Masters in Data Analytics Online

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A few of the biggest dilemmas while deciding to do a master’s in data analytics are which program to pursue and what are the best data analytics programs. And then, there are other considerations such as cost, placements, starting salary, employment rates, acceptance rates, etc. We at OMC went through all these matrices and created a list of the best online master’s in data analytics programs. See the data analytics masters rankings below:

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Source – Integrated Post Secondary Education Data System & University Data
*Tuition rates are for in-state and per year. Program specific rates may apply.
**NA – data not available

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The cost of education is such that it is the primary measure for anyone considering an online masters in data science. And to the latest Salliemae study, the average family spent about $ 30,000 on education for the year 2019-20. This is up from $26,266 from a year ago. That is a 15 percent jump in a year. Students that want to pursue a master’s in data analytics online also identify affordability as a key factor in choosing a university. We have researched dozens of programs and created a list of the most affordable data analytics masters:

Link to this Page

Source – Integrated Post Secondary Education Data System & University Data
*Tuition rates are for in-state and per year. Program specific rates may apply.
**NA – data not available

The first thing to understand when discussing a master’s in data analytics is the difference between analysis and analytics concerning data. An analysis is the study of historical data to understand the trends and the patterns. In other words, analysis helps answer the ‘HOWS’ and the “WHYS’ from a historical point of view. On the other hand, analytics tries to implement ‘data applications’ considering the intelligence gained from analyzing the data. Though the program is called a master’s in data analytics, students learn data analysis and data analytics in this stream.

Analytics applies to two areas, Qualitative Analyticsand Quantitative Analytics. Mainly in qualitative analytics, one uses intuition and experience with the combination of the analysis results for a good decision, on the other hand, in “quantitative analytics”, one uses formulas and algorithms to apply on the numbers that one already gathered from the previous step, analysis, to predict and prepare for the future.

Typically, the data analytics practice encompasses many separate processes, which can comprise a data pipeline as listed below:

• Collecting and ingesting the data
• Categorizing of the data (into structured/unstructured forms)
• Managing the data, usually in databases, data lakes, and/or data warehouses
• Storing the data in hot, warm, or cold storage
• Performing ETL (extract, transform, load)
• Analyzing the data to extract patterns, trends, and insights. Sharing the data to business users or consumers, often in a dashboard or via specific storage. (Data Visualization)

Tools for Data Analytics

Analytics software is a tool that helps humans and machines perform the analysis that allows us to make mission-critical business decisions.

Common tools for performing data analysis and overall analytics include:

1. Microsoft Excel
2. Microsoft Power BI
3. Tableau
4. R analytics
5. Python
6. Google Analytics

Masters in Data Analytics Degree Types

There would typically be a few variants of a master’s in data analytics, with a few subjects changing concerning the specialization being pursued. Master’s in Data Analytics programs mainly come in three degree formats – MS in Data Analytics, MPS in Analytics, and MBA in Data Analytics. Below we would discuss the different types of master’s that can be pursued in data analytics.

When some are trying to start a career in a data-related field, students are often confused about the difference between data analytics and data science. Despite the two being interconnected, they provide different results and pursue different approaches. Here we will discuss the differences between them and look at what each delivers. Furthermore, we will also discuss the difference between data analytics and business analytics.

Data analysis involves answering questions generated for better business decision-making. It uses existing information to uncover actionable data. Data analytics focuses on specific areas with specific goals. On the other hand, data science focuses on discovering new questions that you might not have realized needed answering to drive innovation. Unlike data analytics which involves checking a hypothesis, data science tries to build connections, and it shapes the questions to answer them for the future. If we used the analogy of a house, it would be safe to say that, If data science is a home for all the methods and tools, data analytics is a small room in that house. Reflecting on the fact that data analytics is more specific and could be said to be a special and concentrated field of data science.

Data analytics focuses more on viewing the historical data in context, while data science focuses more on machine learning and predictive modeling. Data science is a multi-disciplinary blend that involves algorithm development, data inference, and predictive modeling to solve analytically complex business problems. On the other hand, data analytics involves a few different branches of broader statistics and analysis. If you would feel the need to learn more about data science, you can do it here.

Data Analytics vs. Business Analytics: Understanding the difference

Data analytics involves analyzing datasets to uncover trends and insights used to make informed organizational decisions. Business analytics focuses on analyzing various types of information to make practical, data-driven business decisions and implement changes based on those decisions. Business analytics often uses insights drawn from data analysis to identify problems and find solutions.

While data analysts and business analysts both work with data, the main difference lies in what they do with it. Business analysts use data to help organizations make more effective business decisions. In contrast, data analysts are more interested in gathering and analyzing data to evaluate and make decisions independently.

Some experienced hands in the data analytics domain would benefit from a fast-track masters in data analytics online program. But these programs are very rigorous and fast-paced and hence not for someone not well versed with the discipline. These accelerated programs are similar to the 1-year MBA degrees. Here are a few universities that offer 1-year Masters in Data Analytics Online programs:

University of North Texas

12 Months

Southern New Hampshire University

15 Months

Western Governor’s University

22 Months

According to the latest figures of the Federal Reserve, the average college debt among student loan borrowers is around $32,731, wherein most borrowers have debt between $25,000 and $50,000 outstanding loans. Looking at the data from 2015-16, that is an increase of about 20 percentage points. Keeping in mind the cost of education these days and the amount of debt each student carries, it is important to think about funding masters in data analytics online programs. The smartest way to fund education is a scholarship.

It might happen that someone does not qualify for a scholarship, for such candidates, if they are working professionals, they can check with their employers to reimburse the tuition fee. The other way of funding your master’s in data science is to apply for a student loan. Students can also check if they are eligible for federal student aid. Find out more about FAFSA.

Scholarships for Masters in Data Analytics Online Students

There are many scholarships available in the sphere of a master’s in data analytics online program. A few of them are listed below:

ACM SIGHPC/Intel Computational & Data Science FellowshipThe Association for Computing Machinery’s (ACM) Special Interest Group on High-Performance Computing (SIGHPC) partners with Intel to provide the Computational & Data Science Fellowship worth $15,000 annually to masters or doctoral students researching with big data. Eligible nominees will be females or members of racial minority groups who have completed less than half of their computing degrees at accredited US colleges.

Acxiom Corporation Diversity ScholarshipAs a publicly traded marketing analytics firm founded in 1969, Acxiom Corporation proudly began the Diversity Scholarship Initiative to award $5,000 yearly to nine full-time college students majoring in computer science, information systems, data science, statistics, math, information quality, and related fields. Eligibility criteria require attaining at least junior status, belonging to a historically underrepresented group, achieving a minimum 3.0 GPA, and agreeing to internships at Acxiom offices.

Bill Caspare Memorial Diversity ScholarshipValued at $10,000, the Bill Caspare Memorial Diversity Scholarship was initiated by The Collective, LLC, in 2015 to nudge undergrads and post-grads into careers for new media, digital advertising, or data science. It’s reserved for diverse applicants who are Black, Asian, Hispanic, Native American, multiracial, or LGBTQ. Students must also be US citizens or green card holders, study full-time, finish 60+ college credits, uphold a minimum 3.25 GPA, and submit three references.

CA Technologies Fellowship for WomenAt the University of Vermont, the CA Technologies Fellowship for Women is one of the most lucrative big data scholarships covering full tuition up to $14,670 per semester for outstanding bachelor’s females pursuing the MS in Complex Systems and Data Science. Engagement in CA’s Tech Girls Rock Initiative and the Anita Borg Institute for Women is included. Qualified fellows will be creative thinkers, great communicators, and high achievers with a minimum 3.0 GPA.

Daniel Larose Scholarship for Data Mining ExcellenceNamed for the Department of Mathematical Sciences’ professor, the Daniel Larose Scholarship for Data Mining Excellence confers $4,000 annually, or $2,000 per semester, to post-grads at Central Connecticut State University. They excel in the MS in Data Mining program. Big data lovers must have finished 12+ master’s credits, been graded “B” or better for Stat 520-Stat 527, exhibited strong performance as graduate assistants, and reached a cumulative 3.5 GPA.

Post a master’s in data science online, one can delve into many roles in the data field. This could vary from a data analyst role at an entry-level to a chief information officer role at the top of the hierarchy. There would be other intermediatory roles in between. Below is a list of roles and salaries with their responsibilities highlighted. 

Post a master’s in data analytics online, data analytics professionals can further their careers and keep being relevant in this fast-paced industry by becoming members and hence accrediting themselves with a professional organization in the field of data analytics.  Apart from being a body that assists in developing and distributing knowledge, professional data science associations keep their members informed about the newest developments and innovations. Here are a few of such organizations below: