computer science, data science, or related quantitative fields | Business, economics, mathematics, statistics, computer science, information systems, analytics, or other fields relevant to the role |
| Graduate degree universally required? | No | No |
| Advanced quantitative preparation | Often important | Depends substantially on role |
| Programming preparation | Commonly important | Varies by technical depth |
| Graduate paths that may align | Data science, statistics, computer science, analytics, or related quantitative programs | Data analytics, business analytics, data science, statistics, information systems, or related programs |
Education for Data Scientists
BLS identifies a bachelor’s degree as the typical entry-level education for Data Scientists , commonly in mathematics, statistics, computer science, or a related field.
Data-science preparation can require substantial quantitative and computational coursework. Depending on the program and career target, relevant areas can include:
- Mathematics and statistics
- Programming
- Data management
- Algorithms
- Statistical modeling
- Machine learning
- Data visualization
- Computational methods
BLS also notes that some employers require or prefer a master’s or doctoral degree .
That distinction matters. It supports saying graduate education can be relevant for some Data Scientist positions. It does not support saying a master’s degree is generally required to become a Data Scientist.
Education for Data Analysts
There is no single educational pathway into data analysis because the title covers a broad range of work.
Some Data Analyst positions emphasize business or operational analysis and may value knowledge of a particular domain alongside skills such as SQL, spreadsheets, visualization, and reporting. More technical analyst positions may require stronger preparation in programming, statistics, experimentation, databases, or advanced analytical methods.
Relevant academic backgrounds can therefore include fields such as:
- Data analytics
- Business analytics
- Statistics
- Mathematics
- Economics
- Computer science
- Information systems
- Finance or other quantitative business fields
- A subject-area discipline combined with analytical training
The appropriate preparation depends less on whether the degree says “Data Analytics” and more on whether you can perform the work required by the positions you are targeting.
Do You Need a Master’s Degree for Data Science?
Not universally.
BLS lists a bachelor’s degree as the typical entry-level education for Data Scientists. However, it also states that some employers require or prefer a master’s or doctoral degree.
A master’s may make sense when you need deeper preparation in areas such as:
- Statistics and probability
- Machine learning
- Advanced modeling
- Programming and algorithms
- Data management
- Experimental methods
- Computational or quantitative problem-solving
It may also be relevant when the specific Data Scientist positions you want explicitly require or prefer graduate education.
But earning a master’s should not be treated as an automatic prerequisite—or as a guarantee of becoming a Data Scientist.
Students considering graduate study can explore OMC’s online master’s in data science programs .
Do You Need a Master’s Degree for Data Analysis?
Not as a universal requirement.
Because Data Analyst is not one standardized occupation, graduate-school requirements vary considerably across employers and positions.
Before considering a master’s, look at the actual gap between your current capabilities and your target jobs.
If positions you’re interested in primarily require SQL, spreadsheets, visualization, reporting, and business analysis , a full graduate degree may be more education than you need to close the gap.
If you want to develop deeper capabilities in statistics, programming, experimentation, predictive analytics, data management, or advanced analytical methods , graduate study may become more relevant.
Students considering that route can explore OMC’s online master’s in data analytics programs .
Master’s in Data Science vs Master’s in Data Analytics
If you are considering graduate school, the degree title should follow the type of analytical work you want to develop toward.
| If You Want More Emphasis On… | Path More Likely to Align |
|---|---|
| Statistics and advanced quantitative methods | Data Science |
| Predictive modeling | Data Science |
| Machine learning | Data Science |
| Algorithms and computational methods | Data Science |
| Reporting and decision support | Data Analytics |
| Business intelligence and visualization | Data Analytics |
| Applied organizational analytics | Data Analytics |
| Data management and analysis | Either — compare curricula |
| Programming | Either — technical depth varies by program |
| Statistical analysis | Either — compare required coursework |
These are directional distinctions, not universal curriculum rules . A technically rigorous Data Analytics program may include substantial programming, statistics, and predictive modeling, while Data Science programs also vary significantly in depth and emphasis.
Students choosing between the degrees can use OMC’s MS Data Science vs MS Data Analytics comparison to evaluate the two graduate paths directly.
When Graduate School May Not Be the Best Next Step
A master’s degree is a significant investment of time and money. It makes the most sense when the curriculum addresses a meaningful gap between what you can do now and what your target positions require .
Graduate school may not be the most efficient next step if your primary gap is narrower—for example:
- Learning SQL.
- Becoming proficient with a visualization platform.
- Developing stronger spreadsheet skills.
- Learning a programming language.
- Building a portfolio of analytical work.
- Gaining experience with a particular database or cloud platform.
- Developing knowledge specific to the industry you want to enter.
Those gaps may be addressable through coursework, certificates, projects, employer training, or experience without completing an entire master’s program.
The better question is therefore not:
“Do Data Scientists or Data Analysts need a master’s?”
But the better question is:
“Do the positions I want require capabilities that graduate study is the best way for me to develop?”
That keeps the education decision tied to the career objective rather than assuming that another credential is automatically the next step.
Career Trajectory: Where Each Path Can Lead
Data analysis and data science can overlap early in a career, and professionals can move between them. But neither follows a standardized ladder, and Data Analyst should not be treated automatically as an entry-level stage that eventually becomes Data Scientist .
The careers can develop in different directions based on the expertise a professional builds over time.
| Career Dimension | Data Scientist | Data Analyst |
|---|---|---|
| Core foundation | Statistical and computational analysis, modeling, and extracting insights from data | Analyzing, interpreting, and communicating data to answer questions and support decisions |
| Potential technical direction | Advanced modeling, machine learning, experimentation, specialized applied data science | Advanced analytics, product analytics, business intelligence, experimentation, specialized domain analytics |
| What may deepen over time | Statistics, programming, modeling, algorithms, machine learning, and domain expertise | Analytical methods, SQL, visualization, experimentation, business or domain expertise, and decision support |
| Potential leadership direction | Data science, analytics, or technical team leadership | Analytics, business intelligence, or functional analytics leadership |
| Adjacent paths | Machine learning, advanced analytics, AI-related work, quantitative roles | Business intelligence, product analytics, business analysis, operations or domain-specific analytics |
| Standardized promotion ladder? | No | No |
These are representative career directions, not federally defined promotion sequences. Titles, responsibilities, and requirements vary by employer.
How a Data Analyst Career Can Develop
Data Analysts can deepen their analytical capabilities without necessarily becoming Data Scientists.
Potential directions can include:
- Senior or more technically advanced analytics work.
- Business intelligence.
- Product analytics.
- Marketing, financial, healthcare, operations, or other domain-specific analytics.
- Experimentation and statistical analysis.
- Analytics engineering or other more technical data work where the required capabilities align.
- Analytics team or functional leadership.
A professional’s direction often depends on the problems they become good at solving.
Someone who develops deep knowledge of a particular business domain may become increasingly valuable for connecting data with organizational decisions. Someone who builds stronger programming, statistical, or technical capabilities may move toward more quantitatively advanced analytical work.
Neither path requires becoming a Data Scientist.
How a Data Science Career Can Develop
Data Scientists can deepen their expertise in statistical and computational methods or take on broader responsibility for analytical work.
Potential directions can include:
- Advanced statistical modeling.
- Machine learning.
- Experimentation and causal analysis.
- Specialized applied data science.
- Quantitative research or analytics.
- Data and analytical strategy.
- Technical leadership.
- Data science or analytics management.
The direction can also depend heavily on industry and problem domain. A Data Scientist working on healthcare problems may develop very different expertise from one working in finance, marketing, technology, scientific research, or another field.
Data science can also provide relevant preparation for some machine-learning and AI-related positions, but Data Scientist, Machine Learning Engineer, and AI Researcher are not interchangeable career categories . Moving into those roles may require additional software-engineering, mathematical, systems, or research capabilities.
Can a Data Analyst Become a Data Scientist?
Yes, but it is not an automatic promotion.
The transition is possible because the careers share capabilities such as data preparation, SQL, analysis, visualization, statistical reasoning, and communicating findings.
The important question is what additional capabilities the target Data Scientist positions require.
Depending on the analyst’s existing background, gaps may include:
- More advanced statistics and probability.
- Programming depth.
- Statistical modeling.
- Machine learning.
- Algorithms.
- Experimental methods.
- Mathematics.
- Working with larger or more complex datasets.
- Developing and evaluating models rather than primarily consuming their outputs.
An analyst who already performs technically advanced work may have a smaller gap than someone whose role primarily involves dashboards and recurring reporting.
For that reason, OMC would not characterize Data Analyst → Data Scientist as a fixed career ladder or claim that earning a master’s degree automatically completes the transition.
Does Data Science Have a Higher Career Ceiling?
The available federal data do not establish that.
BLS wage data describe the current wage distribution for Data Scientists. They do not define a maximum career salary or measure the long-term compensation ceiling of Data Scientists relative to the broad range of jobs carrying the Data Analyst title.
Likewise, titles such as Senior Data Scientist, Analytics Director, Head of Data, Machine Learning Engineer, or other advanced positions cannot simply be placed into a universal progression and assigned BLS wage percentiles.
Both career directions can lead to deeper specialization, greater organizational responsibility, and leadership.
A better long-term comparison is which expertise you want to compound .
How the Careers Diverge Over Time
For Data Analysts, experience can increasingly build expertise around interpreting organizational data, understanding a business or domain, designing useful analyses, measuring performance, communicating findings, and helping decision-makers understand what the data mean .
For Data Scientists, experience can increasingly build expertise around statistics, computational methods, algorithms, modeling, experimentation, machine learning, and solving increasingly complex problems with data .
That produces two different forms of analytical value:
Data Analyst: How can we use our data to understand what is happening and make better decisions?
Data Scientist: What can we learn, model, predict, or optimize using data and quantitative methods?
Those questions can overlap, but they point toward different concentrations of expertise.
If you want your career to become progressively more centered on statistical and computational modeling , Data Science provides the more direct direction.
If you want to become increasingly skilled at using data to understand organizations, measure outcomes, investigate questions, and support decisions , Data Analysis can remain a substantive career path in its own right.
OMC Analysis: The Careers Compound Different Types of Expertise
The most useful long-term distinction between Data Scientists and Data Analysts may not be their job titles. It is what each career allows you to become progressively better at .
A Data Analyst can compound expertise in understanding how an organization works through its data. Over time, that can mean becoming better at defining useful metrics, recognizing meaningful patterns, investigating performance, understanding a particular domain, and translating analysis into decisions.
A Data Scientist can compound a different form of expertise: using increasingly sophisticated statistical and computational methods to extract information from data. Over time, that can mean deeper capabilities in modeling, experimentation, machine learning, algorithms, and quantitative problem-solving.
That produces two different—but overlapping—forms of career capital:
| Data Analysis Can Compound Toward… | Data Science Can Compound Toward… |
|---|---|
| Deeper domain knowledge | Deeper statistical knowledge |
| Better understanding of organizational metrics | More advanced modeling |
| Stronger business and decision context | More advanced computational methods |
| More sophisticated applied analysis | Machine-learning expertise |
| Better translation between data and stakeholders | More complex quantitative problem-solving |
| Analytics and functional leadership | Data-science and technical leadership |
Neither direction is inherently more advanced.
A highly experienced Data Analyst with deep industry knowledge and strong analytical judgment may solve problems that require substantial expertise without becoming a Data Scientist. Likewise, a Data Scientist may become increasingly specialized in technical methods without moving toward business or organizational leadership.
This is why OMC does not treat Data Analyst → Data Scientist as the default career ladder.
The better long-term question is:
Which type of expertise do you want five or ten years of experience to compound?
If the answer is increasingly sophisticated statistical, computational, and predictive methods , Data Science is the stronger direction.
If the answer is increasingly sophisticated analysis of organizations, performance, behavior, and decisions , Data Analysis can be the stronger direction.
Industries and Work Settings
Data Scientists and Data Analysts can work across many of the same industries. The more important difference is often what they are hired to do within those organizations.
A healthcare company, financial institution, retailer, manufacturer, technology company, or government agency may employ both. A Data Analyst might focus on performance measurement, reporting, operational questions, or business decisions, while a Data Scientist might work on statistical models, prediction, experimentation, or other more computationally intensive problems.
Because Data Analyst does not map to one federal occupation, OMC does not assign industries “High,” “Very High,” or “Moderate” Data Analyst concentration ratings without a consistent occupational dataset supporting those classifications.
How the Two Careers Can Work Within the Same Industry
| Industry Context | Data Scientist Work May Include | Data Analyst Work May Include |
|---|---|---|
| Technology and software | Product modeling, experimentation, recommendation or prediction problems, user-behavior modeling | Product metrics, dashboards, user analysis, performance reporting, trend analysis |
| Finance and insurance | Risk or predictive modeling, quantitative analysis, fraud-related modeling | Financial or operational reporting, performance analysis, customer analysis, business intelligence |
| Healthcare | Predictive modeling, outcomes analysis, statistical research, data-intensive analytical problems | Operational analysis, utilization reporting, performance metrics, quality or business analysis |
| Retail and e-commerce | Forecasting, personalization, pricing or customer modeling | Sales analysis, inventory reporting, customer segmentation, marketing or performance analytics |
| Manufacturing and operations | Predictive or optimization models, advanced process analysis | Production metrics, operational reporting, quality analysis, supply-chain analytics |
| Government and public sector | Statistical modeling, research, forecasting, or other data-intensive analytical work | Program analysis, reporting, performance measurement, operational or policy-related analysis |
| Consulting and professional services | Advanced modeling and specialized quantitative analysis for client problems | Client reporting, business analysis, market analysis, dashboards, and decision support |
These examples illustrate common ways the work can differ within broad industry settings. They are not federal definitions of Data Analyst work, rankings of industry demand, or claims that every employer in these industries hires both roles.
How Industry and Employer Context Change the Role
The same career title can look different depending on the industry, employer, and team.
A Data Scientist working in healthcare may develop expertise around clinical or outcomes data, while one in finance, retail, technology, or manufacturing may work on very different problems. The statistical and computational foundation can transfer, but understanding the domain becomes increasingly important when deciding what to measure, how to model it, and whether the results make sense in context .
The variation can be even greater for Data Analysts because the title covers a broader range of responsibilities.
A Data Analyst might work within:
- Finance.
- Marketing.
- Product.
- Operations.
- Healthcare.
- Supply chain.
- Human resources.
- Government.
- Business intelligence.
- Research.
Two analysts may both use SQL and visualization tools while doing substantially different work because one is analyzing customer behavior and the other is evaluating financial, operational, or healthcare data.
Employer structure matters too. Some organizations place Data Scientists and Data Analysts within centralized data or analytics teams. Others embed them directly within product, finance, marketing, operations, or other functional teams. Smaller organizations may combine responsibilities that larger employers divide among several specialized positions.
That makes the job description more informative than the title alone .
When comparing opportunities, look at:
- The problems the team expects you to solve.
- The data you will work with.
- The technical methods required.
- How much of the job involves reporting versus modeling.
- Who uses your analysis.
- How much domain knowledge the position requires.
- Whether you will work primarily with technical teams, functional stakeholders, or both.
For either career, technical skills can help you move between industries. But as your career develops, the combination of analytical expertise + domain knowledge can become an important part of what differentiates you.
Does One Career Offer More Industry Flexibility?
The available evidence does not establish a clear winner.
Both career directions can be applied to problems across multiple industries. What transfers between industries, however, is somewhat different.
Data Scientists can carry capabilities in statistics, programming, modeling, experimentation, machine learning, and quantitative problem-solving into different domains.
Data Analysts can carry capabilities in querying, analysis, visualization, reporting, metric design, and decision support while developing expertise in the new organization’s business or subject area.
The practical question is therefore less:
“Which career lets me work in more industries?”
and more:
“Which type of analytical expertise do I want to carry from one industry to another?”
If you want that expertise centered increasingly on statistical and computational modeling , Data Science is the more direct direction.
If you want it centered on interpreting organizational data and helping people understand performance, trends, and decisions , Data Analysis may align more closely.
Which Career Fits You?
The labor-market data cannot tell you whether Data Scientist or Data Analyst is the better career for you . The more useful decision comes from comparing the kind of problems you want to solve, the technical depth you want to develop, and how you want your work to influence decisions.
The biggest mistake is choosing based only on salary or assuming Data Analyst is simply the first step toward becoming a Data Scientist. These are overlapping but distinct career directions, and Data Analysis can be a long-term career rather than a temporary entry point.
Choose Data Science If…
Data Science may be the stronger fit if you want to:
- Work extensively with statistics, probability, and quantitative methods.
- Use programming as a central part of your analytical work.
- Build, test, and refine statistical or predictive models.
- Work with machine learning.
- Investigate complex or open-ended problems using data.
- Develop deeper expertise in algorithms and computational methods.
- Spend more of your career moving toward advanced modeling and quantitative problem-solving.
You should also be comfortable with the possibility that some positions will expect stronger mathematical preparation and that some employers may prefer or require graduate education.
Choose Data Analysis If…
Data Analysis may be the stronger fit if you want to:
- Use data to answer defined organizational or business questions.
- Work extensively with SQL, reporting, dashboards, and visualization.
- Investigate trends and changes in performance.
- Translate data into information that stakeholders can act on.
- Develop expertise in a particular business function or industry.
- Combine analytical ability with substantial stakeholder interaction.
- Build a career around analytics without necessarily making machine learning or advanced modeling central to your work.
Technical depth varies considerably among Data Analyst positions. Choosing Data Analysis does not mean choosing a nontechnical career.
Data Scientist vs Data Analyst Decision Table
| If This Sounds More Like You… | Stronger Direction | Why |
|---|---|---|
| I want machine learning to be a significant part of my work. | Data Scientist | Machine learning and model development are more consistently associated with Data Scientist roles. |
| I enjoy statistics, mathematical modeling, and programming. | Data Scientist | These capabilities are more consistently central to the occupation. |
| I want to build predictive models and algorithms. | Data Scientist | Model and algorithm development are part of the federally defined Data Scientist occupation. |
| I prefer answering concrete organizational questions with data. | Data Analyst | Analyst roles commonly center on applying data to defined business or operational problems. |
| I enjoy SQL, dashboards, visualization, and reporting. | Data Analyst | These are common components of many Data Analyst positions. |
| I want to work closely with business or functional stakeholders. | Data Analyst may fit better | Many analyst roles are closely connected to the teams whose questions they support, although organizational structures vary. |
| I enjoy visualization and explaining findings. | Either | Communication and visualization matter in both careers. |
| I want a highly technical analytics career but am not sure about machine learning. | Compare actual roles | Advanced Data Analyst positions can be highly technical without being Data Scientist positions. |
| I already work as a Data Analyst and want more modeling and machine learning. | Consider Data Science | The desired change in responsibilities matters more than the title itself. |
| I mainly want whichever career pays more. | Don’t decide yet | Federal data provide a Data Scientist wage benchmark but not an equivalent national Data Analyst wage, so a clean salary-gap comparison is not available. |
Three Questions That Usually Clarify the Choice
If the distinction still feels unclear, ask yourself these three questions.
1. Do I want to analyze data, or do I specifically want to build models from it?
Both careers analyze data. If developing predictive or statistical models is one of the parts that attracts you most, that pushes the decision toward Data Science.
2. How much mathematical and programming depth do I want?
Both careers can be technical. Data Science, however, more consistently requires deeper statistical, computational, and programming preparation.
If you enjoy analytics but would rather center your work on SQL, visualization, metrics, reporting, and organizational questions, Data Analysis may be the better direction.
3. What do I want people to rely on me for?
A Data Analyst may become the person stakeholders rely on to understand what is happening in the data, what changed, which metrics matter, and what the findings mean for a decision .
A Data Scientist may increasingly be relied on to determine what can be modeled, predicted, classified, optimized, or discovered using statistical and computational methods .
Neither is inherently the better contribution. They solve different types of problems.
If You’re Still Unsure, Compare Jobs Instead of Titles
Job titles in the data field are inconsistent enough that choosing a career from the title alone can be misleading.
Pull several Data Scientist and Data Analyst openings from employers you could realistically see yourself working for and compare:
- Required skills.
- Day-to-day responsibilities.
- Programming expectations.
- Statistical requirements.
- Machine-learning responsibilities.
- Reporting and visualization work.
- Education requirements.
- Experience requirements.
- Industry knowledge.
- Salary or compensation when disclosed.
You may discover that an advanced Data Analyst opening is closer to the work you want than a particular Data Scientist opening—or the reverse.
That exercise also tells you something a national career comparison cannot: the actual skill gap between where you are now and the jobs you want.
Choose Data Science because you want the work—not simply because its BLS salary and growth figures are attractive. Choose Data Analysis because its analytical focus fits you—not because you assume it is an easier version of Data Science.
The better career is the one whose problems, tools, and developing expertise match the work you actually want to become good at.
Still comparing technical career paths? OMC also examines Cybersecurity vs Data Science using the same career-comparison approach.
Data Scientist vs Data Analyst: Final Comparison
Data Scientist and Data Analyst careers overlap, but they are not simply advanced and entry-level versions of the same job.
The clearest difference is where each career tends to place its analytical depth.
| Decision Factor | Data Scientist | Data Analyst |
|---|---|---|
| Primary career emphasis | Statistical and computational modeling | Applied analysis and decision support |
| Typical questions | What can we model, predict, classify, optimize, or discover? | What happened, what is changing, and what does the data mean for a decision? |
| Programming emphasis | Commonly important | Varies by role |
| Statistics | Generally deeper and more consistently central | Important, but depth varies substantially |
| Machine learning | Common in many roles | Role-dependent |
| SQL / data querying | Common | Common |
| Dashboards / recurring reporting | May be part of the role | Common in many roles |
| Stakeholder communication | Important | Often central |
| Federal median wage | $120,230 — May 2025 | No single BLS benchmark |
| Federal employment outlook | 33.5% projected growth, 2024–2034 | No single BLS benchmark |
| Typical BLS entry education | Bachelor’s degree | No single occupational standard |
| Master’s universally required? | No | No |
| Stronger fit if you enjoy… | Modeling, statistics, programming, machine learning, and quantitative problem-solving | SQL, analysis, visualization, metrics, domain questions, and translating data into decisions |
Final Answer
Choose Data Science if you want statistical modeling, programming, machine learning, and increasingly complex quantitative methods to become central to your work.
Choose Data Analysis if you want to use data to understand performance, investigate questions, communicate findings, and help organizations make better decisions.
If you enjoy both, don’t assume you need to choose permanently. The careers share enough analytical foundations that movement between them is possible, particularly as you develop new technical or domain expertise.
The key is to compare the work you want to become good at , not simply the titles.
OMC Finding
Data Scientist is generally the more direct path toward advanced statistical and computational modeling. Data Analyst is generally the more direct path toward applied organizational analysis and decision support. Neither is inherently the “better” career—the stronger choice depends on which type of analytical expertise you want to build.
If Data Science appears to be the stronger fit and graduate education is part of your plan, explore OMC’s online master’s in data science programs. If you’re still deciding between the academic paths, compare an MS in Data Science vs MS in Data Analytics before choosing a program.
Frequently Asked Questions about Data Scientist Vs. Data Analyst Careers
Yes. Data Analysts can move into Data Scientist roles because the careers share foundations in data preparation, querying, analysis, statistics, visualization, and communication. The transition is not automatic. Depending on the target role, an analyst may need deeper capabilities in statistics, programming, predictive modeling, machine learning, algorithms, or other quantitative methods . The right pathway depends on the gap between the analyst’s existing skills and the requirements of the Data Scientist positions being targeted.
There is no clean federal dataset that allows OMC to calculate a national Data Scientist-vs-Data Analyst salary gap . BLS reports a May 2025 median annual wage of $120,230 for Data Scientists (SOC 15-2051) . Data Analyst, however, is not a standalone BLS occupation with an equivalent national wage estimate. Private salary sources can provide additional information about jobs carrying the Data Analyst title, but combining those estimates with BLS Data Scientist wages would not create an apples-to-apples federal comparison. For an individual career decision, compare compensation for actual Data Scientist and Data Analyst positions in the same location, industry, and experience range.
No. BLS identifies a bachelor’s degree as the typical entry-level education for Data Scientists , although some employers require or prefer a master’s or doctoral degree. Graduate study may be useful when you need deeper preparation in statistics, machine learning, programming, modeling, or other quantitative areas—or when your target positions specifically prefer graduate education. Students considering that route can compare OMC’s MS Data Science vs MS Data Analytics guide.
There is no universal answer . Data Analyst may be a practical target for some career changers because many positions emphasize skills such as SQL, spreadsheets, visualization, reporting, and applied analysis. But Data Analyst roles vary considerably, and OMC does not have consistent federal evidence establishing that Data Analysis is universally “easier” to enter. Data Science generally requires stronger preparation in statistics, programming, modeling, and computational methods. A career changer who already has a quantitative or technical background may therefore face a very different transition from someone starting without that foundation. Instead of choosing based on which title seems easier, compare the requirements of actual jobs against the skills you already have.
The evidence used for this comparison does not support saying that Data Analyst jobs as a category are being replaced by AI . AI and automation can change individual analytical tasks, including aspects of data preparation, querying, reporting, visualization, and analysis. But Data Analyst is not a single federal occupation for which OMC can point to a standardized national projection showing AI-driven job loss. The more useful career question is how the work is changing. Analysts who can evaluate data quality, frame useful questions, interpret results in context, communicate limitations, understand their domain, and help organizations make decisions contribute capabilities that extend beyond generating a report or dashboard. AI proficiency itself may also become part of the analytical toolkit rather than simply a substitute for the analyst.
Use your coursework and projects to test the work rather than choosing based only on salary or job titles. If you find yourself most interested in statistics, programming, mathematical methods, predictive modeling, and machine learning , explore Data Science more deeply. If you prefer querying data, visualization, metrics, investigating organizational questions, and communicating findings , explore Data Analysis. Then test that preference through projects, internships, research, or entry-level work when possible. If graduate school is part of your plan, compare the actual curricula rather than relying on degree names. OMC’s MS Data Science vs MS Data Analytics comparison examines that education decision directly.