Intelligence Brief Sciences Sector
Statistics
Bachelor's · 4 years
B-
Scorecard
- $104,110
- Median salary
- 30%
- Projected growth
- 56/100
- Difficulty
- 5
- Career paths
AI Resilience 62
Overall Score 69
CollegeRanker Degree Outlook Score™
69
out of 100 · B+
Strong 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 30% 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
- Pfizer
- Merck
- Genentech
- National Labs
- Thermo Fisher
- Regeneron
Representative employers that commonly hire Sciences graduates — illustrative of where graduates concentrate, not a guarantee.
Industry Mix
- Pharma & Biotech 33%
- Research & Labs 24%
- Healthcare 16%
- Government 14%
- Other 13%
Estimated distribution of Sciences graduates across hiring industries.
Executive Summary
- Statistics scores 69/100 (B-), reflecting a balanced profile among bachelor's programs.
- Median salary of $104,110 reflects competitive earning potential.
- Projected growth of 30% significantly outpaces the national average.
- AI resilience score of 62 indicates moderate disruption risk across associated careers.
Statistics scores 69/100 — B-. The strongest dimension is growth (98/100), followed by salary (52/100). The biggest challenge: remote potential (35/100).
Research Insights
- Strong Future-proof
Statistics rates as a strongly future-proof degree (74/100). The degree benefits from solid growth outlook and opens doors to 5+ distinct career paths. Demand for graduates is expected to remain robust.
Score 74 /100 - Decent ROI
Statistics offers a moderate ROI (56/100). Salary outcomes are reasonable but the path to maximum earning requires additional credentials or specialization.
Score 56 /100 - Moderate Career Breadth
Statistics offers moderate career breadth (68/100). The 5 identified career paths provide options, but mobility across fields may require additional credentials or experience.
Score 68 /100
Decision Intelligence
Statistics 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 moderate AI risk makes it important to specialize.
Who Should Think Twice
Individuals who struggle with mathematical concepts or have a strong aversion to analytical tasks may find this degree challenging. Additionally, those expecting rapid career advancement without a commitment to continuous learning may be disappointed.
Student Archetypes
- The Analytical Thinker Recommended
This student enjoys problem-solving and has a strong aptitude for mathematics and logic. They thrive in environments where data is used to inform decisions.
Economic Importance
The Statistics degree is essential in various industries such as healthcare, finance, and technology, driving data-driven decision-making. The significant demand for data analysis capabilities has made the market value statistical expertise highly, particularly as organizations increasingly rely on data for strategic insights.
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
Primarily in-person
Low competition
Difficulty Score
56/100
Composite reflecting the combined demands of salary, growth, barrier, remote compatibility, and competition.
AI Resilience Assessment
Automation risk for careers linked to this degree.
Statistics faces moderate AI disruption risk (62/100). While AI will automate routine components within many associated careers, core responsibilities still require human oversight and strategic thinking. Upskilling in AI collaboration tools is recommended.
- Domain expertise from this degree provides some protection against full automation.
- AI can handle routine reporting, data aggregation, and first-pass analysis in many associated careers.
- Risk factor: entry-level roles in fields linked to this degree may face headcount reduction as AI handles more data processing.
Intelligence Deep Dive
-
Reality Check
Despite the promising growth rate, the field can be competitive, with many graduates vying for similar roles. Additionally, the rapidly evolving nature of data science tools means continuous skill development is necessary to remain relevant.
-
Hiring Market Signal
The job market is currently robust, with a notable demand for data professionals across various sectors. Employers are particularly seeking candidates with strong programming skills in addition to statistical knowledge.
-
Risk Factors
- High student debt levels
- Potential oversaturation in certain markets
- Automation of basic statistical tasks
- Geographic concentration of job opportunities
- Rapidly changing technology landscape
-
ROI Timeline
Typically, graduates can expect to recoup their investment within 3-5 years, depending on starting salary and any debt incurred. Factors such as industry demand and personal financial management also play significant roles in this timeline.
What You'll Study
The curriculum's blend of theoretical and applied courses, including Bayesian Statistics and Regression Analysis, equips students with robust analytical skills. This foundation prepares graduates to tackle complex data challenges across diverse fields.
The academic experience in a Statistics program typically begins with foundational courses in calculus and introductory statistics, followed by more advanced topics such as regression analysis, probability theory, and inferential statistics. As students progress, they often engage in practical projects that require them to apply their knowledge to real datasets, which can include internships with companies or research opportunities with faculty.
Students might find the coursework challenging, particularly in areas that involve complex mathematical concepts and statistical software. Group projects and labs help reinforce learning and provide hands-on experience, essential for mastering the tools and techniques of the field.
Typical Curriculum
- Probability Theory
- Statistical Inference
- Regression Analysis
- Experimental Design
- Bayesian Statistics
- Time Series
- Computational Statistics
- Capstone
Career Pipeline
From entry to executive.
Entry-Level
- Junior Statistician
- Data Analyst
- Research Assistant
- Quantitative Analyst
- Biostatistician Intern
Mid-Career
- Statistician
- Data Scientist
- Biostatistician
- Actuary
- Quantitative Risk Analyst
Advanced
- Lead Data Scientist
- Senior Statistician
- Director of Analytics
- Chief Data Officer
Pipeline Insight
Graduates often start in entry-level analytical roles before moving into specialized mid-career positions as they gain experience. Those who advance typically exhibit strong problem-solving skills and the ability to adapt to new statistical technologies.
Career Outcomes
Graduates with a degree in Statistics can pursue career paths as Statisticians, Data Scientists, Biostatisticians, Actuaries, or Quantitative Analysts, with a robust job market projected to grow by 30% in the coming years. This demand is driven by an increasing reliance on data analysis in sectors like healthcare, finance, and technology, leading to competitive salaries that often start around the median of $104,110.
- Statistician
- Data Scientist
- Biostatistician
- Actuary
- Quantitative Analyst
Compensation Context
The median salary of $104,110 reflects the high demand for skilled statisticians in a competitive job market. Compensation can vary based on geographic location, industry sector, and the complexity of the role, with professionals in finance or tech often earning higher salaries due to the high revenue impact of their work.
Alternative Routes
Similar or competing pathways students consider alongside Statistics:
- Data Science (Bachelor's)
- Mathematics (Bachelor's)
- Computer Science (Bachelor's)
- Statistics Bootcamps
- Online Data Analysis Certifications
Getting In & Timeline
Typical time to complete: 4 years full-time
- High school diploma with a strong background in mathematics
- Standardized test scores (SAT/ACT) may be required
- Letters of recommendation and a personal statement may also be needed
Advice
Focus on building a strong mathematical foundation and seek internships early to gain practical experience in the field.
Is This Degree Worth It?
This degree pays off particularly well in high-demand sectors like tech and finance, where data-driven decision-making is critical. However, it may not be worth the investment for those who are not comfortable with quantitative analysis or who prefer less technical fields.
Schools With Strong Outcomes in Sciences
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.
