Indiana MS in Data Science vs. ASU MS in Applied Statistics and Data Science

Consider Indiana University’s MS in Data Science If…

Give Indiana University more weight when:

• You want a fully asynchronous program. Indiana’s online MS in Data Science is delivered asynchronously and does not require a residency.
• You prefer an interdisciplinary data-science curriculum. The program draws coursework from statistics, informatics, computing, engineering, and related areas.
• You want to shape the degree around a data-science domain. Indiana’s curriculum incorporates domain-based study alongside its broader data-science requirements.
• You value broad elective choice. The curriculum provides opportunities to study areas such as machine learning, artificial intelligence, visualization, databases, natural language processing, computer vision, cybersecurity, bioinformatics, and other data-science topics.
• Indiana’s domain and elective options better match how you want to structure the degree.

Indiana’s interdisciplinary structure or asynchronous delivery should not be interpreted as evidence of better career outcomes, greater academic rigor, or stronger preparation for particular industries.

Consider ASU’s MS in Applied Statistics and Data Science If…

Give Arizona State University more weight when:

• You specifically want a degree combining applied statistics and data science. ASU’s program explicitly integrates advanced statistical methods with machine learning and data-science coursework.
• You want required study in statistical methods as part of the degree structure. Current coursework includes areas such as applied regression analysis and applied analysis of variance alongside machine learning and data-science tools.
• You prefer ASU’s more prescribed curriculum structure. The 30-credit degree includes a 12-credit required core, 9 credits of restricted electives, 6 additional elective credits, and a 3-credit capstone.
• ASU’s available advanced coursework better matches your interests. Current options extend into areas such as Bayesian statistics, deep learning, time series, statistical theory, optimization, and other advanced topics.

ASU’s applied-statistics identity or course structure should not be interpreted as evidence of stronger industry preparation, employer preference, better placement, or superior career outcomes.

Where There Is No Demonstrated Winner

Comparable program-level evidence does not establish an Indiana University or ASU advantage for salary, job placement, employer preference, industry-specific hiring, geographic employment opportunities, alumni-network effectiveness, faculty attention, overall educational quality, or ROI .

Those factors should remain unresolved unless sufficiently equivalent program-level evidence allows a direct comparison.

What the Table Establishes

Credit count does not create a winner. Both programs require 30 credits. Students should therefore compare what those credits contain rather than assuming one degree is materially shorter because of its total credit requirement.

Indiana has the clearer asynchronous-delivery distinction. Its online MS in Data Science is explicitly asynchronous and does not require a residency. That can matter for students who cannot reliably attend scheduled live classes, although asynchronous study still includes academic terms, deadlines, assignments, and course requirements.

ASU has the clearer applied-statistics identity. The degree itself is an MS in Applied Statistics and Data Science, and its curriculum combines statistical methods with machine learning and data-science study. That distinction should be treated as a curriculum characteristic—not as evidence that ASU is more practical or better aligned with employers.

Indiana organizes the degree more broadly across disciplines and domains. Its curriculum draws from multiple data-science-related academic areas and allows students to develop study around a data-science domain and elective coursework.

The programs allocate their 30 credits differently. Indiana’s degree includes 6 core credits, 6 domain credits, a 3-credit capstone, and 15 elective credits. ASU’s degree includes a 12-credit required core, 9 restricted elective credits, 6 additional elective credits, and a 3-credit capstone. Students should compare how much of each curriculum is prescribed and where they can use domain or elective coursework to shape the degree.

Neither curriculum can be declared more rigorous from its structure alone. Indiana includes substantial statistical, computational, machine-learning, and applied coursework. ASU combines statistical theory and methods with machine learning and data-science tools. Both programs integrate theoretical and applied study, but organize that study differently.

The useful decision is therefore based on the actual required courses, domain or elective options, delivery structure, completion policies, culminating experience, and eventually net cost—not an assumed research-versus-industry divide .

Indiana University MS in Data Science Curriculum

Indiana’s online MS in Data Science draws coursework from several disciplines involved in modern data science, including statistics, informatics, computing, and engineering .

The curriculum combines foundational data-science study with domain coursework and electives, allowing students to shape part of the 30-credit degree around their academic and professional interests.

Available study can extend into areas such as:

• Statistics and regression
• Artificial intelligence and applied machine learning
• Data visualization
• Databases and data systems
• Algorithms
• Natural language processing
• Computer vision
• Cybersecurity
• Bioinformatics

Indiana’s curriculum also incorporates data-science domain study . Current program materials identify Data Analytics and Visualization, Intelligent Systems Engineering, and Cybersecurity as domains through which students can develop more focused study.

The curriculum includes a required 3-credit capstone , giving students a culminating opportunity to apply knowledge developed throughout the degree. Internship opportunities may also be available within the broader curriculum.

This makes Indiana’s structure useful for students who want to combine core data-science study with coursework drawn from a broader interdisciplinary set of subjects.

That breadth should not, however, be interpreted as evidence that Indiana provides greater academic rigor or better career preparation. Its value depends on whether the available domains and courses match what the individual student wants to study.

ASU MS in Applied Statistics and Data Science Curriculum

ASU’s online degree is specifically an MS in Applied Statistics and Data Science , and that program identity is reflected in its curriculum.

The 30-credit program includes a 12-credit required core, 9 credits of restricted electives, 6 additional elective credits, and a required 3-credit capstone . This structure combines statistical methods, machine learning, data-science tools, and opportunities for additional advanced study.

The 12-credit required core includes:

• Machine Learning for Data Science
• Industry Tools for Data Science
• Applied Regression Analysis
• Applied Analysis of Variance

The broader curriculum also provides opportunities to study subjects such as:

• Bayesian statistics
• Deep neural networks
• Time series
• Statistical theory
• Applied linear models
• Optimization
• R and Python
• Applied Bayesian methods

The program concludes with a required capstone , giving students a culminating opportunity to integrate methods and skills developed throughout the degree.

ASU’s curriculum therefore should not be characterized as primarily tool-focused or as providing less statistical depth than Indiana. Statistical methods are central to the degree itself, while machine learning and data-science coursework extend that foundation into computational and applied work.

How Do the Curriculum Structures Differ?

The clearest distinction is not theory versus application . Both programs contain elements of each.

Instead:

Indiana uses a broader interdisciplinary and domain-based structure. Students can combine data-science study with coursework spanning statistics, computing, informatics, engineering, and specific application domains.

ASU uses an explicitly applied-statistics-and-data-science structure. Its 12-credit required core places statistical methods alongside machine learning and data-science tools, followed by restricted electives, additional electives, and a required 3-credit capstone.

That creates different ways to build a 30-credit master’s degree.

Students comparing the curricula should ask:

• Which required courses would I rather take?
• Which program offers electives that better match the skills I want to develop?
• Do Indiana’s domain options align with an area I want to explore?
• Do I want ASU’s explicit combination of applied statistical methods and data science?
• How do the required capstone and other applied-learning opportunities at each program fit what I want from the degree?
• Which program allows me to spend more of my 30 credits on coursework I would actually choose?

Which Program Is Better for Machine Learning and AI?

The published curricula do not establish a universal winner.

Both programs provide opportunities to study machine learning and related computational methods.

Indiana’s curriculum includes options in areas such as artificial intelligence, applied machine learning, natural language processing, and computer vision , alongside its broader interdisciplinary data-science coursework.

ASU incorporates Machine Learning for Data Science into its curriculum and provides additional opportunities for advanced study in areas such as deep neural networks , alongside statistics, optimization, and other data-science methods.

Students specifically interested in AI or machine learning should therefore compare the actual required and elective courses they could take , including prerequisites and course availability, rather than assuming that either university has an inherent advantage based on the program name.

Which Program Is Better for Statistics?

ASU’s degree has the more explicit applied-statistics identity , but that distinction should be interpreted carefully.

Its curriculum directly incorporates applied regression, analysis of variance, machine learning, and other statistical coursework, with additional study available in areas such as Bayesian statistics, time series, statistical theory, and applied linear models.

Indiana also incorporates statistics and statistical modeling within a broader interdisciplinary data-science curriculum.

Students who specifically want a master’s degree whose identity and required structure combine applied statistics with data science may give ASU more weight.

Students who want to combine statistical study with a broader selection of computing, informatics, engineering, or domain coursework may give Indiana more weight.

That is a curriculum-fit distinction—not evidence that one university teaches statistics better than the other.

Indiana University MS in Data Science Cost

Indiana’s online MS in Data Science requires 30 credits .

Indiana directs prospective students to its current tuition and cost resources rather than publishing one universal program-total figure on the main MS in Data Science program page.

That makes the student’s applicable tuition classification and enrollment assumptions important when estimating the cost of the degree.

Before calculating an Indiana program total, students should confirm:

• The tuition rate applicable to the online MS in Data Science
• Whether tuition varies by residency or other student classification
• Mandatory university or program fees
• How fees change with the number of credits taken per term
• Whether the published rate changes during the expected enrollment period

A reliable estimate should multiply the applicable current rate by the credits the student expects to complete and then add required fees under the same enrollment pattern used to estimate ASU’s cost.

ASU MS in Applied Statistics and Data Science Cost

ASU’s MS in Applied Statistics and Data Science also requires 30 credits .

For the 2026–27 academic year, ASU publishes online graduate base tuition of $605 per credit .

At 30 credits, that produces a base-tuition calculation of:

30 credits × $605 = $18,150

That figure should not be treated as the complete cost of the degree .

ASU Online graduate students may also pay required charges such as student-initiated fees, graduate-support fees, and advanced-technology fees. The amount can depend on factors including the student’s enrollment load.

The more accurate financial comparison is therefore:

ASU base tuition + applicable required fees under the student’s expected enrollment pattern

rather than simply describing the program as an $18,150 degree.

Students should confirm the rates and fees applicable to the term in which they enroll because tuition and fee schedules can change.

Which Program Costs Less?

A current cost winner should not be declared until both programs are calculated using equivalent assumptions.

The two degrees each require 30 credits, but their published tuition and fee systems are structured differently.

ASU’s current $605-per-credit online graduate base tuition provides a useful starting point, but required fees need to be incorporated.

Indiana’s current program cost likewise needs to be calculated using the tuition and required fees applicable to the online MS in Data Science.

A defensible comparison should use:

• 30 credits at Indiana’s applicable current tuition rate
• Indiana’s required fees under the assumed enrollment pattern
• 30 credits at ASU’s applicable current tuition rate
• ASU’s required fees under the same or an equivalent enrollment pattern

Only then should the programs be labeled cheaper or more expensive.

Does Lower Tuition Mean Better Value or ROI?

Not necessarily.

A lower net cost is financially meaningful, but tuition alone does not establish:

• Higher return on investment
• A shorter financial payback period
• Higher post-graduation earnings
• Better job placement
• Greater career advancement
• Higher lifetime earnings

A defensible ROI comparison would require comparable information about student costs and outcomes, including pre-degree earnings, post-degree earnings, financial aid, career changes, employment outcomes, and the period over which returns are measured.

Current program information does not provide a sufficiently comparable dataset to establish an Indiana-versus-ASU ROI winner.

Students should therefore separate two questions:

Which program will cost me less?

from:

Which program will produce the better financial return for me?

The first can be answered once comparable current tuition and fee calculations are established. The second depends on individual costs and outcomes that cannot be determined from published tuition alone.

What Should Students Include in Their Cost Comparison?

Before choosing either program, students should estimate:

• Tuition for all 30 credits
• Mandatory university and program fees
• Any fees affected by credits taken per term
• Books, software, and required technology
• Scholarships and institutional aid
• Employer tuition assistance
• Military and veterans education benefits
• Any change in employment or work hours while enrolled

The most useful financial question is:

After tuition, required fees, scholarships, employer assistance, and other necessary expenses, what would I actually pay to complete each 30-credit degree?

Until that calculation is completed under equivalent assumptions, no cost or financial-value winner is established .

Indiana University Online Format

Indiana’s online MS in Data Science is fully asynchronous .

Course lectures are recorded and available on demand rather than requiring students to attend regularly scheduled live lectures. The program also has no residency requirement , allowing students to complete the degree without traveling to campus for a required academic experience.

Asynchronous does not mean self-paced.

Students still complete coursework within academic terms and must meet assignment, project, examination, and other course deadlines. The distinction is that students generally have more control over when they access recorded course material and complete asynchronous learning activities within those requirements .

That structure may be particularly useful for students who:

• Work irregular or changing hours
• Live across different time zones
• Cannot reliably attend scheduled live classes
• Need to fit coursework around professional or family responsibilities

Students should not assume that asynchronous delivery makes the program easier or requires less time. It changes when students participate in course activities , not the academic requirements of the degree.

ASU Online Format

ASU’s MS in Applied Statistics and Data Science is available as an online 30-credit degree .

The curriculum includes a 12-credit required core, 9 credits of restricted electives, 6 additional elective credits, and a required 3-credit capstone.

Students who need a particular type of online delivery should verify the current requirements of the courses they plan to take. The program information used for this comparison does not establish that every ASU course follows an identical participation or scheduling format.

Before enrolling, students for whom delivery format is a major decision factor should confirm:

• Whether specific courses include scheduled participation
• How assignments and assessments are scheduled
• How many courses can realistically be taken at once
• Course sequencing and availability
• Available start terms
• Any requirements that could affect their weekly schedule

This does not make ASU less flexible. It means the current published information does not support assigning ASU a specific delivery-format advantage or disadvantage beyond confirming that the degree is offered online.

How Long Can Students Take to Complete the Programs?

Both degrees require 30 credits , but identical credit totals do not mean every student will complete them in the same amount of time.

Indiana allows students up to five years to complete its online MS in Data Science. That documented completion window can matter for students who anticipate adjusting their enrollment intensity because of work, family responsibilities, or other obligations.

A directly comparable maximum-completion period for ASU has not been established from the current program information used for this comparison.

Students considering ASU should confirm its current time-to-completion policies and then evaluate how course availability, sequencing, enrollment intensity, and any maximum completion requirements would affect their individual degree plan.

Until equivalent policies are established, OMC does not assign either program an overall time-to-degree advantage.

Which Program Is More Flexible?

Indiana has the clearer documented advantage for students who specifically require asynchronous delivery.

Its online MS in Data Science is explicitly asynchronous, has no residency requirement, and provides a documented completion window of up to five years.

That does not establish that Indiana is universally more flexible.

Overall flexibility can also depend on:

• Course availability
• Course sequencing
• Number of courses taken at once
• Assignment and project deadlines
• Available start terms
• Ability to change enrollment intensity
• Maximum completion policies

Students who specifically need asynchronous study can give Indiana’s documented delivery structure more weight.

Students considering ASU should evaluate the actual delivery and scheduling requirements that would apply to their planned courses rather than assuming that the program follows either a more flexible or more restrictive format.

The evidence supports a narrower conclusion:

Indiana has a documented asynchronous-delivery advantage for students who specifically need that format. Current comparable information does not establish an overall flexibility or time-to-completion winner between the two programs.

Neither delivery structure establishes better educational quality, greater academic rigor, or stronger student outcomes.

Can Career Outcomes Be Compared Directly?

Not reliably from the currently available program-level information.

A meaningful head-to-head comparison would ideally use equivalent measures from both programs, such as:

• Employment rates measured at the same point after graduation
• Median or average salaries using the same methodology
• Pre-degree and post-degree compensation
• Job-change or promotion rates measured over the same period
• Types of positions obtained by graduates
• Employer or industry distributions for comparable graduating cohorts
• Cohort sizes and survey response rates

Without sufficiently equivalent reporting, OMC does not assign either program a salary, placement, or career-advancement advantage.

Does Curriculum Establish an Industry Advantage?

No.

Indiana’s interdisciplinary curriculum may allow a student to pursue coursework related to areas such as cybersecurity, intelligent systems, data analytics and visualization, or other data-science subjects.

ASU’s curriculum combines applied statistics and data science and provides coursework spanning statistical methods, machine learning, data-science tools, Bayesian methods, deep learning, time series, and other quantitative areas.

Those differences can help students choose coursework relevant to their goals.

They do not establish that:

• Indiana graduates are preferred for healthcare, finance, research, or academic employment ;
• ASU graduates are preferred for technology, defense, consulting, or industrial employment ;
• either university provides a stronger geographic hiring network;
• employers generally prefer one credential over the other; or
• either program creates a stronger hiring pipeline.

Students targeting a particular field should compare the courses, projects, electives, domain options, and applied experiences they could use to develop relevant skills. Employer preference requires separate evidence.

What About Career Services?

Students should evaluate the professional resources available specifically to online graduate students at each university.

Useful services can include:

• Career advising or coaching
• Resume and LinkedIn support
• Interview preparation
• Job-search resources
• Employer events accessible to online students
• Internship support
• Alumni networking opportunities
• Career-transition resources

The existence of these services does not establish which university provides more effective career support.

A defensible comparison would require evidence showing how online students use those services and what outcomes result from them. Current comparable evidence does not support assigning either program a career-services winner.

Does Alumni Network Size Create an Advantage?

Not by itself.

Both Indiana University and Arizona State University have large alumni communities. The number of alumni associated with a university does not establish how effective that network will be for an individual online data science student.

Alumni-network effectiveness can depend on factors such as:

• Presence in the student’s target industry
• Presence in the student’s geographic market
• Access to alumni platforms and events
• Mentoring opportunities
• Responsiveness of individual alumni
• Relationships the student develops while enrolled

A larger network may create more potential connections, but alumni count alone does not demonstrate more referrals, interviews, job offers, or career advancement.

How Should Career-Focused Students Compare the Programs?

Students should start with the career they are targeting and work backward into the curriculum.

For each program, ask:

• Which required courses develop skills relevant to the roles I want?
• Which electives or domain courses would I actually take?
• Can I complete projects or a culminating experience relevant to my target work?
• What career resources are available specifically to online students?
• Can I identify alumni working in the roles, industries, or locations I am targeting?
• What program-level employment evidence does the university actually publish?

This produces a more defensible decision than assuming Indiana is inherently better for one set of industries and ASU for another.

Until comparable program-level outcomes establish otherwise, there is no demonstrated winner for salary, placement, employer preference, industry hiring, geographic hiring advantage, career-services effectiveness, alumni-network effectiveness, or overall career advancement .

Indiana University May Not Be the Best Fit If You…

• Specifically want a master’s degree combining applied statistics and data science in the degree title and curriculum structure. ASU’s program is explicitly an MS in Applied Statistics and Data Science and builds statistical methods and data-science coursework into that identity.
• Prefer ASU’s particular required statistics curriculum. ASU’s required core includes Applied Regression Analysis and Applied Analysis of Variance alongside Machine Learning for Data Science and Industry Tools for Data Science. Students who specifically want that combination should compare it directly with the Indiana courses they would take.
• Prefer a larger prescribed core. ASU requires a 12-credit core, while Indiana’s 30-credit degree includes 6 core credits, 6 domain credits, a 3-credit capstone, and 15 elective credits. Students who prefer more required coursework centered on ASU’s applied-statistics-and-data-science structure may favor that approach.
• Find ASU’s restricted or additional electives more relevant to your goals. Students interested in areas such as Bayesian statistics, deep learning, time series, statistical theory, optimization, or other available ASU coursework should compare those options with the courses they could realistically take at Indiana.

The more useful tradeoff is:

Indiana offers a fully asynchronous, interdisciplinary 30-credit data-science degree with 6 core credits, 6 domain credits, a required 3-credit capstone, and 15 elective credits. ASU offers a 30-credit Applied Statistics and Data Science degree with a 12-credit required core, 9 restricted-elective credits, 6 additional elective credits, and a required 3-credit capstone.

Students should decide which combination of required coursework, domain study, electives, and delivery structure better matches how and what they want to study.

ASU May Not Be the Best Fit If You…

• Need a program explicitly documented as fully asynchronous. Indiana’s online MS in Data Science is explicitly asynchronous and does not require a residency. Students who require that delivery format have a clearer documented match with Indiana.
• Want a broader interdisciplinary curriculum structure. Indiana draws coursework from statistics, informatics, computing, engineering, and other areas involved in data science. Students who want to build their degree across those disciplines may prefer Indiana’s approach over ASU’s more prescribed Applied Statistics and Data Science core.
• Want formal data-science domain study. Indiana requires 6 credits of domain coursework, with current domains including Data Analytics and Visualization, Intelligent Systems Engineering, and Cybersecurity. Students who want one of those areas incorporated into their degree structure may give Indiana more weight.
• Want a larger general elective allocation. Indiana includes 15 elective credits in its 30-credit curriculum, in addition to its core, domain coursework, and capstone. ASU instead divides its non-core coursework between 9 restricted-elective credits and 6 additional elective credits. Students should compare which structure gives them more useful choices rather than relying on the total number of electives alone.
• Prefer Indiana’s particular course options. Students interested in combinations involving natural language processing, computer vision, databases, cybersecurity, bioinformatics, visualization, or other Indiana coursework should compare those options directly with the ASU courses available to them.
• Need a documented extended completion window. Indiana allows students up to five years to complete its online MS in Data Science. Students who expect to adjust their enrollment intensity over time may value that published limit. A directly comparable maximum-completion period for ASU has not been established from the current program information used for this comparison.

The central tradeoff is:

ASU allocates its 30 credits across a 12-credit required core, 9 restricted-elective credits, 6 additional elective credits, and a required 3-credit capstone. Indiana allocates its 30 credits across 6 core credits, 6 domain credits, 15 elective credits, and a required 3-credit capstone, while also providing explicitly asynchronous delivery, no residency requirement, and a documented completion window of up to five years.

ASU is therefore not a weaker choice because of academic quality, rigor, career preparation, or employer perception. It may simply be a weaker structural fit for students who place greater weight on Indiana’s interdisciplinary curriculum, domain component, elective allocation, asynchronous delivery, or documented completion window.

Students should compare the actual required courses, domain or restricted-elective requirements, elective choices, delivery format, and completion policies that would apply to them before deciding which structure fits better.

Choose Based on Curriculum Structure

Neither program has a universal curriculum advantage.

Indiana structures its 30-credit MS in Data Science around 6 core credits, 6 domain credits, 15 elective credits, and a required 3-credit capstone . The curriculum draws from statistics, informatics, computing, engineering, and other areas involved in data science.

ASU structures its 30-credit MS in Applied Statistics and Data Science around a 12-credit required core, 9 restricted-elective credits, 6 additional elective credits, and a required 3-credit capstone .

Give Indiana more weight if you prefer an interdisciplinary curriculum, want formal domain study, or find its particular elective options more relevant to what you want to learn.

Give ASU more weight if you specifically want a degree combining applied statistics and data science or prefer its larger prescribed core, which includes Machine Learning for Data Science, Industry Tools for Data Science, Applied Regression Analysis, and Applied Analysis of Variance.

The better structure depends on which required and elective courses you would actually choose—not simply how many credits each program requires.

Choose Based on Statistical and Data-Science Emphasis

ASU has the more explicit applied-statistics-and-data-science identity .

Its required core places applied regression and analysis of variance alongside machine learning and data-science tools. Restricted and additional electives can extend study into areas such as Bayesian statistics, time series, statistical theory, deep learning, optimization, and other quantitative subjects.

Indiana incorporates statistics and machine learning within a broader interdisciplinary data-science degree. Its curriculum also provides formal domain study and elective opportunities across subjects such as artificial intelligence, visualization, natural language processing, computer vision, cybersecurity, databases, and bioinformatics.

Students specifically seeking a curriculum built around the combination of applied statistical methods and data science may give ASU more weight.

Students who prefer to combine data science with broader interdisciplinary, domain, and elective study may give Indiana more weight.

Neither distinction establishes that one program is more rigorous, more practical, or better preparation for employment.

Choose Based on Online Delivery

Indiana has the clearer documented advantage for students who specifically require asynchronous study.

Indiana’s online MS in Data Science is explicitly asynchronous and has no residency requirement. Students can access recorded course material without regularly scheduled live lectures while still completing coursework within academic terms and deadlines.

ASU’s MS in Applied Statistics and Data Science is offered online. However, the current program information used for this comparison does not establish that every ASU course follows an identical participation or scheduling format.

Students who specifically require asynchronous study can therefore give Indiana’s documented format more weight.

Students considering ASU should verify the delivery and participation requirements of the courses they expect to take if scheduling flexibility is a major decision factor.

This is a documented delivery distinction , not evidence that Indiana provides an easier or academically superior online experience.

Choose Based on Completion Flexibility

Indiana allows students up to five years to complete its online MS in Data Science.

That documented completion window may matter to students who anticipate changing their enrollment intensity because of work, family responsibilities, or other obligations.

A directly comparable maximum-completion period for ASU has not been established from the current program information used for this comparison.

OMC therefore does not assign either program an overall time-to-degree winner.

Students who expect to need an extended completion window can give Indiana’s published five-year policy more weight while confirming ASU’s current completion requirements before making a final decision.

Choose Based on Cost

No cost winner is established yet.

Both programs require 30 credits, but equal credit totals do not mean equal total costs.

ASU publishes online graduate base tuition of $605 per credit for 2026–27 , producing a base-tuition calculation of $18,150 for 30 credits . Applicable required fees must also be included before treating that figure as the cost of the degree.

Indiana’s applicable current tuition and required fees for the online MS in Data Science likewise need to be calculated under equivalent enrollment assumptions.

Until both programs are evaluated using comparable tuition and fee calculations, neither should be described as cheaper.

And even if one program ultimately has a lower published cost, that would establish a cost advantage—not a salary, career-outcomes, or ROI advantage .

The Bottom Line

Give Indiana University’s MS in Data Science more weight if you specifically value:

• Fully asynchronous online delivery
• No residency requirement
• An interdisciplinary data-science curriculum
• Formal domain study
• Its particular elective options
• A documented completion window of up to five years

Give ASU’s MS in Applied Statistics and Data Science more weight if you specifically value:

• A degree explicitly combining applied statistics and data science
• A larger prescribed core
• Required study in applied regression and analysis of variance
• Required machine-learning and data-science-tools coursework
• Its particular restricted and additional elective options

Both programs require 30 credits and a 3-credit capstone .

Current comparable evidence does not establish that either program produces better salaries, job placement, employer preference, industry-specific hiring, career advancement, educational quality, or ROI.

The evidence supports a narrower decision:

Indiana offers a fully asynchronous, interdisciplinary MS in Data Science with formal domain study, a large elective component, no residency requirement, and a documented five-year completion window. ASU offers an MS in Applied Statistics and Data Science with a larger prescribed core that explicitly combines statistical methods, machine learning, and data-science tools, followed by restricted and additional electives. Both programs require 30 credits and a 3-credit capstone. Choose the curriculum and documented delivery structure that better match what and how you want to study.

For additional university-level information, review OMC’s Indiana University Online profile and Arizona State University profile .

Students who want to compare additional program and degree options can continue to OMC’s Master’s Degree Comparisons .