A working adult deciding between an online bachelor’s, master’s, analytics, or computer science route should begin with academic starting point and intended role family. A bachelor’s route generally builds foundational college and major requirements. A master’s route generally assumes prior undergraduate preparation and moves more quickly into advanced or specialized work. Analytics and computer science routes may also support data-focused goals when their required curricula contain the relevant statistics, programming, database, modeling, and applied-project work.
The choice is not a ranking between degree titles. It is a source-based decision about which credential level, curriculum, prerequisite structure, delivery format, and occupational context match the learner’s current preparation.
Start with the learner’s academic starting point
A bachelor’s route is usually the place to begin when the learner does not already hold a bachelor’s degree or needs a full undergraduate pathway. The official catalog may combine general education, foundational mathematics, programming, statistics, database work, major requirements, electives, and a capstone or project. Transfer-credit evaluation can change the remaining plan, but only after the institution applies prior coursework to the selected degree.
A master’s route generally requires a completed bachelor’s degree and may expect previous quantitative or technical preparation. Program-specific admissions pages may identify prerequisites in statistics, calculus, linear algebra, programming, databases, computer science, or related areas. Other programs may provide foundation or bridge coursework. These requirements must be verified on the exact admissions and catalog pages for the program being researched.
Before choosing a level, answer four questions:
Does the learner already hold the prior credential required for admission?
Does the admissions page list prerequisites that are already complete?
Would bridge or foundation coursework add time or cost?
Does the desired curriculum begin with foundations or assume them?
These questions prevent a student from choosing a graduate title that assumes missing preparation or an undergraduate route that repeats substantial prior learning.
Compare field routes by required curriculum
Data science, data analytics, business analytics, and computer science are related academic routes, but their titles do not establish identical curricula. NCES classifies data science, data analytics, computer science, information science, statistics, management information systems, and related fields as distinct instructional categories. Those classifications help organize research, while the program catalog determines what a specific degree requires.
Data science routes
A data science route may combine statistics, programming, databases, data preparation, machine learning, visualization, ethics, and applied projects. The strongest evidence is the required curriculum, not the presence of “data science” in the title. A working adult should identify which technical and quantitative areas are required, which are electives, and whether the sequence assumes prior preparation.
Data analytics routes
A data analytics route may emphasize collecting, preparing, analyzing, interpreting, and communicating data for decisions. Some programs are highly technical. Others place more weight on reporting, visualization, business intelligence, or applied analysis. Check whether programming, SQL, statistics, databases, modeling, and project work are required or optional.
Business analytics routes
A business analytics route may connect quantitative methods with operations, marketing, finance, management, supply chain, or organizational decision-making. The academic unit and course requirements matter. A business-school location does not prove that a program is less technical, and an analytics title does not prove a business focus.
Computer science routes
A computer science route may be relevant when the catalog includes databases, artificial intelligence, machine learning, statistics, data mining, visualization, or a formal data science concentration. A general computer science title alone does not establish data-science preparation. Confirm that the data-focused coursework is available in the online route and fits within the degree sequence.
Use occupational sources to define the research target
Government occupational sources provide neutral context for deciding which curriculum areas deserve attention. The Bureau of Labor Statistics describes data scientists as workers who use analytical tools and techniques to extract meaningful insights from data. BLS describes operations research analysts as workers who use mathematics and logic to help organizations solve complex issues. Market research analysts study consumer preferences and business conditions, while database administrators and architects work with systems used to store and secure data.
These profiles do not tell a student which degree to choose and do not guarantee employment. They help define the task families to compare against the curriculum.
For example:
Data scientist research may emphasize statistics, programming, databases, machine learning, model evaluation, visualization, and applied projects.
Data analyst or business intelligence research may emphasize SQL, data cleaning, dashboards, reporting, visualization, and communication.
Operations research may emphasize optimization, simulation, probability, mathematical modeling, and decision analysis.
Market research may emphasize statistics, research methods, consumer or market data, reporting, and communication.
Database-focused research may emphasize database design, SQL, data modeling, warehousing, governance, performance, and security.
The program catalog should show whether these areas are required, available only as electives, or absent from the route.
Account for admissions and prerequisite differences
A bachelor’s and a master’s route often differ most sharply at admission. Undergraduate admissions may focus on prior secondary education, transcripts, transfer status, and general eligibility. Graduate admissions may add prior-degree requirements, prerequisite coursework, GPA rules, professional materials, or program-specific preparation.
Do not infer prerequisites from a degree title. Use the exact admissions page, program page, and catalog to determine:
Required prior degree or credits
Transcript requirements
Minimum GPA or grade rules, when published
Required mathematics, statistics, programming, or computing coursework
Test-score policy, when applicable
Work-experience, résumé, essay, or recommendation requirements
Foundation, bridge, conditional-admission, or waiver options
A learner who needs several prerequisites should include them in both the timeline and cost estimate.
Evaluate format separately from degree level
An online bachelor’s route is not automatically more flexible than an online master’s route, and an online program is not automatically asynchronous or self-paced. Delivery format must be verified separately.
Check the official program page, catalog, academic calendar, and course-delivery materials for:
Synchronous, asynchronous, or mixed delivery
Required live sessions or presentations
Part-time enrollment rules
Number of courses taken at once
Term or session length
Start dates and cohort structure
Required course sequence
Residencies, immersions, orientations, or campus visits
Proctored exams and technology requirements
Maximum time to complete the degree
For a working adult, the more useful question is not which degree is described as flexible. It is which published schedule can be mapped to actual work, travel, caregiving, and study time.
Review cost through the selected route’s official sources
Cost should be calculated from the route actually under consideration. A bachelor’s program may require more total credits but allow more transfer credit. A master’s program may require fewer credits but add prerequisites or graduate tuition. A competency-based or subscription format may use a different billing structure from a per-credit program.
Review:
Current tuition unit and required fees
Total credits or courses required
Transfer-credit or graduate-transfer policy
Foundation or bridge coursework
Required software, books, hardware, or platform costs
Residency or immersion expenses, if applicable
Course load and number of terms
Financial aid eligibility and enrollment-status rules
No degree label establishes a fixed final cost. The tuition page, catalog, transfer policy, financial aid materials, and student-specific evaluation control the estimate.
Route-selection sequence
Identify the learner’s current credential and completed coursework.
Define the occupational task family being researched through BLS and O*NET.
Review bachelor’s, master’s, analytics, and computer science routes only where the required curriculum relates to that task family.
Confirm admissions eligibility and prerequisites through exact official pages.
Read the catalog for required courses, electives, sequencing, and final projects.
Verify online delivery, part-time availability, term length, and live requirements.
Review transfer or prior-credit policy where relevant.
Estimate cost using the tuition structure and remaining requirements.
Verify institutional accreditation through DAPIP or the recognized accreditor directory.
Record unanswered questions for the admissions, registrar, financial aid, or program office.
Frequently asked questions
Should someone without a bachelor’s degree research master’s programs first?
Usually, the first step is to confirm the prior-degree requirement on the graduate admissions page. A person without the required bachelor’s degree may need an undergraduate route before becoming eligible for the master’s program.
Is a master’s degree always better for data science career entry?
No. The appropriate level depends on prior education, prerequisites, curriculum, and the role family being researched. Some occupations list a bachelor’s degree as typical entry-level education, while other positions may require or prefer advanced preparation. Employer requirements vary.
Can a computer science degree be used as a data science route?
It can be relevant when the required curriculum includes sustained data-focused work, such as statistics, databases, machine learning, data mining, visualization, or a data science concentration. The catalog must support the connection.
Is data analytics a less technical route than data science?
Not necessarily. Degree titles do not establish technical depth. Some analytics programs require substantial programming, statistics, databases, and modeling. Others emphasize applied reporting or business decisions. Required courses provide the evidence.
How should a working adult choose between a bachelor’s and a master’s route?
Start with eligibility and preparation, then compare the required curriculum and schedule. The route should fit the learner’s prior degree, prerequisite background, available weekly time, cost plan, and intended occupational context.
Bottom line
Choosing an online bachelor’s or master’s route in data science is a decision about starting point, curriculum, prerequisites, format, and occupational alignment. Degree titles help organize the search, but official admissions pages, catalogs, program requirements, tuition pages, transfer policies, academic calendars, accreditation records, and government occupational sources provide the evidence needed to choose a research path.
Sources
NCES CIP, Data Science, General: https://nces.ed.gov/ipeds/cipcode/cipdetail.aspx?y=56&cip=30.7001
NCES CIP, Data Analytics, General: https://nces.ed.gov/ipeds/cipcode/cipdetail.aspx?y=56&cip=30.7101
NCES CIP, Computer Science: https://nces.ed.gov/ipeds/cipcode/cipdetail.aspx?y=56&cip=11.0701
NCES CIP, Information Science/Studies: https://nces.ed.gov/ipeds/cipcode/cipdetail.aspx?y=56&cip=11.0401
NCES CIP, Management Information Systems, General: https://nces.ed.gov/ipeds/cipcode/cipdetail.aspx?y=56&cip=52.1201
BLS Occupational Outlook Handbook, Data Scientists: https://www.bls.gov/ooh/math/data-scientists.htm
O*NET OnLine, Data Scientists: https://www.onetonline.org/link/summary/15-2051.00
BLS Occupational Outlook Handbook, Operations Research Analysts: https://www.bls.gov/ooh/math/operations-research-analysts.htm
U.S. Department of Education, DAPIP: https://ope.ed.gov/dapip/#/home#/home
This website has been paid for by the University of Phoenix.