Intelligence Brief Technology Sector
Data Science
Master's · 1.5-2 years
B
Scorecard
- $123,000
- Median salary
- 36%
- Projected growth
- 73/100
- Difficulty
- 5
- Career paths
AI Resilience 70
Overall Score 70
CollegeRanker Degree Outlook Score™
80
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 70/100 (B), reflecting a balanced profile among master's programs.
- Median salary of $123,000 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 70/100 — B. The strongest dimension is growth (98/100), followed by remote potential (85/100). The biggest challenge: salary (62/100).
Research Insights
- Strong Future-proof
Data Science rates as a strongly future-proof degree (77/100). The degree benefits from low AI disruption risk across its career pathways and opens doors to 5+ distinct career paths. Demand for graduates is expected to remain robust.
Score 77 /100 - Decent ROI
Data Science offers a moderate ROI (58/100). Salary outcomes are competitive but the educational investment required is significant.
Score 58 /100 - Broad Career Breadth
Data Science provides exceptional career flexibility (71/100). Graduates can pursue 5+ distinct roles across multiple industries, making this degree highly adaptable to changing labor market conditions.
Score 71 /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. Those with a related undergraduate background will see the strongest ROI. The strong AI resilience across associated careers adds long-term security.
Who Should Think Twice
Individuals who struggle with quantitative reasoning or lack a passion for data analysis may find this degree challenging and unfulfilling. Additionally, those expecting immediate high-paying roles without a willingness to invest time in skill development may be disappointed.
Student Archetypes
- The Career Switcher Conditional
This type of student comes from a non-technical background and is looking to pivot into data science, often motivated by the high salaries and growth potential in the field.
Economic Importance
Data Science is crucial across various industries, including technology, finance, healthcare, and marketing, as organizations increasingly rely on data-driven decision-making. The market values this expertise due to the growing volume of data and the need for advanced analytics to drive efficiency and innovation.
Scorecard Analysis
Our proprietary scorecard evaluates degrees across five dimensions from BLS wage and growth data, O*NET work context, and standard education requirements.
Strong earning potential
Exceptional growth trajectory
Moderate barrier
Strong remote/online compatibility
Less competitive
Difficulty Score
73/100
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 growth rate is impressive, the field is becoming increasingly competitive, with many graduates entering the job market each year. It's essential to recognize that not every program guarantees a high-paying job, and actual career paths can vary significantly.
-
Hiring Market Signal
The hiring market for data science professionals is robust, with many companies actively seeking candidates. Tech firms, financial institutions, and healthcare organizations are particularly keen on hiring skilled data scientists, making this a hot job market.
-
Risk Factors
- Increasing competition for positions
- Potential for automation of entry-level roles
- High student debt burdens
- Geographic concentration of job opportunities
- Rapidly evolving technology requiring continuous learning
-
ROI Timeline
Typically, it takes 3 to 5 years to recoup the investment in a Master's in Data Science, depending on starting salary and debt levels. Graduates entering high-paying roles can see a quicker return, while those with lower starting salaries may take longer.
What You'll Study
The curriculum is distinguished by its focus on advanced statistical techniques and ethical considerations, equipping students with the skills to handle complex data challenges and develop responsible AI solutions. This combination prepares graduates to tackle real-world problems in diverse settings.
The academic experience in a Master's in Data Science program typically includes a blend of theoretical and practical coursework. Students engage in projects that involve real-world data, often collaborating with industry partners to solve tangible problems. Hands-on labs are common, allowing students to apply their skills in statistical modeling, machine learning, and data mining.
Internships may also be a key component, providing valuable experience and networking opportunities. The curriculum can be challenging, especially courses involving complex algorithms and large-scale data processing, but these are essential for building the expertise needed in the field.
Typical Curriculum
- Advanced Statistical Modeling
- Deep Learning
- Big Data Architecture
- Causal Inference
- Bayesian Methods
- Data Ethics
- Capstone Project
Career Pipeline
From entry to executive.
Entry-Level
- Data Analyst
- Junior Data Scientist
- Business Intelligence Analyst
Mid-Career
- Data Scientist
- Machine Learning Engineer
- Analytics Manager
Advanced
- Senior Data Scientist
- Director of Analytics
- Chief Data Officer
Pipeline Insight
Graduates typically progress from entry-level analytical roles to mid-career positions through gaining practical experience and developing specialized skills. Those who advance often stay current with evolving technologies and possess strong problem-solving abilities.
Career Outcomes
Graduates of a Master's in Data Science can expect to pursue roles such as Senior Data Scientist, Machine Learning Engineer, or even Chief Data Officer. The demand for data science professionals is projected to grow by 36% in the coming years, driven by the increasing importance of data-driven decision-making across industries. As businesses continue to harness data for strategic advantages, the earning potential remains strong, with median salaries around $123,000.
- Senior Data Scientist
- ML Engineer
- Director of Analytics
- Chief Data Officer
- Research Scientist
Compensation Context
The median salary of $123,000 reflects the high demand for skilled data professionals and the significant impact their work has on business performance. Pay can vary based on factors such as geographic location, industry, and level of experience, with opportunities for higher earnings in tech hubs.
Alternative Routes
Similar or competing pathways students consider alongside Data Science:
- Computer Science (Master's)
- Statistics (Master's)
- Business Analytics (Master's)
- Data Analytics Bootcamp
- Self-taught Data Science through online courses
Getting In & Timeline
Typical time to complete: 1.5-2 years full-time
- Bachelor's degree in a related field (e.g., mathematics, computer science, engineering)
- Strong analytical skills and familiarity with programming languages
- Letters of recommendation and a statement of purpose
Advice
It's important to build a solid foundation in statistics and programming before applying. Engaging in relevant projects or internships can significantly enhance your application.
Is This Degree Worth It?
This degree can yield a strong ROI, particularly for graduates who enter high-paying roles in competitive industries. However, it may not pay off for those who lack a solid quantitative background or fail to keep up with rapid technological advancements.
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
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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.
