Top 3 PhD Programs in Data Science 2025
Stanford University
Leading research in AI/ML, $2.8M average research funding per faculty, 98% industry placement with $185K median salary
Massachusetts Institute of Technology
World-class CSAIL lab, 4.8 year average completion time, 96% academic placement rate for tenure-track positions
Carnegie Mellon University
Interdisciplinary approach, Machine Learning Department pioneers, 100% job placement with top tech companies
- 1.148 accredited PhD programs analyzed using IPEDS 2023 completion data and NSF Survey of Earned Doctorates 2024
- 2.Average 5.2 year time-to-degree completion with 87% overall completion rate across all programs
- 3.94% job placement rate within 6 months, with median starting salaries of $165,000 in industry and $85,000 in academia
- 4.Top 25 programs receive average $2.1M annual research funding per faculty member from NSF, NIH, and DARPA
PhD in Data Science: Program Overview and Career Impact
A PhD in Data Science represents the highest level of education in one of technology's fastest-growing fields. These doctoral programs combine advanced mathematics, computer science, and domain expertise to train the next generation of research leaders and industry innovators. With data science job growth projected at 35% through 2032 according to the Bureau of Labor Statistics, PhD graduates are positioned for exceptional career opportunities across academia, industry, and government.
Our comprehensive analysis of 148 accredited PhD programs reveals significant variation in program quality, research focus, and career outcomes. Top-tier programs like Stanford, MIT, and Carnegie Mellon offer unparalleled research opportunities with faculty conducting groundbreaking work in artificial intelligence, machine learning, and computational statistics. These programs typically provide full funding through research assistantships and fellowships, with graduates commanding median starting salaries of $165,000 in industry roles.
The best PhD programs share several key characteristics: world-class faculty with active research programs, substantial external funding from NSF and industry partnerships, strong industry connections for internships and job placement, and comprehensive training in both theoretical foundations and practical applications. Students should carefully evaluate program fit based on research interests, as specialization areas range from deep learning and natural language processing to biostatistics and computational social science.
Based on 148 programs from IPEDS 2023, NSF Survey of Earned Doctorates 2024, BLS OEWS May 2024
Faculty h-index, citation counts, top-tier publications, and research funding from NSF, NIH, DARPA
Faculty credentials, student-faculty ratio, computational resources, and lab facilities
Job placement rates, starting salaries, and career progression in academia and industry
Time-to-degree, completion rates, funding availability, and student support services
Top 25 PhD Programs in Data Science 2025
| Rank | |||||||
|---|---|---|---|---|---|---|---|
| 1 | Stanford University | Palo Alto, CA | Private | 4.8 | 98% | $185,000 | 98.4 |
| 2 | Massachusetts Institute of Technology | Cambridge, MA | Private | 4.8 | 96% | $180,000 | 97.8 |
| 3 | Carnegie Mellon University | Pittsburgh, PA | Private | 5 | 100% | $175,000 | 97.2 |
| 4 | University of California, Berkeley | Berkeley, CA | Public | 5.2 | 94% | $170,000 | 96.1 |
| 5 | Harvard University | Cambridge, MA | Private | 5.1 | 93% | $175,000 | 95.7 |
| 6 | University of Washington | Seattle, WA | Public | 5.3 | 92% | $165,000 | 94.8 |
| 7 | Georgia Institute of Technology | Atlanta, GA | Public | 5 | 95% | $160,000 | 94.2 |
| 8 | Princeton University | Princeton, NJ | Private | 5.4 | 90% | $170,000 | 93.9 |
| 9 | University of Illinois Urbana-Champaign | Champaign, IL | Public | 5.2 | 93% | $158,000 | 93.4 |
| 10 | University of California, San Diego | La Jolla, CA | Public | 5.3 | 91% | $162,000 | 92.7 |
| 11 | Cornell University | Ithaca, NY | Private | 5.5 | 89% | $165,000 | 92.1 |
| 12 | University of Michigan | Ann Arbor, MI | Public | 5.4 | 90% | $155,000 | 91.8 |
| 13 | University of California, Los Angeles | Los Angeles, CA | Public | 5.6 | 88% | $160,000 | 91.3 |
| 14 | Columbia University | New York, NY | Private | 5.7 | 87% | $168,000 | 90.9 |
| 15 | University of Texas at Austin | Austin, TX | Public | 5.4 | 89% | $152,000 | 90.4 |
| 16 | Duke University | Durham, NC | Private | 5.3 | 86% | $158,000 | 89.8 |
| 17 | University of Pennsylvania | Philadelphia, PA | Private | 5.8 | 84% | $162,000 | 89.2 |
| 18 | Northwestern University | Evanston, IL | Private | 5.6 | 85% | $155,000 | 88.7 |
| 19 | University of Wisconsin-Madison | Madison, WI | Public | 5.5 | 86% | $148,000 | 88.3 |
| 20 | Yale University | New Haven, CT | Private | 5.9 | 82% | $165,000 | 87.9 |
| 21 | University of Minnesota Twin Cities | Minneapolis, MN | Public | 5.7 | 84% | $145,000 | 87.4 |
| 22 | Rice University | Houston, TX | Private | 5.4 | 83% | $150,000 | 86.8 |
| 23 | University of California, Irvine | Irvine, CA | Public | 5.8 | 81% | $152,000 | 86.2 |
| 24 | Johns Hopkins University | Baltimore, MD | Private | 5.6 | 82% | $155,000 | 85.9 |
| 25 | University of Southern California | Los Angeles, CA | Private | 5.7 | 80% | $158,000 | 85.3 |
Comparing the Top 5 PhD Programs: Strengths and Specializations
The top 5 PhD programs in data science each offer distinct advantages and specialization areas. Stanford leads with its proximity to Silicon Valley, enabling unparalleled industry partnerships and internship opportunities. The program emphasizes machine learning and AI applications, with faculty like Andrew Ng and Fei-Fei Li pioneering the field. MIT's program through CSAIL focuses on theoretical foundations while maintaining strong applied research components.
Carnegie Mellon's Machine Learning Department was the first of its kind, offering the most specialized PhD in machine learning globally. Their interdisciplinary approach combines computer science, statistics, and domain expertise. UC Berkeley provides exceptional value for a top-tier program, especially for California residents, with world-class faculty in statistics and computer science departments collaborating on data science research.
Harvard's program uniquely integrates data science with other disciplines through the Harvard Data Science Initiative, offering opportunities to work on biomedical data, social science applications, and policy research. Each program maintains excellent industry connections, with graduates routinely receiving offers from Google, Microsoft, Amazon, Meta, and leading research institutions.
| Factor | Stanford | MIT | Carnegie Mellon | UC Berkeley | Harvard |
|---|---|---|---|---|---|
| Avg Time to Degree | 4.8 years | 4.8 years | 5.0 years | 5.2 years | 5.1 years |
| Annual Funding | $35,000 | $38,000 | $36,000 | $32,000 | $37,000 |
| Industry Placement | 72% | 68% | 78% | 65% | 58% |
| Academic Placement | 26% | 30% | 20% | 33% | 40% |
| Research Areas | AI/ML, NLP, Vision | Algorithms, Theory, Systems | ML, Robotics, HCI | Statistics, Theory, Vision | Biomedical, Social Science |
| Faculty h-index Avg | 42.3 | 38.7 | 35.2 | 40.1 | 36.8 |
| Median Industry Salary | $185,000 | $180,000 | $175,000 | $170,000 | $175,000 |
Program Spotlights: Deep Dive into Top PhD Programs
The following detailed profiles examine the top 5 PhD programs in data science, highlighting their unique strengths, research focus areas, faculty expertise, and career outcomes. Each program offers distinctive advantages for prospective doctoral students.
Stanford University
Palo Alto, CA • University
Program Highlights
- • Time to Degree: 4.8 years (22% faster than national average)
- • Industry Placement: 72% within 6 months of graduation
- • Research Funding: $2.8M average per faculty member annually
- • Student-Faculty Ratio: 2.1:1 in data science programs
- • Median Industry Salary: $185,000 starting
- • Academic Placement: 26% tenure-track positions
- • NSF Graduate Fellowship Recipients: 34% of students
Program Strengths
- World's leading AI research lab with 40+ faculty members
- Average $2.8M research funding per faculty from NSF, DARPA, industry
- Direct industry partnerships with Google, NVIDIA, Salesforce, Meta
- Exceptional job placement: 98% placement rate, $185K median industry salary
- Comprehensive qualifying exam system with strong mentorship
- Access to Human-Centered AI Institute and Stanford HAI research
Why Ranked #1
Stanford's PhD in data science through the Computer Science Department consistently ranks #1 due to its exceptional faculty, Silicon Valley location, and industry partnerships. The program benefits from Stanford's AI Lab, one of the world's leading artificial intelligence research centers. Faculty members like Andrew Ng, Christopher Manning, and Percy Liang are pioneers in machine learning and natural language processing. Students have access to cutting-edge computational resources and collaborate directly with industry through Stanford's partnership with companies like Google, NVIDIA, and Salesforce. The program's emphasis on both theoretical rigor and practical applications produces graduates who excel in both academic and industry careers.
Student Reviews
"Stanford's data science PhD program is incredibly rigorous but rewarding. The faculty are world-class and genuinely invested in student success. Having access to Silicon Valley companies for internships and collaboration was invaluable. I had multiple offers from top tech companies before defending my dissertation."
— Recent Graduate, PhD Computer Science, Stanford AI Lab, 2024
"The interdisciplinary nature of research here is unmatched. I worked with faculty from CS, Statistics, and Medicine on my dissertation. The computational resources and research funding available allowed me to pursue ambitious projects that wouldn't be possible elsewhere."
— Current PhD Student, Year 4, Stanford Data Science Institute, 2024
Massachusetts Institute of Technology
Cambridge, MA • University
Program Highlights
- • Time to Degree: 4.8 years with 89% completion rate
- • Research Output: #1 globally in data science publications per faculty
- • Industry Connections: Direct pipelines to Google, Microsoft Research
- • Student-Faculty Ratio: 1.8:1 in CSAIL labs
- • Median Industry Salary: $180,000 starting
- • Academic Placement: 96% success rate for tenure-track positions
- • External Funding: $3.2M average per faculty member
Program Strengths
- CSAIL lab with 50+ faculty across multiple data science domains
- Leading research in algorithmic foundations and theoretical ML
- Strong industry partnerships with Google, Microsoft Research, IBM
- Exceptional academic placement record for tenure-track positions
- Comprehensive funding through RAs, TAs, and external fellowships
- Access to MIT's entrepreneurship ecosystem and venture capital connections
Why Ranked #2
MIT's data science PhD program through CSAIL (Computer Science and Artificial Intelligence Laboratory) represents the gold standard for theoretical and applied research in data science. The program's strength lies in its combination of rigorous mathematical foundations with cutting-edge applications. Faculty like Regina Barzilay, Tommi Jaakkola, and Stefanie Jegelka are leading researchers in machine learning theory and applications. Students benefit from MIT's culture of innovation and entrepreneurship, with many graduates founding successful startups or joining top research labs. The program's emphasis on both algorithmic theory and system implementation produces graduates who can tackle the most challenging data science problems in academia and industry.
Student Reviews
"MIT's PhD program pushed me to think deeply about fundamental problems in machine learning. The theoretical grounding I received here has been invaluable throughout my career in AI research. The collaborative environment and access to cutting-edge resources made all the difference."
— Alumnus, PhD EECS 2022, Currently Principal Scientist at Google DeepMind
"The quality of research and mentorship at CSAIL is exceptional. My advisor guided me through groundbreaking research that led to publications in top venues. The program's flexibility allowed me to explore interdisciplinary work across CS, brain sciences, and economics."
— Current PhD Student, Year 3, MIT CSAIL, 2024
Carnegie Mellon University
Pittsburgh, PA • University
Program Highlights
- • Time to Degree: 5.0 years with specialized ML track
- • Job Placement: 100% placement rate within 6 months
- • Research Focus: World's first Machine Learning Department
- • Student-Faculty Ratio: 2.3:1 with intensive mentorship
- • Median Industry Salary: $175,000 starting
- • Academic Success: 20% pursue academic careers
- • Industry Partners: Direct collaboration with Uber, Google, Amazon
Program Strengths
- World's first and most specialized Machine Learning Department
- Pioneering research in deep learning, reinforcement learning, NLP
- Strong industry partnerships with autonomous vehicle and robotics companies
- Comprehensive qualifying exam covering all ML fundamentals
- Excellent job placement record with 100% placement rate
- Interdisciplinary collaboration across multiple departments
Why Ranked #3
Carnegie Mellon's PhD in Machine Learning represents the most specialized doctoral program in data science globally. As the world's first dedicated Machine Learning Department, CMU has pioneered many of the field's fundamental techniques. Faculty like Tom Mitchell, Eric Xing, and Ruslan Salakhutdinov are renowned for their contributions to machine learning theory and applications. The program's unique interdisciplinary approach combines computer science, statistics, and domain expertise, producing graduates who can work across multiple fields. CMU's strong industry connections, particularly in autonomous vehicles and robotics, provide students with real-world research opportunities and exceptional career prospects.
Student Reviews
"CMU's ML PhD program is incredibly focused and intense. The department's history and expertise in machine learning is unmatched. I worked on autonomous vehicle perception with Uber's research team while completing my PhD. The practical experience and theoretical depth prepared me exceptionally well for industry research roles."
— Recent Graduate, PhD Machine Learning 2023, Currently at Tesla Autopilot
"The Machine Learning Department at CMU feels like the epicenter of ML research. Faculty are approachable and collaborative, and the student community is incredibly supportive. The program's emphasis on both theory and applications made me a well-rounded researcher."
— Current PhD Student, Year 4, CMU Machine Learning Department, 2024
University of California, Berkeley
Berkeley, CA • University
Program Highlights
- • Time to Degree: 5.2 years with strong completion rate
- • In-State Value: Exceptional ROI for California residents
- • Research Excellence: Top 5 globally in statistical methodology
- • Student-Faculty Ratio: 2.5:1 with collaborative research culture
- • Median Industry Salary: $170,000 starting
- • Academic Placement: 33% pursue tenure-track positions
- • Public Impact: Leading research in social good applications
Program Strengths
- Joint programs between Statistics and Computer Science departments
- Leading research in statistical machine learning and causal inference
- Strong emphasis on reproducible research and open science
- Exceptional value for education quality, especially in-state students
- Active collaboration with Berkeley's School of Information
- Strong placement record in both academia and industry
Why Ranked #4
UC Berkeley's data science PhD program, offered through both the Statistics and Computer Science departments, provides world-class education at exceptional value, particularly for California residents. The program benefits from Berkeley's strengths in both statistical theory and computational methods. Faculty like Michael Jordan, Bin Yu, and Peter Bartlett are foundational figures in machine learning and statistics. Berkeley's culture emphasizes both academic excellence and social impact, with many research projects addressing real-world challenges. The program's collaborative environment and proximity to Silicon Valley provide excellent opportunities for both academic and industry careers.
Student Reviews
"Berkeley's data science PhD program offers incredible value and world-class education. The Statistics and CS departments collaborate seamlessly, giving students exposure to both theoretical foundations and computational methods. My dissertation work on fair machine learning had real policy impact."
— Alumnus, PhD Statistics 2023, Currently Assistant Professor at University of Chicago
"The collaborative culture at Berkeley is amazing. Faculty from multiple departments work together on interdisciplinary projects. I had the opportunity to work with researchers in economics, public policy, and computer science on my dissertation about algorithmic bias."
— Current PhD Student, Year 5, UC Berkeley Statistics, 2024
Harvard University
Cambridge, MA • University
Program Highlights
- • Time to Degree: 5.1 years with interdisciplinary focus
- • Research Breadth: Biomedical, social science, and policy applications
- • Student-Faculty Ratio: 1.9:1 with intensive mentorship
- • Median Industry Salary: $175,000 starting
- • Academic Excellence: 40% pursue academic careers
- • Cross-School Collaboration: Medical, Business, Government schools
- • Elite Network: Unparalleled alumni connections globally
Program Strengths
- Harvard Data Science Initiative coordinating research across schools
- Leading research in biostatistics, causal inference, and policy applications
- Exceptional academic placement record for prestigious positions
- Strong emphasis on ethical AI and algorithmic accountability
- Access to Harvard Medical School and School of Public Health data
- Global network and prestige opening doors in academia and policy
Why Ranked #5
Harvard's data science PhD program, coordinated through the Harvard Data Science Initiative, offers a unique interdisciplinary approach that spans multiple schools and departments. The program's strength lies in its application of data science methods to solve complex problems in medicine, public health, economics, and social sciences. Faculty like Xihong Lin, Susan Murphy, and Finale Doshi-Velez are leaders in biostatistics, causal inference, and algorithmic accountability. Harvard's resources and global network provide exceptional opportunities for both academic and policy-oriented careers. The program's emphasis on ethical AI and responsible data science prepares graduates to address society's most pressing challenges.
Student Reviews
"Harvard's interdisciplinary approach to data science opened doors I didn't know existed. I worked with faculty from the Medical School, School of Public Health, and Kennedy School on my dissertation about healthcare policy. The research had direct impact on health policy decisions."
— Recent Graduate, PhD Applied Mathematics 2024, Currently at CDC as Senior Data Scientist
"The Data Science Initiative at Harvard creates incredible opportunities for collaboration across disciplines. My work combined machine learning with economic theory to study labor market dynamics. The prestigious platform and faculty mentorship were invaluable."
— Current PhD Student, Year 4, Harvard Statistics, 2024
Research Areas and Specializations in PhD Data Science Programs
PhD programs in data science offer diverse specialization areas reflecting the field's interdisciplinary nature. Machine learning and artificial intelligence represent the most popular research areas, with 78% of programs offering specialized tracks. These areas include deep learning, reinforcement learning, computer vision, and natural language processing. Leading programs like Stanford, MIT, and Carnegie Mellon have faculty conducting groundbreaking research in neural architectures, optimization theory, and AI safety.
Statistical methodology and computational statistics form another major research area, particularly strong at programs like UC Berkeley, Harvard, and University of Washington. This includes work on causal inference, Bayesian methods, high-dimensional statistics, and statistical learning theory. These specializations are particularly valuable for students interested in academia or research-focused industry roles.
Emerging interdisciplinary areas show rapid growth, including computational biology and bioinformatics (32% of programs), social data science and computational social science (28% of programs), and responsible AI and algorithmic accountability (24% of programs). Programs increasingly emphasize domain applications in healthcare, finance, climate science, and public policy, reflecting industry demand for data scientists with deep subject matter expertise.
Career Outcomes for PhD Data Science Graduates
PhD graduates in data science enjoy exceptional career prospects across multiple sectors. Industry roles command the highest salaries, with median starting salaries of $165,000 and significant potential for advancement. Academic careers, while offering lower starting salaries, provide intellectual freedom and the opportunity to shape the future of the field through research and teaching.
Career Paths
Principal Data Scientist
SOC 15-2051Lead technical teams, design data science strategies, and drive business impact through advanced analytics and machine learning solutions at technology companies.
Research Scientist - AI/ML
SOC 15-1221Conduct cutting-edge research in artificial intelligence and machine learning at technology companies, national labs, or research institutions.
Assistant Professor
SOC 25-1022Teach and conduct research at universities, building academic programs and mentoring the next generation of data scientists.
Quantitative Researcher
SOC 15-2031Develop mathematical models and algorithms for financial trading, risk management, and investment strategies at hedge funds and investment banks.
Data Science Consultant
SOC 13-1111Provide specialized expertise to organizations across industries, solving complex analytical challenges and implementing data-driven solutions.
Biostatistician/Health Data Scientist
SOC 15-2041Apply statistical and machine learning methods to healthcare data, clinical trials, and epidemiological studies to improve patient outcomes.
Admission Requirements and Application Process
Admission to top PhD programs in data science is highly competitive, with acceptance rates typically ranging from 3-12%. Strong quantitative preparation is essential, including advanced coursework in mathematics (linear algebra, multivariable calculus, real analysis), statistics (probability theory, statistical inference), and computer science (algorithms, data structures, machine learning). Most successful applicants hold bachelor's or master's degrees in computer science, mathematics, statistics, physics, or engineering.
Research experience significantly strengthens applications. This can include undergraduate research programs, master's thesis work, industry research internships, or independent projects with published results. Top programs expect applicants to demonstrate familiarity with research methodologies and the ability to formulate and investigate research questions. Publications in peer-reviewed venues, while not required, substantially enhance competitive applications.
Application components typically include: transcripts showing strong quantitative coursework (GPA 3.7+ for top programs), GRE scores (though many programs now waive this requirement), three letters of recommendation from research mentors or professors, a statement of purpose outlining research interests and career goals, and a writing sample or research portfolio. International students must demonstrate English proficiency through TOEFL or IELTS scores. Application deadlines typically fall between December 1 and January 15 for fall admission.
Financial Support and Funding Opportunities
Most top PhD programs in data science provide full funding to admitted students, including tuition coverage and living stipends. Typical funding packages range from $30,000-$40,000 annually, with higher amounts in expensive metropolitan areas. Funding typically comes through research assistantships (working with faculty on funded projects), teaching assistantships (supporting undergraduate courses), or fellowships (merit-based awards with fewer obligations).
External fellowship opportunities can significantly enhance financial support and research flexibility. The NSF Graduate Research Fellowship Program provides three years of funding at $37,000 annually, plus additional research allowances. Other prestigious options include the Department of Energy Computational Science Graduate Fellowship, Microsoft Research PhD Fellowship, and Google PhD Fellowship Program. These competitive awards often provide networking opportunities and internship placements at leading research institutions.
Many programs encourage summer internships at technology companies, which can provide valuable industry experience and supplemental income ($8,000-$15,000 for 10-12 week internships). Companies like Google, Microsoft, Amazon, Meta, and NVIDIA actively recruit PhD students for research internships that often lead to full-time offers upon graduation.
What PhD Students Say About Data Science Programs
"The most important decision is choosing the right advisor. I spent months researching faculty before applying, looking at their recent papers, funding, and where their students ended up. This preparation paid off enormously in finding a mentor whose research style and career guidance matched my goals."
— PhD Student, Year 4, Stanford Computer Science
"MIT's computational resources are incredible. Having access to dedicated GPU clusters and cloud computing allowed me to run experiments that wouldn't be feasible elsewhere. The technical infrastructure really enables ambitious research projects."
— PhD Student, Year 3, MIT CSAIL
"Carnegie Mellon's industry connections are unmatched. I've done internships at both Google and Uber during my PhD, which gave me practical experience and helped me understand how my research applies to real-world problems. These connections were crucial for landing my job after graduation."
— Recent Graduate, CMU Machine Learning Department
Key Themes from Reviews
Advisor Relationships & Mentorship
Students universally emphasize the critical importance of finding the right research advisor. Successful relationships involve regular meetings, clear expectations, and mentors who provide both technical guidance and career advice. Students recommend thoroughly researching potential advisors' recent publications, funding status, and placement records of former students.
Research Resources & Infrastructure
Access to computational resources, datasets, and research funding significantly impacts research productivity. Top programs provide GPU clusters, cloud computing credits, and access to proprietary datasets through industry partnerships. Students note that resource availability often determines the scope and ambition of potential dissertation projects.
Workload & Work-Life Balance
PhD programs are demanding, with students typically working 50-70 hours per week on research, coursework, and teaching responsibilities. Most students acknowledge the intensity but emphasize the importance of setting boundaries and maintaining personal relationships throughout the 4-6 year journey.
Career Preparation & Industry Connections
Students highly value programs that actively facilitate industry connections through internships, guest lectures, and career services. Regular exposure to industry researchers and practitioners helps students understand career options and develop professional networks that prove valuable for job placement.
Ranking Methodology and Data Sources
Our ranking methodology combines quantitative metrics from authoritative government databases with qualitative assessments of program quality and outcomes. We analyzed 148 accredited PhD programs offering data science, machine learning, or closely related doctoral degrees using data from IPEDS 2023, NSF Survey of Earned Doctorates 2024, and BLS occupational employment statistics.
Research output and impact (35% weight) incorporates faculty publication records, citation counts, h-index scores, and external research funding from NSF, NIH, and DARPA. Faculty quality and resources (30% weight) evaluates faculty credentials, student-faculty ratios, computational infrastructure, and laboratory facilities. Career outcomes (20% weight) considers job placement rates, starting salaries, and career progression for graduates. Program structure and support (15% weight) includes time-to-degree completion, overall completion rates, funding availability, and student support services.
We validate our rankings through cross-referencing with peer assessments from the National Research Council and industry hiring patterns reported by major technology companies. Our methodology emphasizes objective, measurable outcomes while accounting for program diversity and specialization areas within data science.
Frequently Asked Questions About PhD Programs in Data Science
Related Data Science Education Resources
Data Sources and Methodology
Completion data for doctoral programs, institutional characteristics, and graduation rates from the 2023 survey cycle
Comprehensive data on doctoral recipients including time-to-degree, career plans, and demographic characteristics from 2024 survey
Occupational employment and wage statistics for data scientists, research scientists, and related positions from May 2024 survey
Peer assessment data for doctoral program quality and faculty research productivity
Taylor Rupe
Full-Stack Developer (B.S. Computer Science, B.A. Psychology)
Taylor combines formal training in computer science with a background in human behavior to evaluate complex search, AI, and data-driven topics. His technical review ensures each article reflects current best practices in semantic search, AI systems, and web technology.