mastersindatascience.org

MastersinDataScience.org covers graduate degrees and shorter upskilling options in data science, including Master of Science (MS) in data science programs and related degrees such as the Master of Information and Data Science (MIDS) and the Master of Applied Data Science. The site is owned by 2U, LLC, the parent company of edX, and it identifies sponsored or advertised programs with the word “AD” or “Advertisement.” It is a guide and program-listing resource, not a degree-granting institution, so the specific degrees, formats, and requirements you see depend on the individual university listings.

Degree types you can browse

Based on the page evidence, the site organizes its coverage around a few broad categories:

• MS in data science programs — the core category the site is named for and the main focus of its “Master’s in data science programs and related degrees” section.
• Related master’s degrees — programs that sit adjacent to a pure MS in data science. The listings shown include:
  • Master of Information and Data Science (MIDS) — UC Berkeley’s online program, described as taking a multidisciplinary approach to the data life cycle, from planning and gathering data to analyzing and presenting findings, with coursework on privacy, machine learning, and data storage and management.
  • Master of Applied Data Science — UNC-Chapel Hill’s online program, described as covering the data life cycle, advanced programming, machine learning, and industry networking.
• Upskilling options — the site’s description also references boot camps and short courses alongside graduate degrees, so the coverage is not limited to full master’s programs.

How the programs differ in focus and goals

The two sponsored examples illustrate a real distinction in orientation:

The MIDS listing leans toward information-school framing — privacy, data management, and leadership — while the Applied Data Science listing emphasizes applied technical skills and professional networking. That difference in stated emphasis is the main signal the page gives for comparing related degrees.

Program formats

The listings shown are online programs. The page evidence also references full-time and part-time study: UNC-Chapel Hill’s program is described as completable in as few as 16 months full time, and the broader site description mentions graduate degrees alongside boot camps and short courses. Because format, duration, and admission requirements vary by university, treat the details in each listing as program-specific rather than site-wide rules.

How to judge which degree type fits you

The page itself does not prescribe a decision framework, but its structure suggests a practical approach:

1. Start with the degree category. If you want a broad, information-focused curriculum that includes privacy and data management, a MIDS-style program may match. If you want applied programming and machine learning with industry networking, an applied data science master’s may match.
2. Check the stated focus against your background. The listings describe target audiences — for example, UC Berkeley’s MIDS is described as being for professionals looking to become leaders in the field.
3. Confirm format and duration against your constraints. The two examples differ (12 months vs. 16 months full time), and both are online.
4. Verify requirements directly with the university. The listings note “no GRE required” for these two programs, but that is a program-level detail, not a general rule.
5. Note the sponsorship. Programs marked “AD” or “Advertisement” are sponsored or paid placements, and some are offered by universities that partner with 2U. Use the listings as a starting point, then check each university’s own page for current curriculum, cost, and admission terms.

If you are still deciding between a full master’s and a shorter upskilling path, the site’s coverage of boot camps and short courses alongside graduate degrees gives you a way to compare commitment levels before narrowing to a specific program.

MastersinDataScience.org is a guide site for people exploring data science graduate programs and upskilling options. It publishes overviews of master's degrees and related credentials, a browsable set of sponsored online programs, and career-oriented guides for both prospective students and recent graduates. It is owned by 2U, LLC, the parent company of edX, and some listed programs come from universities that partner with 2U or are paid advertisements — both are labeled "AD" or "Advertisement."

What the site covers

The site's stated goal is to help learners make confident, informed decisions about education and career paths. Its content clusters around a few areas:

• Graduate degree guides — explanations of what data science is, what a master's in data science involves, and how related degrees compare.
• Program browsing — a listing of online master's programs, including sponsored entries, that you can scan before requesting more information.
• Upskilling options — alternatives to full degrees, such as boot camps and short courses, for people who want targeted skills rather than a graduate credential.
• Career navigation — material aimed at prospective students weighing their options and at recent graduates looking for a new job.

Sponsored program listings

The program cards are the most concrete resource on the site. Each sponsored listing typically includes the school, the degree name, a short description of the curriculum, completion time, and admission notes. Two examples from the page:

Each card ends with a "Request more info" action, which routes you to the school rather than enrolling you directly.

How to tell sponsored content from editorial content

Because the site carries both partner programs and third-party ads, the labeling matters when you weigh what you read:

• Programs offered by universities that partner with 2U are marked, and 2U provides marketing and operational support and receives compensation for them.
• Other listings may be paid advertisements from third parties.
• Both types carry the word AD or Advertisement.

A practical approach: treat the descriptive text on a sponsored card as marketing copy from the school, and use the site's editorial guides — the sections on what data science is, degree types, and choosing a program — as the neutral framing. Verify admission requirements, tuition, and timelines directly with the university before applying, since the site does not publish pricing details.

Who gets the most out of it

The site is most useful if you are at a decision point: comparing an MS in data science against related degrees, deciding between a full master's and a boot camp or short course, or job-hunting after graduation and looking for structured guidance. If you already know which program you want, going straight to that university's admissions page will be faster than working through a listing site.

MastersinDataScience.org is a guide to data science graduate programs and upskilling options, including master's degrees, boot camps, and short courses. It is owned by 2U, LLC, the parent company of edX, and its stated goal is to help learners make confident, informed decisions about their education and career. The site is most useful if you are comparing data science master's programs or shorter training paths and want a starting point for research — but you should read it knowing that some listed programs are sponsored or paid placements.

What the site covers

According to its own description, the site focuses on:

• Graduate programs — primarily Master of Science in data science programs and related degrees, such as a Master of Information and Data Science.
• Upskilling options — boot camps and short courses for people who want targeted skills rather than a full degree.
• Career and decision guides — content aimed at both prospective students evaluating programs and recent graduates looking for a new job.

The homepage is organized around a guide titled "Master's in data science: Your guide to data science graduate programs," with sections on what data science is, the types of programs and related degrees available, and how to choose a program.

Who runs it and how programs are listed

MastersinDataScience.org is owned by 2U, LLC, which is also the parent company of edX. This ownership matters when you interpret what you see on the site:

• Some programs are offered by universities that partner with 2U, which provides marketing and operational support and receives compensation.
• Other programs may be paid advertisements from third parties.
• Both types are labeled with "AD" or "Advertisement."

In other words, the site is not a neutral directory. Listings can be influenced by commercial relationships, so treat it as one input among several rather than a ranking you can take at face value.

Examples of programs listed

The sponsored section on the homepage includes programs such as:

These details come from the site's own program descriptions and are labeled as advertisements. They are a sample of what the site lists, not a complete catalog.

How to use it when choosing a program

Because the site mixes editorial guides with sponsored listings, a practical approach is:

1. Use the guides for framing — the sections on program types and selection criteria can help you build a list of questions (format, length, prerequisites, focus areas).
2. Check the label on every program — if a listing says "AD" or "Advertisement," factor that into how much weight you give it.
3. Verify details at the source — confirm cost, admissions requirements, and timelines directly with the university, since the site's summaries are marketing-oriented and may not cover everything.
4. Compare across sources — cross-check against other program directories or the universities' own pages before deciding.

The site is a reasonable first stop for orientation, especially if you are early in your search and want to see the range of degree and non-degree options. It is less suited as a final decision tool, given the sponsored nature of many listings.

Start by defining three constraints before you browse any program pages: how much time you can commit, whether you need online or on-campus delivery, and whether you can take the GRE. Then compare programs on the same five dimensions — curriculum coverage, faculty and delivery format, time to completion, admission requirements, and how the program connects you to employers. MastersinDataScience.org organizes its guide around exactly these decision points, and the two sponsored programs it features illustrate how much programs can differ on each one.

What the site covers, and why that shapes your comparison

MastersinDataScience.org is a guide to data science graduate programs and upskilling options, owned by 2U, LLC (the parent company of edX). It states that some listed programs come from universities partnering with 2U, which provides marketing and operational support and receives compensation, and that other listings may be paid advertisements. Both types are labeled "AD" or "Advertisement."

That disclosure matters for how you use the site: treat it as a starting point for building a comparison list, not as a neutral ranking. Cross-check any program you shortlist against the university's own department page before applying.

The site's guide sections map directly onto the questions you need to answer:

• What is data science? (scope of the field)
• Master's in data science programs and related degrees (what types exist)
• Choosing the best MS in data science program for you (evaluation criteria)
• Sponsored online master's programs (specific listings)

The five dimensions to compare

1. Curriculum coverage across the data life cycle

Look for whether the program covers the full pipeline or only part of it. The site describes data science work as spanning planning and gathering data, analyzing it, and presenting findings.

UC Berkeley's Master of Information and Data Science (MIDS), as described on the site, takes what it calls a multidisciplinary approach to the data life cycle and includes coursework on privacy, machine learning, and data storage and management. UNC-Chapel Hill's Master of Applied Data Science is described as covering the data life cycle plus advanced programming and machine learning.

If your goal is a specific role — say, ML engineering versus analytics leadership — check which end of that pipeline the required courses actually sit in.

2. Faculty and delivery format

Delivery format affects whether you can keep working and how much structure you get. Berkeley's MIDS is described as delivering class sessions live online, taught by many of the same faculty and industry professionals who teach on campus. That's a meaningful difference from fully asynchronous programs, where you lose scheduled interaction but gain flexibility.

Ask each program: Are sessions live or recorded? Who teaches — full-time faculty, adjuncts, or industry practitioners? What's the typical class size?

3. Time to completion

The two featured programs differ here, and both state their timelines explicitly:

Note the qualifier "full time" on the UNC figure — part-time timelines are typically longer. If you're working while studying, ask for the part-time completion window separately, since that's the number that will actually apply to you.

4. Admission requirements, including the GRE

Both featured programs state that no GRE is required. This is not universal across data science master's programs, so make it an explicit filter: if you don't want to take the GRE, confirm the requirement in writing for every program on your list rather than assuming.

Other admission factors to verify per program: prerequisite coursework in math, statistics, or programming; minimum GPA; and whether work experience substitutes for any of these.

5. Career connection

UNC's program is described as offering opportunities to connect with industry professionals in North Carolina and beyond. Berkeley's is positioned for professionals aiming at leadership roles in the field.

Career support varies widely — some programs run employer networking events, others rely on alumni networks. Ask what specifically is provided: career fairs, capstone projects with company sponsors, alumni mentoring, or job placement data.

A practical comparison workflow

1. Write down your constraints first. Available hours per week, target start date, GRE willingness, and budget range.
2. Build a shortlist of 5–8 programs using the site's program listings and related-degree sections.
3. Fill in the same table for each — curriculum focus, delivery, time to completion, GRE requirement, career support. Use identical criteria so the comparison is fair.
4. Verify every data point on the university's own site. Listings on aggregator sites, including sponsored ones, can lag behind current requirements.
5. Talk to someone in the program — an admissions contact or a current student — about the two or three factors you can't evaluate from a webpage, such as workload realism and how responsive faculty are.

Common sticking points

• Confusing "as few as" timelines with typical timelines. A 12-month completion usually assumes full-time enrollment and may require prior coursework.
• Assuming no-GRE applies everywhere. It's program-specific, not field-wide.
• Treating sponsored listings as rankings. The site labels advertisements; a listing's position doesn't indicate quality.
• Skipping the part-time question. If you'll study while employed, the full-time figure is irrelevant to your planning.

Where to go next

The site's guide covers what data science is, the range of master's and related degrees available, and how to evaluate them, plus a section on taking the next step toward earning a master's. Use those sections to build your shortlist, then do the verification work on each university's own pages before you apply.

MastersinDataScience.org frames data science as work that spans the entire data life cycle — from planning and gathering data to analyzing and presenting findings. That definition matters if you are comparing graduate programs, because it tells you what a degree should train you to do: not just build models, but manage data end to end and communicate results. The site presents this framing alongside sponsored online master's programs, including UC Berkeley's Master of Information and Data Science and UNC-Chapel Hill's Master of Applied Data Science.

The data life cycle as the organizing idea

The site's guide to data science graduate programs describes the field through a sequence of stages rather than a single skill:

• Planning and gathering data — deciding what data is needed and how to collect it
• Analyzing data — applying statistical and computational methods
• Presenting findings — communicating results to decision-makers

This is the same framing UC Berkeley's MIDS program uses in its own description on the page, which notes a "multidisciplinary approach to the data life cycle, from planning and gathering data to analyzing and presenting findings." In other words, the site treats the life cycle as the common thread across programs, not a single course topic.

Technical areas the site associates with data science

Alongside the life cycle, the page names specific domains that coursework is expected to cover:

UNC's program description adds "technical expertise in advanced programming and machine learning" and a "comprehensive understanding of the data life cycle."

Why this definition is useful when choosing a program

If you accept the life-cycle framing, you can evaluate programs against it directly. A program that only teaches modeling but skips data collection, storage, or presentation is covering part of the field as this site defines it. The Berkeley and UNC descriptions both claim coverage across multiple stages, which is the claim worth testing against a syllabus.

Two concrete details from the sponsored listings are also worth noting because they affect fit:

• UC Berkeley MIDS: completable in as few as 12 months; no GRE required; live online class sessions taught by I School faculty and data science professionals.
• UNC-Chapel Hill Master of Applied Data Science: completable in as few as 16 months full time; no GRE required.

What the site is, and what that means for how you read it

MastersinDataScience.org is owned by 2U, LLC, the parent company of edX. The page states that some programs shown are offered by universities partnering with 2U, for which 2U provides marketing and operational support and receives compensation, and that other programs may be paid advertisements from third parties. Both types are labeled "AD" or "Advertisement."

That disclosure is the main thing to keep in mind: the definitions and program descriptions are useful as a starting framework, but the program listings are commercial placements. Use the life-cycle definition to build your own comparison criteria, then verify curriculum, cost, and format directly with each university rather than relying on the listing alone.