If you are comparing an analytics/data science degree New York option, this guide is built to help you evaluate real pathways—not generic rankings. It covers analytics/data science schools in New York and analytics/data science programs in New York, choices by degree level, delivery format, and how to verify program details with federal and official sources.
This page focuses on degree and school pathways, and only references institutions when there is a verified program, outcomes context, or distinctive workforce/research/training initiative worth noting.
How We Keep This Page Current
This page is updated using a source stack that includes O*NET OnLine, BLS Occupational Outlook Handbook (OOH), BLS OEWS, CareerOneStop, College Navigator/NCES-IPEDS resources, College Scorecard resources, and official New York institution or state pages.
School and program claims are checked against federal tools (College Navigator / College Scorecard context) and then cross-checked with official college or university program pages before inclusion.
Time-sensitive labor market and wage claims are reviewed periodically; if a claim cannot be verified with a current authoritative source, it is revised or removed.
Quick Facts About Analytics/Data Science Education In New York
- New York wage snapshot (Data Scientists): O*NET’s New York local wages page (using BLS wage data) lists an average annual wage of $125,400 for data scientists in New York.
- New York jobs snapshot (Data Scientists): CareerOneStop’s New York occupation profile snippet reports 16,280 data scientists employed in New York.
- National outlook context: BLS OOH projects data scientist employment to grow 34% from 2024 to 2034, much faster than average.
- Skills and tasks students should expect: O*NET describes data scientist work around analyzing large datasets, building models, and communicating findings—useful for evaluating whether a curriculum covers statistics, programming, and data visualization.
- Program naming varies: NCES CIP identifies separate categories for 30.70 Data Science and 30.71 Data Analytics (including Business Analytics and Data Visualization), which is why program titles can differ across schools.
- Federal school research tools exist: College Navigator (IPEDS-based) and College Scorecard are built to help students compare institutions, but they measure different things and should be used together.
Analytics/Data Science Workforce And Career Context In New York
BLS Occupational Outlook Handbook (OOH) describes data scientists as professionals who use analytical tools and techniques to derive insights from data, and it lists a bachelor’s degree as the typical entry-level education for the occupation. BLS also projects strong national growth for this role (34% from 2024–2034), which is useful context when comparing New York degree pathways.
For New York-specific wage and employment context, BLS OEWS is the official state wage/employment framework, and New York State’s labor site also notes OEWS as the source for statewide occupational wage estimates. For a student-facing New York wage benchmark, O*NET’s New York local wages page reports an average annual wage of $125,400 for data scientists in the state.
CareerOneStop complements that with local career framing. Its New York occupation profile snippet reports 16,280 data scientists employed in the state and an average salary figure, which is useful for high-level state context when you are comparing program costs and timelines.
O*NET is especially useful for curriculum alignment: it emphasizes data analysis, modeling, and interpretation work, which maps directly to what strong programs should teach (statistics, SQL/database work, programming, and visualization/communication).
OOH, OEWS, ONET, and CareerOneStop measure different things. OOH provides national occupation outlook and typical education, OEWS provides state wage/employment estimates, CareerOneStop packages local career views, and ONET focuses on tasks/skills/work activities. Use them together for context, not as interchangeable metrics.
Related Resources
Analytics/Data Science Degree Pathways In New York
This section is designed to help readers compare analytics/data science degree pathways in New York by level, format, and expected outcomes. It is education-first and does not rank schools.
Associate degrees
Associate-level analytics or data-focused programs in New York are often a practical starting point for students who want lower upfront cost, a faster timeline, or a transfer route into a bachelor’s program. Look for foundational coursework in statistics, data literacy, spreadsheets, introductory programming, SQL/database concepts, and data visualization.
For this pathway, transfer planning matters as much as course titles. Before enrolling, confirm whether the associate program is designed for transfer into a four-year analytics, data science, computer science, information systems, or applied math pathway.
Check modality and scheduling carefully (day/evening, part-time, online/hybrid). “Online” may still include in-person labs or proctored components, depending on the institution.
To verify availability, use College Navigator for institution-level program listings and then confirm the exact curriculum on the official school page. College Navigator is IPEDS-based, so it is strong for broad verification, while the official page is better for current course sequencing and delivery details.
What to confirm before enrolling:
- Whether the curriculum includes transferable math/statistics coursework
- Whether SQL and programming are included early
- Whether credits are designed to stack into a bachelor’s degree
- Whether the delivery format is fully online or hybrid
- Whether advising supports transfer planning
Bachelor’s degrees
A bachelor’s pathway is often the standard route for analytics/data science roles because BLS OOH lists a bachelor’s degree as the typical entry-level education for data scientists.
When comparing an analytics bachelor’s degree in New York, focus on curriculum depth and sequencing:
- statistics and probability
- programming (often Python and/or R)
- databases and data management
- analytics methods/modeling
- visualization and communication
- applied project work
Applied learning matters. Strong programs usually include capstones, practicum projects, or employer-connected work that helps students build a portfolio.
Program names vary widely (data science, analytics, applied analytics, business analytics). NCES CIP categories show why: Data Science (30.70) and Data Analytics (30.71) are separate classifications, and Business Analytics/Data Visualization can be coded under the analytics family. Compare curriculum and outcomes—not title alone.
For format and school comparison, use College Navigator to verify institutional offerings and degree levels, then use College Scorecard for institution-level outcomes/cost context (completion, earnings, debt) with caution because many Scorecard metrics are school-level rather than program-level.
Optional example (verified program page): NYU’s Center for Data Science publishes its data science degree offerings and admissions details on an official program page, which is the right place to confirm current requirements and structure after using federal tools for initial research.
Master’s degrees
A master’s in data science in New York can be a good fit for career changers, analysts moving into more technical roles, or professionals seeking deeper modeling/ML training.
Compare programs by orientation:
- Technical: heavier math, statistics, machine learning, computing
- Applied/professional: more business-facing analytics, decision support, and implementation
- Hybrid/interdisciplinary: mixes technical core with domain applications
Admissions prerequisites vary significantly. Some programs expect calculus, linear algebra, probability, and programming experience; others offer bridge courses or are more flexible for working professionals.
Verify delivery format and pacing on the official program page (full-time, part-time, online, hybrid). This matters more than marketing labels.
Project expectations are also important. Look for capstones, industry projects, or applied research experiences—especially if you need a portfolio to support a career pivot.
Official example (source-backed): Columbia Data Science Institute’s education page lists graduate options (MS, PhD specialization, and certification), and it explicitly notes part-time, full-time, and online study options plus hands-on learning language.
Certifications And Workforce Programs
Short-term options can be useful when you need skills quickly or want to test fit before committing to a degree. This includes:
- credit-bearing college certificates
- university continuing education programs
- workforce-focused training options
- stackable pathways that may later apply to a degree
For a data analytics certificate New York option, verify:
- curriculum coverage (stats, SQL, programming, visualization)
- tools/software used
- project/portfolio requirements
- transferability into a degree program
- format and pacing (online/hybrid/evening)
Use O*NET’s tasks/skills profile as a practical screening checklist. If a short program does not cover core data analysis, data handling, and communication of results, it may not prepare you well for entry-level analytics work.
Official example (source-backed): Columbia DSI also lists a Certification of Professional Achievement in Data Sciences, which illustrates a graduate-level certificate route within a larger data science ecosystem.
Program Naming And CIP Alignment (IPEDS/CIP Guidance)
Analytics/data science programs in New York may be labeled as data science, data analytics, business analytics, applied analytics, or even appear within interdisciplinary or computing-related departments.
NCES CIP helps standardize this. The CIP taxonomy includes:
- 30.7001 Data Science, General
- 30.7101 Data Analytics, General
- 30.7102 Business Analytics
- 30.7103 Data Visualization
This is one reason program titles alone can be misleading. Compare curriculum, delivery format, and outcomes context—not title alone.
How To Compare Analytics/Data Science Programs In New York
Program comparison checklist
- Does the curriculum match your goal (analytics, data science, business analytics, or broader quantitative training)?
- Are statistics, programming, SQL/databases, and visualization all included?
- Does the program include applied learning (capstone, practicum, employer projects, internship)?
- Is the format truly online, hybrid, or campus-based?
- Is the schedule workable (full-time, part-time, evening)?
- If starting at the associate level, is there a clear transfer path?
- Are there labs, centers, or faculty research groups relevant to analytics/data science?
- Can you verify the school and degree level in College Navigator (or official institutional listings)?
- Can you use College Scorecard for school-level outcomes/cost context (while recognizing it may not be program-level)?
- Is total cost clear (tuition, fees, time to completion, part-time options)?
- Is there advising/career support for internships, portfolio development, or job search?
Pathway comparison table
| Pathway | Typical timeline | Best for | What to verify | Key source(s) to check |
| Associate degree | ~2 years | Lower-cost start, transfer-focused students | Transferability, foundational math/stats, SQL/programming coverage, delivery format | College Navigator, official college page, SUNY transfer resources |
| Bachelor’s degree | ~4 years | Students seeking standard entry pathway | Curriculum depth, capstone/internship, format, school-level outcomes context | BLS OOH, College Navigator, College Scorecard, official program page |
| Master’s degree | ~1–2 years | Career changers or advanced specialization | Prereqs, technical vs applied focus, capstone, pacing, online/hybrid format | Official program page, College Navigator, College Scorecard |
| Certificate/workforce training | Weeks to ~1 year | Upskilling, stackable credentials, trial pathway | Skills coverage, tools, project work, transferability | O*NET, official program page, College Navigator (if credit-bearing) |
Online Vs. Campus Analytics/Data Science Programs In New York
Online programs can be a better fit if you are working full-time, need schedule flexibility, or want to stay in your current location while earning a credential. They are especially useful when the program offers part-time pacing and strong remote project support.
Campus or hybrid programs may be a better fit if you want in-person lab access, closer faculty interaction, or easier participation in on-campus research and networking.
Always verify format in two places:
- a federal tool (College Navigator or Scorecard context), and
- the official program page.
This helps catch differences between institution-level online offerings and the specific program’s actual delivery.
Also note that “online” may still include hybrid or occasional in-person requirements, and format can vary by course or term.
School And Program Research Tips For New York (College Navigator + Scorecard)
- Use College Navigator first to confirm the institution exists, basic school facts, and whether the school reports relevant program offerings through IPEDS-based data. It is a strong starting point for school verification.
- Use College Scorecard next for school-level outcomes/cost context (earnings, debt, completion), but read carefully because many metrics are institution-level and may not reflect one specific analytics/data science program.
- Cross-check with official program pages for current curriculum, admissions prerequisites, and format details. Federal tools are excellent for comparison and context, while official pages are best for program specifics.
- Use CIP guidance when titles vary. “Data science,” “data analytics,” and “business analytics” may map to different CIP categories, so compare coursework and outcomes rather than titles alone.
Unique New York Analytics/Data Science Initiatives
SUNY Transfer Paths
- What it is: SUNY publishes statewide transfer pathway guidance designed to help students move from one SUNY institution to another with clearer credit alignment. This is not data-science-specific, but it is highly relevant for associate-to-bachelor’s planning in New York.
- Why it matters for students: It can make community college-to-bachelor’s pathway planning more predictable for analytics/data science students.
Columbia Data Science Institute (Education + research center model)
- What it is: Columbia’s Data Science Institute lists multiple education pathways (MS, PhD specialization, certification) and also publishes research centers and student-facing education infrastructure.
- Why it matters for students: It is a clear example of a New York program ecosystem where education, research, and applied domains are connected.
New York State Department of Labor occupational wage resources (OEWS)
- What it is: New York’s Department of Labor publishes occupational wage resources and states that its occupational wages are based on the OEWS program, which estimates employment and wages across New York job titles.
- Why it matters for students: It gives a New York-specific wage reference point you can use when comparing program cost and timeline.
Questions To Ask Before Enrolling In An Analytics/Data Science Program In New York
- Is the curriculum aligned with the skills used in analytics/data science work (statistics, SQL, programming, visualization)?
- Is the program fully online, hybrid, or campus-based—and is that consistent across all required courses?
- Does the program include a capstone, practicum, internship, or employer-sponsored project?
- Are part-time or evening options available if I am working?
- If I start with a certificate or associate degree, can credits transfer or stack into a bachelor’s program?
- What are the admissions prerequisites (math, coding, prior degree/background)?
- Where can I verify school-level outcomes and cost context (College Scorecard) versus program-specific curriculum details (official page)?
- What software/tools are taught, and do they align with O*NET task/skill expectations?
Frequently Asked Questions About Analytics/Data Science Degrees In New York
CareerOneStop’s New York occupation profile snippet reports 16,280 data scientists employed in New York. For broader analytics-related planning, also review related occupations (such as statisticians and operations research roles) because programs may prepare students for more than one job title.
For data scientists specifically, O*NET’s New York local wages page reports an average annual wage of $125,400 (using BLS wage data). CareerOneStop and BLS tools may show different values depending on source year, wage type (mean vs median), or occupation grouping, so compare methodology before using a number in cost/ROI planning.
There is no single “best” degree for everyone. The right path depends on your timeline, budget, math/programming background, and career goals. BLS OOH lists a bachelor’s degree as the typical entry-level education for data scientists, but certificates, associate degrees, and master’s programs can all make sense depending on your pathway.
Yes—New York institutions offer online and hybrid options, but format varies by program. Always verify the exact delivery model on the official program page (not just the institution homepage) and use federal tools for comparison context. Columbia DSI, for example, explicitly references online study options on its education page.
Yes, New York has short-term and certificate-style options, but quality varies. The most reliable way to evaluate them is to check whether they cover core O*NET-aligned skills (data analysis, modeling, communication), include project work, and—if credit-bearing—whether they can stack into a degree.
Strong programs usually teach statistics, programming, data management/SQL, and data visualization, because those map to the work activities and skills emphasized in O*NET and the job functions described by BLS OOH for data scientists.
Nationally, BLS projects data scientist employment growth of 34% (2024–2034), which is much faster than average. In New York, CareerOneStop and state/OEWS wage resources provide local context you can use to evaluate demand alongside program choices.
Yes. An associate degree can be a practical entry point, especially if you plan to transfer into a bachelor’s program later. In New York, statewide transfer tools (such as SUNY transfer pathway resources) can help you plan credits more effectively.
– Associate degree: ~2 years
– Bachelor’s degree: ~4 years
– Master’s degree: ~1–2 years
– Certificate: weeks to ~1 year
Start with College Navigator for school verification and institutional facts, then use College Scorecard for school-level outcomes/cost context, and finally confirm curriculum and format on the official program page. Use CIP guidance when program titles differ.
The exact mix varies, but data scientists and analysts are used across many industries. O*NET and BLS occupational profiles are a better starting point than school marketing pages for understanding transferable skills and roles. Then use local wage/career tools (CareerOneStop and state OEWS resources) for New York context.
Yes, but “entry-level” titles vary (analyst, junior analyst, reporting/BI roles, etc.). A bachelor’s degree is the typical entry education listed for data scientists in BLS OOH, while some students enter through associate-plus-transfer or certificate-plus-experience pathways.
They often overlap, but data science programs may lean more technical (modeling, machine learning, computational methods), while analytics programs may focus more on business decision-making, reporting, and applied analysis. CIP categories also separate Data Science (30.70) and Data Analytics (30.71), which helps explain naming differences.
O*NET OnLine is a federal occupation database that summarizes tasks, skills, tools, and work activities. It helps students compare programs by showing what employers expect in the occupation—so you can check whether a curriculum actually covers those capabilities.
Sources
- U.S. Bureau of Labor Statistics | Data Scientists — Occupational Outlook Handbook | Accessed February 25, 2026
- U.S. Bureau of Labor Statistics | OEWS State Occupational Employment and Wage Estimates | Accessed February 25, 2026
- U.S. Bureau of Labor Statistics | Data Scientists (SOC 15-2051) — OEWS | Accessed February 25, 2026
- O*NET Online | 15-2051.00 — Data Scientists | Accessed February 25, 2026
- O*NET Online | New York Wages: 15-2051.00 — Data Scientists | Accessed February 25, 2026
- CareerOneStop | Occupation Profile for Data Scientists | Accessed February 25, 2026
- NCES / IPEDS CIP | CIP user site | Accessed February 25, 2026
- NCES / College Navigator | College Navigator | Accessed February 25, 2026
- College Scorecard | College Scorecard | Accessed February 25, 2026
- Columbia Data Science Institute | The Data Science Institute | Accessed February 25, 2026
- SUNY | Transfer Paths | Accessed February 25, 2026
- New York State Department of Labor | Occupational Wages | Accessed February 25, 2026