Master's Degrees in Analytics
Last Reviewed/Updated: October 7, 2020
A Master of Science in Analytics provides an open-ended core study of big data with statistics, data mining, data warehousing, and more. Typically, these degrees offer a number of concentrations that branch out into specific study, such as business marketing, healthcare, and information systems. Many universities offer a Master's degree that already has a specific focus in these subjects.
Master’s in Data Analytics Degree Programs
What is the difference between degrees in data analytics and business analytics?
Mainly, the education will provide a different focus. There are many instances of overlap, such as data mining and analyzing data, creating visualizations, and coming up with data-driven solutions to organizational problems. However, data analysts tend to be less engaged in the business decision-making process. Business analytics requires less programming skills, database knowledge, and analytical tools — business analysts are more involved in business intelligence and influencing an organization's direction.
There are also differences between a full Master's degree in an analytical subject, and a concentration within a separate Master program.
There are also differences between a full Master's degree in an analytical subject and a concentration within a separate Master's program. For example, a Master of Business Administration with an area of emphasis in Business Analytics is geared toward business professionals with more theoretical courses and how to improve the bottom line. A Master of Science in Business Analytics is for those with a computer science or information technology background that will have hands-on experience with programming and engineering.
Online Masters Programs
Featured Online Data Science Programs
Johns Hopkins AAP
|MS in Data Science and Policy||Website|
|Master of Science in Applied Data Science||Website|
George Mason University
|Master of Science in Data Analytics Engineering||Website|
Northern Illinois University
|Master of Science in Data Analytics||Website|
University of Denver
|Master of Science in Data Science||Website|
Villanova University Master of Science in Analytics
The Villanova School of Business has an MSA program that is accredited by the Association to Advance Collegiate Schools of Business (AACSB). Most of the program is in an asynchronous format where coursework is accessible and completed at any time of day and there are discussion forms to keep in touch with professors and other peers. 36 credit hours are needed to complete the program, which takes around two years to complete, and there are no campus visits required.
Courses are separated into fundamental and core groups, providing an introduction into business analytics, data analysis, and programming. Core course examples include Applications with Python, Enterprise Data Management, and Analytical Methods for Optimization and Simulation. To be administered into the program, prospective students must hold a Bachelor's degree with at least a 3.0 cumulative GPA. GRE or GMAT scores are not required, but recommended for those that have a lower GPA and have less than two years of professional experience.
Georgia Institute of Technology Master of Science in Analytics
Georgia Tech offers an online MSA that can be completed within one to two years, and the curriculum is divided up evenly between core courses and elective options to customize the degree. Different tracks to choose from are Analytical Tools, Business Analytics, and Computational Data Analytics. The first option provides a general look into quantitative methods while the other two are more specialized within a business or big data approach to analysis.
Regardless of track and whether students are taking the online or on-campus version, there is a required six-hour practicum that showcases the experience gained from the program. Students can either complete an analytical project based on the organization they work with or a determined project from the university. Most of the online learning will be asynchronous, but there will be consistent deadlines and proctored exams that students will need to keep up with. Typically, students that are working full time can complete two courses each semester.
Dakota State University Master of Science in Analytics
Another online analytics program in South Dakota is offered by the College of Business and Information Systems at DSU. Courses are distributed either live or asynchronous and there will be discussion boards to keep in contact with other members and faculty. Courses are offered in three different terms per calendar year and students have the ability to complete the program within five years after taking the first course. Concentration options to customize the degree include a generalized format, Business, Healthcare Analytics, and Information Systems.
To be admitted into the program, students must have a Bachelor's degree from an accredited university with a cumulative GPA of 2.70 or higher. Prior coursework should be completed in database design (or any SQL-related course), programming, and statistics. There are foundational courses offered by the program to fulfill these needs. Students will need to maintain at least a 3.0 GPA to stay in the program, and only two C grades are allowed throughout the curriculum and no final grades below that.
Capella University Master of Science in Analytics
The School of Business and Technology has an online MSA degree that features 11 courses and a capstone project. Students will experience a wealth of technical tools such as SAS, Python, R, and Tableau for programming, mining and querying data, and developing forecasting models. As a partner with SAS, they provide all the software needed and there are certification exams to become a Certified Advanced Programmer or Certified Statistical Business Analyst with SAS 9.
Instead of having specializations, this MSA program simply emphasizes a general skill set in analytics. A special offering from Capella University is GuidedPath, a specialized learning format that sets up deadlines around your schedule and provides weekly interaction with faculty and other classmates. There is a quiz and trial course available for those interested in participating and to see if it is a good fit for them.
Foundational courses within analytics involve modeling, transforming information from raw data to make business decisions, developing algorithms, and presenting these findings through data visualization. Expect to find these kinds of courses regardless of analytical subject that is studied, be it in a more technical approach through data analytics or exploring how to give a professional team an advantage to win a championship within sports analytics.
Statistical inference looks to make hypotheses about a population based on data sampled from that population. Introductory courses will consist of an overview of statistical theory and applications, such as the importance of gathering randomized samples. Courses will introduce inference methods of parameter estimation, interval estimation, and hypothesis testing. For example, statistical inference can be applied to determine a new cancer drug's success or failure.
Statistical inference is connecting the dots and forming a hypothesis on properties of a population. If the course is under the generic name, coursework will consist of an overview of strategies involved in developing theories and generating randomized samples to come toward a conclusion.
In quantitative methods, students analyze and interpret statistical analysis through the use of mathematics, sampling, theories, and hypotheses. These methods focus on the collection of data and the creation of models that depict the data that is found. This is in contrast to qualitative methods, which attempts to find the reasoning behind why the data is represented or trending a certain way.
Data mining is all about discovering patterns in structured and unstructured data that lead to meaningful insight. The process intersects data warehousing, data pre-processing techniques, and applying machine learning. Data mining courses will cover classification, regression, clustering, deviation detection, and evaluation of patterns mined from data. The problem and available data tend to determine the method. For example, forecasting future sales (regression) vs. predicting if a party will default or not default on their loan (classification) requires different methods.
Data mining is the gathering of information from structured and unstructured data. These courses will focus on the many processes that make this happen, and other responsibilities such as cleaning, querying, and organizing the information.
Additionally, there must be no errors within the data, such as missing information or negative numbers, where it is implausible when making data-driven decisions or developing company predictions. Data mining covers preprocessing techniques to handle missing data and engineer new variables from existing data to strengthen insights.
Business Intilligence (BI)
Business intelligence involves data-driven decision making for an organization looking for a competitive advantage or to run more efficiently and smoothly. These courses will look over the data available within the company and strategizes toward the future, cutting costs that may not be needed or implementing new hardware and software capabilities that can improve performance.
This coincides with data visualization courses that will help students be able to present their findings and create a story in a professional way, either through reporting or through various charts and graphs for non-technical employees to gain an understanding of the information.
How to Begin an Analytical Career
When it comes to analyst opportunities, it is important to have higher education with at least a Bachelor’s degree and related coursework in the field. This includes majors in business, computer science, and data science with courses in programming, statistics, mathematics, probability, modeling, and machine learning. While not all analyst positions will require developing databases or the architecture surrounding it, this is still important to understand within the organization.
Helpful Skills and Tools
Analysts will need to be proficient in many skills, such as basic programming within Python or querying data with SQL, explain and understand statistical inference and algorithms, and clean data and fix corrupt information. They must be able to spot trends and variations, being able to connect the information together. This provides an opportunity to create data visualizations and be able to communicate this information to other non-technical parties. As one would expect, having another soft skill, such as communication, is very important.
Workability and Collaboration
Not only do analysts need to present information, but they must be able to work with other members on the team. They may have to work with engineers to help build and maintain the architecture, or to data scientists in order to work with unstructured data and to fill in any potential gaps with their findings. Communication is certainly key if the analysts’ role deals with more reporting and presentation responsibilities, acting as a middle man between the information technology sector and stakeholders in the company.
Becoming certified within big data can show employers that a candidate is proficient in a particular skill set. Some examples include:
- Cloudera Certified Associate Data Analyst
- Microsoft Certified Solutions Expert: Data Management and Analytics
- SAS Certified Data Science Using SAS 9
Note on Certifications
For the CCA Data Analyst certification, test takers must demonstrate preparing and manipulating Hadoop data on a Cloudera Enterprise cluster. This certification is ideal for SQL developers looking to hone their query skills and achieve work advancement. 8 to 12 questions need to be answered within a two-hour time frame and the exam is proctored. Scoring over 70 percent correct is a passing score and each exam attempt is $295. There are no prerequisites to take the exam, but it is recommended to take Cloudera’s training course.
Important Data Analytics Organizations and Associations
International Institute for Analytics (IIA)
The International Institute for Analytics (IIA) has a focus on a variety of methods and the business impact that data analytics can have. One of the services they offer is an Analytics Maturity Assessment that gives an overview of how well the organization uses analytics to help optimize their processes and to limit as much money loss as possible. Companies can receive an analysis twice over the span of 15 months, or three analysis reports over two years.
Healthcare Data and Analytics Association (HDAA
The Healthcare Data and Analytics Association (HDAA) is the leading organization within the healthcare industry, which contains over 400 providers such as the Mayo Clinic, Kaiser Permanente, Mercy, Mt. Sinai, Sutter Health, and universities such as Stanford, Harvard, Northwestern, and Penn State. Members range from developers and analysts up to executives such as chief data officers and chief medical information officers. Benefits of joining the program include networking with other professionals, obtaining the latest innovations in the healthcare sector, and being able to participate in panel discussions and webinars.
Digital Analytics Association (DAA)
The Digital Analytics Association (DAA) is another data-driven community that was founded as the Web Analytics Association back in 2004. Some of their goals include creating a standard when it comes to defining analytics and terminology within the data science industry, being able to influence any legislation that can impact the space, and bringing together professionals through networking events. They provide a Web Analyst Certification exam that demonstrates expertise within analytics and potential work candidates can stand out from the competition.
Data Analytics Career Options
This position oversees an organization’s processes and is able to make them more efficient and profitable. They are able to determine any issues that arise and find ways to solve them through data-driven solutions. This could be analyzing the information themselves or meeting with other people in the company. Frequently, this information is reported to higher-level professionals with recommendations on the next steps, or they act as consultants when needed on a particular subject. According to the Bureau of Labor Statistics, the outlook of management analysts is rising by 14 percent between 2018-28, and the average salary across the country is approximately $83,610, with that number skewing higher for more scientific and technical industries.
These analysts have the important task of managing, modeling, and validating medical information for the organization they work for in an effort to improve the system. Duties can include being able to identify problem areas and recommending solutions through reports and presentations toward higher-level executives. In many cases, these analysts will be dealing with multiple projects at one time. Due to the complex nature of healthcare information, it is important that these analysts have plenty of professional experience with information systems and are comfortable with working in the healthcare sector. Holding a Master’s degree is preferred in many cases, but not required.
Data Governance Specialist
Focuses on the policies, security, and accuracy of data moving through the organization. This means they create the procedures in term of accessing information throughout the company. They are frequently part of keeping data safe from unauthorized access within or by outside sources, and they can determine the validity of the data. According to Salary.com, data governance specialists make anywhere from $57,200 to $86,700 annually across the United States.