Intelligence Brief Technology Sector
Data Science
Bachelor's · 4 years
B
Scorecard
- $108,020
- Median salary
- 36%
- Projected growth
- 67/100
- Difficulty
- 6
- Career paths
AI Resilience 70
Overall Score 72
CollegeRanker Degree Outlook Score™
81
out of 100 · A-
Exceptional Outlook
Composite of earnings, projected growth, demand gap, AI resilience, career breadth, and remote flexibility — CollegeRanker's proprietary degree outlook model.
Supply vs Demand
Severe ShortageMarket Demand95
Graduate Supply5
Demand far exceeds graduate supply — projected 36% occupational growth (much faster than average).
Salary Trajectory
~7.5%/yrModeled from BLS median wage and occupational growth. Dashed bars are forecast. Illustrative, not a guarantee.
Where Graduates Work
Common Employers
- Microsoft
- Amazon
- Meta
- Apple
- NVIDIA
- IBM
- Salesforce
Representative employers that commonly hire Technology graduates — illustrative of where graduates concentrate, not a guarantee.
Industry Mix
- Software & Internet 38%
- Cloud & AI Infrastructure 19%
- Finance & Fintech 14%
- Healthcare Tech 11%
- Defense & Aerospace 9%
- Other 9%
Estimated distribution of Technology graduates across hiring industries.
Executive Summary
- Data Science scores 72/100 (B), reflecting a balanced profile among bachelor's programs.
- Median salary of $108,020 reflects competitive earning potential.
- Projected growth of 36% significantly outpaces the national average.
- AI resilience score of 70 suggests the careers this degree feeds into face low automation risk.
Data Science scores 72/100 — B. The strongest dimension is growth (98/100), followed by remote potential (85/100). The biggest challenge: salary (54/100).
Research Insights
- Strong Future-proof
Data Science rates as a strongly future-proof degree (82/100). The degree benefits from low AI disruption risk across its career pathways and opens doors to 6+ distinct career paths. Demand for graduates is expected to remain robust.
Score 82 /100 - Decent ROI
Data Science offers a moderate ROI (62/100). Salary outcomes are reasonable but the path to maximum earning requires additional credentials or specialization.
Score 62 /100 - Broad Career Breadth
Data Science provides exceptional career flexibility (82/100). Graduates can pursue 6+ distinct roles across multiple industries, making this degree highly adaptable to changing labor market conditions.
Score 82 /100
Decision Intelligence
Data Science offers solid potential but requires strategic execution — the right concentration, school, and internships matter significantly to the outcome.
Who Benefits Most
Students who value career stability and meet the academic prerequisites. Students who pair this degree with internships and networking outperform peers. The strong AI resilience across associated careers adds long-term security.
Who Should Think Twice
Individuals who struggle with quantitative subjects or do not enjoy problem-solving and analytical thinking may find this degree challenging. Additionally, those seeking a clear-cut career path without the willingness to adapt to evolving technologies and methodologies might also be poorly suited for this field.
Student Archetypes
- The Data Enthusiast Recommended
This student has a strong interest in data analysis and enjoys working with numbers and technology. They are typically self-motivated and eager to learn.
- The Career Switcher Conditional
This student is transitioning from a different field and seeks to leverage their analytical skills in data science. They may require additional foundational knowledge.
Economic Importance
The Data Science degree plays a crucial role in various industries, including technology, finance, healthcare, and marketing. As organizations increasingly rely on data-driven decision-making, the demand for skilled professionals in data science continues to grow significantly.
Scorecard Analysis
Our proprietary scorecard evaluates degrees across five dimensions from BLS wage and growth data, O*NET work context, and standard education requirements.
Moderate earning potential
Exceptional growth trajectory
Moderate barrier
Strong remote/online compatibility
Low competition
Difficulty Score
67/100
Composite reflecting the combined demands of salary, growth, barrier, remote compatibility, and competition.
AI Resilience Assessment
Automation risk for careers linked to this degree.
Data Science ranks highly for AI resilience (70/100). The careers this degree feeds into demand complex human judgment, specialized expertise, or physical presence that AI cannot easily replicate. Graduates who stay current with AI tooling in their domain will remain in strong demand.
- Careers from this degree require complex human judgment and specialized expertise that AI cannot replicate.
- High-touch human interaction is central to many career paths from this degree, making full automation unlikely.
- Limited risk: administrative or analytical components within some roles may see AI-driven efficiency gains.
Intelligence Deep Dive
-
Reality Check
While the job market for data science is robust, it is also highly competitive. Graduates must be prepared for continuous learning and adapting to new tools and technologies, as the field is rapidly evolving and can be demanding.
-
Hiring Market Signal
The hiring market for data science graduates is currently strong, with various sectors actively seeking talent. Job seekers should focus on developing a strong portfolio and networking to stand out in this competitive environment.
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Risk Factors
- High student debt
- Saturation in certain geographic areas
- Rapid changes in technology leading to skill obsolescence
- Increased automation in data tasks
- Variability in job market demand based on industry shifts
-
ROI Timeline
Graduates can typically expect to recoup their investment within 3-5 years, depending on their starting salary and debt load. Factors like industry, geographic location, and economic conditions can also significantly influence this timeline.
What You'll Study
The curriculum combines essential courses in statistics, machine learning, and programming, equipping graduates with the analytical skills required to extract valuable insights from complex data sets. This blend of technical and theoretical knowledge prepares students for a range of data-centric roles.
Expect a core of statistics, probability, and linear algebra alongside programming in Python and R, then machine learning, data visualization, and database work. The strongest programs require a capstone where you take a real dataset end-to-end. Don't skip the statistics for the flashier machine learning — understanding why a model works is what separates a data scientist from someone who runs library functions.
Typical Curriculum
- Statistics & Probability
- Machine Learning
- Data Mining
- Database Management
- Python/R Programming
- Data Visualization
- Linear Algebra
- Big Data Technologies
Career Pipeline
From entry to executive.
Entry-Level
- Data Analyst
- Junior Data Scientist
- Business Intelligence Analyst
Mid-Career
- Data Scientist
- Machine Learning Engineer
- Data Engineer
Advanced
- Chief Data Officer
- Director of Data Science
Pipeline Insight
Graduates typically start in entry-level positions, leveraging their foundational skills before progressing to mid-career roles that demand more specialized expertise. Those who advance often engage in continuous learning and networking, while those who stall may lack the initiative to upskill or adapt to industry changes.
Career Outcomes
Graduates become data scientists, analysts, and machine-learning engineers across nearly every sector. With data science roles projected to grow 36% — among the fastest of any field — demand is strong, though employers increasingly expect demonstrated, hands-on experience, not just a transcript.
- Data Scientist
- Data Analyst
- Business Intelligence Analyst
- Machine Learning Engineer
- Data Engineer
- Quantitative Analyst
Compensation Context
The median salary of $108,020 reflects the high demand for data professionals and the specialized skills they possess. Geographic factors, such as location in tech hubs, can further influence pay, as can the level of experience and the specific industries hiring data scientists.
Alternative Routes
Similar or competing pathways students consider alongside Data Science:
- Computer Science
- Statistics
- Information Technology
- Self-taught Data Science
- Data Science Bootcamps
Getting In & Timeline
Typical time to complete: 4 years full-time
- Strong math background (calculus, statistics)
- Comfort with programming
- Analytical mindset
Advice
Build a public portfolio of analyses; it matters more in hiring than the degree title alone.
Is This Degree Worth It?
This degree can yield a strong return on investment, especially in high-demand markets where graduates command competitive salaries. However, the payoff can diminish for those who graduate with substantial debt or who enter saturated job markets without relevant experience or skills.
Schools With Strong Outcomes in Technology
Ranked by median graduate earnings 10 years after enrollment. Schools grouped into tiers by outcome level.
Top Tier2schools
Strong Outcomes2schools
Explore More Degrees
Methodology & Data Sources
Every score, grade, and verdict on this page is built from a consistent framework designed to answer one question: what is the expected return on this degree?
Scorecard dimensions. We evaluate programs on five proprietary axes — Salary, Job Growth, Education Barrier, Remote/Online Compatibility, and Competition — each normalized to a 0–100 scale. The Overall Score is a weighted composite: salary (30%), job growth (20%), AI resilience (15%), barrier proximity (15%), competition inverse (10%), and career breadth (10%). Letter grades follow a standard scale from A+ (95+) down to F.
AI Resilience. Measures automation risk across the degree's associated career pathways. Each degree receives a category-level baseline adjusted upward for AI-adjacent fields (e.g., machine learning, computer science) and downward for fields with higher routine-task exposure. The score represents the degree's resistance to labor-market disruption, not a prediction of elimination.
Verdict scores. Future-Proof, ROI, and Career Breadth are secondary composites weighting AI resilience, growth, salary, barrier, and career count to answer specific decision questions: is this career durable (Future-Proof), financially worthwhile (ROI), and flexible (Career Breadth)?
Data sources. Salary and growth figures are drawn from the Bureau of Labor Statistics Occupational Employment and Wage Statistics (O*NET) and the Occupational Outlook Handbook (2023–2033 projections). Education requirement data and work context scores come from O*NET 28.2. School-level earnings data is sourced from the Opportunity Insights Economic Tracker (median earnings 10 years after enrollment, based on federal tax records). Program rankings and school lists reflect CollegeRanker's proprietary classification and filtering methodology.
This page is built on disclosed, reproducible data. No affiliate bias, no survey-based rankings, no undisclosed weighting.
Data Behind This Page Updated 2025
Source datasets
- U.S. Bureau of Labor Statistics — Occupational Employment & Wage Statistics (OEWS)
- U.S. Bureau of Labor Statistics — Occupational Outlook Handbook, 2023–2033 projections
- O*NET 28.2 — education requirements and work-context data
- Opportunity Insights — earnings 10 years after enrollment (federal tax records)
Methodology
Degrees are scored on five normalized axes — salary (30%), job growth (20%), AI resilience (15%), education barrier (15%), and competition (10%), plus career breadth (10%) — each on a 0–100 scale.
See the full methodology and weights →Confidence notes
- Salary and growth figures come from federal Bureau of Labor Statistics data — administrative wage records and official projections, not surveys.
- AI-resilience scores are computed from O*NET task and work-context data, applied consistently across every program.
- Every measure is normalized to a fixed 0–100 scale, so degrees are directly comparable.
Limitations
- BLS wage data reflect national medians; actual pay varies widely by region, employer, and experience.
- Job growth is a 2023–2033 projection, not a guarantee — labor markets shift with technology and the economy.
- AI-resilience is a directional estimate of automation exposure, not a prediction about any specific role.
- Figures describe typical outcomes for the field, not a promise for any individual graduate.
