Online Master’s in Data Analytics and Business Analytics Programs

The Institute of Advanced Analytics (IAA) reports that online master’s in data analytics degree programs have expanded significantly in a relatively short period of time. Demand for these programs has followed suit. However, some prospective students may not realize how large and varied the field of analytics really is. Colleges may, for instance, offer separate programs in areas like data analytics, data science, and business intelligence. Distinguishing among them can be difficult, but important: which type of programs students choose can influence both what they learn in school, and the careers that follow.

What is Data Analytics?

Data analytics represents the foundational elements of analytics. All other specializations in the field are built upon the core data analytics curriculum, which integrates advanced mathematics, statistical modeling, and big data IT systems. Data analysts use proficiencies in these areas to identify and quantify patterns, trends, and other causal relationships between two or more factors. This leads to insights that can solve complex problems, predict potential outcomes, and better inform decision-making. Much of what data analytics encompasses is bound up in data mining, a process by which relevant data is extracted, processed, and subjected to rigorous interpretation using tools like spreadsheets, databases, algorithmic programming, statistical probability, and data visualization technologies. Organizations rely on the results of this process to optimize efficiency, minimize risk, and monitor a range of key performance indicators (KPIs).

Online Master’s Degrees in Data Analytics

Master’s degrees in data analytics provide training in the tools and technologies that that drive the field, They are highly interdisciplinary, combining elements of applied mathematics, statistics, computer science, and business. Degree names run from general terms like Analytics, to more tailored designations like Predictive or Quantitative Analytics. The following are among some of the degrees in the field of data analytics identified by the IAA:

• Master of Science in Analytics (MSA)
• M.S. in Data Analytics (MSDA)
• M.S. in Analytics and Modeling
• M.S. in Applied Analytics
• M.S. in Business Analytics
• M.S. in Statistics and Analytics
• M.S. in Applied Statistics and Data Analytics
• M.S. in Strategic Analytics
• M.S. in Data Mining and Predictive Analytics
• M.S. in Predictive Analytics

Online master’s in data analytics degrees are often available through schools of continuing education or professional studies, the Institute notes, although engineering, computer science, and business departments have been known to offer them as well. These programs may allow students to attend on a part- or full-time basis, and often require no campus visits.

How OnlineEducation.com Categorizes Data Analytics Programs

The lack of uniformity among data analytics degree names can be confusing, especially when you factor in programs in the related specialization of business intelligence and data science. This can be a challenge for prospective students seeking programs that best align with their needs and goals. OnlineEducation.com carefully reviews all online analytics degree programs appearing on the site and categorizes them based on its own established criteria, which are derived from common workforce requirements and broader program trends. All online data analytics programs on OnlineEducation.com must include at least four highly-targeted analytics courses.

As of January 2023, we have identified 55 universities offering 60 online Master’s in Data Analytics programs that meet our criteria for inclusion in our directory. On OnlineEducation.com, we only include programs offered by non-profit universities in our listings.

In addition to data analytics, data science, and business intelligence programs, some schools offer business analytics programs. These programs are often offered through schools of business, and typically narrow the focus of data analytics to targeted applications in organizational management, executive decision making, financial risk analysis, strategic marketing, and other business-related concerns. OnlineEducation.com groups business analytics programs that are grounded in a core data analytics curriculum of applied mathematics, statistics, and computer science with data analytics programs (included below). However, a business analytics program with a focus on enterprise IT systems, database architecture, and dashboarding technologies would be listed under business intelligence programs.

Readers who want to learn more about online degrees in data science or business intelligence can refer to OnlineEducation.com’s related program pages.

Note: OnlineEducation.com does not currently include online MBA programs with data analytics specializations among its online master’s in data analytics listings, but may do so in the future.

Master’s in Data Analytics Courses and Curricula

Master’s degrees in data analytics are relatively new. According to the IAA, the first formal M.S. in Analytics emerged in 2007. Many more followed as colleges responded to the growing demand for data talent. These programs may provide somewhat different levels of training in three areas: applied mathematics and statistics; computer science and IT systems; and business/organizational management. For this reason, prospective students should carefully research each program’s curriculum to ensure it fits their needs.

Common areas of instruction in data analytics include:

• Data mining, optimization, validation, and security
• Programming, particularly with R and Python
• Data models and simulations used for time-series, predictive, risk, logistically regressive, and other types of analyses
• Project management and leadership
• Communication skills, including data visualization, presenting, and technical writing

Most online data analytics programs outline their curricula and course requirements on their official websites.

Common Data Analytics Classes

Not all data analytics degrees follow a common curriculum. Course requirements vary. The following are examples of some of the most common data analytics classes offered today, but the IAA encourages students to research individual institutions’ requirements and coursework.

Course Title	Course Description
Foundations of Data	This introductory course offers students a ground-up understanding of data analytics and how it is used. Data integrity is emphasized: students will learn about data validation, ethics, and best practices that govern the field. They also study how to source data sets and the regulatory organizations and policies that apply to their use. Students typically take this required course during their first term.
Predictive Analytics	This course reinforces not only students’ data skills but their decision-making abilities as well. Working with big data sets, students learn how to use statistical tools, models, and strategies to gather actionable predictive insights that support evidence-based decisions, and then communicate them clearly to business professionals. They may also study methods for identifying and verifying quality data and assessing its relevance.
Time Series Analyses and Forecasting	This course reinforces and builds upon students’ knowledge of predictive analytics. Students will study time series analyses that help one make informed predictions within an organization. Specific concepts may include time series models and theories; data analysis; statistical tools that support analyses; data communication; and practical applications of new data knowledge.
Risk Assessment	Students taking this course learn how to gather and interpret information gleaned from data sets critical for risk assessment. They study methods for gathering and optimizing datasets that may point to business risks, and then assess their impact on an organization’s operations, profits, or productivity. They also learn how to communicate this information to business colleagues. These quantitative assessments are especially useful for professionals at insurance, financial, and health care organizations.
Research and Design in Analytics	Analytics professionals often find and review current data research before framing business problems and devising data strategies to address them. This course guides students through the entire research process. They will learn how to turn business needs into researchable questions, identify potential sources, analyze and interpret research information, apply their findings to their own work, and translate insights to non-analytics professionals.
Data Mining	This technical course teaches students foundational statistics and machine learning concepts that can drive data mining. They study memory-based reasoning; the classification and regression of decision trees; logic; scorecard and neural network models; and pattern identification. Students may be required to design and complete data mining research projects using real-world datasets.
Capstone Projects and Practicums	Some online master’s in data analytics programs require students to demonstrate their knowledge through hands-on practicums using real data; open-source and commercial tools; and case studies. Schools may ask students to design their own data analyses and/or research projects. Practicums can often be completed from home, whether alone or with a local business.

Admissions Criteria and Prerequisites for Online Data Analytics Programs

Just as with other graduate programs, colleges and universities offering online data analytics degree programs typically have admissions criteria. Often these requirements help schools assess how prepared applicants are to complete and succeed in a graduate-level analytics degree program. For example, data analytics programs may require—or strongly recommend—that prospective students hold bachelor’s degrees from accredited institutions in a field relevant to data analytics, such as applied statistics, computer science, or engineering. Some schools may allow applicants to submit evidence of completing an analytics-related industry certification should their undergraduate work be unrelated to the field. These professional certifications are conferred by specific vendors, like IBM, SAS, and Microsoft. Online data analytics degree programs may also require that applicants have prior coursework in subject areas related to the field, like statistics or computer programming. Some online programs offer preparatory coursework in these areas for students who may require it.

Most online master’s in data analytics programs assess students’ academic achievement as well. It is common for graduate schools to require applicants to show that they met a minimum cumulative grade point average (GPA) in their undergraduate studies, for instance. GPA criteria vary by program, but often range from 3.0 to 3.5, depending on the competitiveness of the school or program. Some programs also require applicants to earn a minimum score on a standardized graduate exam—or a portion of an exam—like the Graduate Record Exam (GRE) or the Graduate Management Aptitude Test (GMAT). Other programs may only require students to take these exams if their GPA does not meet a minimum threshold.

Additional requirements can include the submissions of a statement of purpose, in which applicants explain how admittance would help them meet their academic or career goals. Online analytics programs may also ask prospective students to submit letters of recommendation, current résumés, and/or curriculum vitae along with their applications. While not all colleges require relevant professional or academic experience, having it may give candidates an edge in tight admissions decisions.

The Structures of Online Data Analytics Programs

Online degree programs have earned a reputation for convenience and flexibility, but that does not necessarily mean all online data analytics programs meet one’s needs and preferences. Some students, for instance, look for accelerated, full-time programs so that they can complete their degrees as soon as possible. This may not be a realistic option for working professionals, parents, military servicemembers, and other individuals who require more flexibility. The following list highlights some of the factors one might consider when researching online analytics degrees.

Course Load and Average Length of Study: Some online data analytics students have the option of studying full- or part-time while others are more limited. Likewise, not all colleges offer accelerated degree options. How many courses students are required to take each term determines how long it takes to earn their degrees—a factor often called time-to-completion. The following table provides examples of enrollment options and their associated course loads and time-to-completion estimates from real online data analytics master’s programs.

School	Enrollment	Avg. Courses Per Term	Avg. Time to Completion*
University A (36 credits)	Accelerated	1 course per 5-week term	16 months
University B (36 credits)	Student-Determined	~2 courses to be completed in a set sequence.	16 to 24 months
University C (30 credits)	Accelerated	1 course per term; 2 terms per semester	15 months
* Schools’ estimated time-to-completion for students who maintain suggested course load throughout their programs.

Practicums, Immersions, or Campus Visits: Some colleges and universities offering online master’s in data analytics degrees require students to complete a capstone practicum or internship, a geo-specific immersion term, or on-campus visits. While options vary by university and program, practicums and internships can often be completed within each student’s home community. Online analytics degrees with immersion programs, however, require students to report to a specific location that may or may not be on the university’s campus. For example, one online data analytics degree program requires students to complete two immersion terms, one in Washington DC and the other in New York City. Such travel requirements could have an impact on whether one can complete the program: full-time employees and military servicemembers, for instance, may not be able to commit to these requirements. The factors can also impact how the total cost of a program, as travel and living expenses are not generally included in tuition costs and fees.

Note: All online master’s in data analytics programs listed on OnlineEducation.com require no more than two travel commitments each year. Degree programs that require more than two visits are considered hybrid programs and are therefore not included.

Synchronous or Asynchronous Instruction: Online master’s in analytics programs offer varying degrees of flexibility, which is often determined by the rigidity of their course schedules. Note that while some colleges adhere to one format, others offer a mix of asynchronous and synchronous options.

• Asynchronous Instruction: The most flexible programs generally offer asynchronous instruction, meaning students are not required to log in at set course times so long as they meet all class deadlines. Students are allowed to sign on at their own convenience to download materials, review recorded lectures, and/or collaborate with students and teachers by email, social media, or discussion boards.

• Synchronous Instruction: Synchronous instruction falls at the other end of the flexibility spectrum: students in these programs must log in at scheduled times to watch and/or participate in live lectures and discussions. While this format offers less flexibility than asynchronous options, some students enjoy the immediacy and interactivity it provides.

To learn more about instructional methods in online degree programs, see our guide to Instructional Methods for Online Learning.

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Online Master’s in Data Analytics Programs by University: