Master's Degrees in Data Analytics

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A Master of Science in Data 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.

Typical names for a Master of Data Analytics program

There are a few different types of Master programs that focus on Data Analytics, and it is worth considering these different options during the application process. However, it's also important not to get too caught up in the name of the program, and instead focus on the curriculum, job outlook, and accreditation. Below are a few different names for a Master of Data Analytics Program:

  • M.S. in Data Analytics
  • Master of Science in Analytics
  • M.S. Applied Statistics and Analytics

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.

Are There Online Masters in Data Analytics Programs

Data Analytics is a fast growing field, but many programs are only offered in-person. This is detrimental for working professionals who are trying to complete their advanced degree 100% online, while still gaining the appropriate credentials and getting the full experience. Fortunately, it is possible to find a Masters program in Data Analytics that can check all the boxes that an in-person program can, while still being 100% virtual.

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Are there online data Analytics programs accredited by DASCA?

Universities that are accredited by the Data Science Council of America, or DASCA, have proven to offer the most advanced Data Science certifications, as well as undergraduate and master's programs in Data Science and Data Analytics. Many employers prefer to hire students from DASCA accredited institutions, and therefore, finding an online Data Analaytics program offered at a university that is accredited by DASCA can be beneficial. To clarify, it is the university that is accredited by DASCA rather than the specific program. When searching for the right program, it can be helpful to refer to the list of DASCA-recognized Data Science educators globally.
Below is a list of some of the well-known institutions that are DASCA-accredited:

  • Duke University
  • Dartmouth University
  • Brown University
  • University of Washington
  • University of San Francisco
  • 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.

    Top 5 online accredited Data Analytics Masters programs

    Although there are many online Data Analytics programs offered by accredited institutions, our team has worked to compile a list of the top programs, which are listed and described below:

    1. Villanova University Master of Science in Analytics
    2. This program is offered through the Villanova School of Business (VSB) and it requires students to complete 36 credits. This program is entirely online, but it is specifically offered as a part-time solution for working professionals. Villanova's MSA is accredited by the Association to Advance Collegiate Schools of Business (AACSB), the professional accrediting body for business schools, making this a great option for those who want to apply their data analytics skills to the business sector.
      This program typically takes 24 months to complete, and the tuition is $1,428 per credit, with a total tuition of approximately $51,500 for the 2021-2022 year. There are scholarships available, and another thing that makes this program stand out is the primarily asynchronous learning environment. Overall, the biggest advantage that this program offers is flexibility.

    3. Capella University Master of Science in Analytics
    4. This comprehensive program includes 11 core courses with 48 quarter-credits. Students can immerse themselves in the world of data using the latest technologies such as SAS®, R, Python, and Tableau. Also, Capella's collaboration with SAS® makes this program marketable to employers. The program ends with a capstone project to test practical application of the skills taught. The total cost of the program is usually between $25,000 and $34,000 depending on the number of credits a student transfers. The cost per credit is $695 and students can transfer a maximum of 12 credits to this accredited program.

    5. Purdue Global Master of Science in Information Technology (Concentration in Data Analytics)
    6. The data analytics concentration at Purdue University Global, offers students the chance to hone their IT skills, while still focusing on statistical methods and basic programming skills. This program is 60 credits, which is made up of 40 core credits and 20 elective credits. Purdue University Global is accredited by the Higher Learning Commission (HLC). The standard rate for tuition is $420 per credit, but there are also multiple discount options for military, Indiana residents, and Purdue Alumni. It is estimated that students will put in 20 hours per week for the coursework and each term is 12 weeks long.

    7. Western Governors University Master's in Data Analytics
    8. This program was named a "Best Value School" by University Research and Review, which makes sense when estimating the total cost of attendance. The financial breakdown comes to $4,235 per term. Because WGU charges by term rather than by credit, students can have better control over the costs. For instance, someone could finish the program in one year, while another may finish it in three years. WGU also offers students the chance to earn what they call "Micro-Credentials" in areas such as Data Preparation and Advanced Data Modeling. WGU is accredited by the Northwest Commission on Colleges and Universities (NWCCU).

    9. Colorado State University Global Master of Science in Data Analytics 5
    10. The CSU Global program partners with SAS® to allow students to get certified throughout the online program. The coursework focuses on all aspects of data including data warehousing, business analytics, predictive analytics, data visualization, and more. CSU even offers a mini Introduction to Data Analytics Course to help students decide if this is the right path for them.

    Typical Online Masters in Data Analytics Curriculum

    While each program has its own core course requirements, electives, specializations, and other unique areas, there are certain courses that come up across nearly all of the accredited online data analytics programs. Below, a list of the top ten courses for a Masters in Data Analytics is provided with a brief overview of the typical course.:

    1. Introduction to Programming in R and Python
    2. R and Python offer data analysts the tools they need to develop comprehensive reporting. R and Python are two of the coding languages that most companies emphasize for data analysts. The introduction courses will typically focus on data preparation, analysis, and visualization. Students will also learn about the different libraries and packages available in each environment and when to utilize them.

    3. Multivariate Data Analysis
    4. Students should have a strong understanding of univariate analyses prior to beginning this program, and a basic knowledge of multivariate statistics is also helpful. This course allows students to dive into real world applications for multivariate data analysis, while also reviewing the fundamentals. Some of the models that are typically reviewed in this course include multiple linear regression, analysis of variance models, and Chi Squares.

    5. Data Mining
    6. Students have the chance to learn how and when to use specific data mining techniques. By mining large datasets and applying the findings to major business decisions, students can better understand how this skill is used in practice as a data analyst. The goal is for students to feel confident finding hidden patterns in the data – separating the important information and core meaning from all the noise.

    7. Database Design
    8. Most data analysts will be working with complex databases, and therefore it is crucial that they understand the optimal design techndiques. Relational databases have intricacies that can make the difference between an efficient data pull and a system crashing. From clustered and nonclustered indexing to table naming conventions, this course can cover all the basics to get a data analyst or database administrator started. There will likely be more advanced courses offered for specific aspects of database design, such as Choosing the Right Index.

    9. Predictive Analytics
    10. Even if a student is not interested in pursuing a career as a data scientist, they will want to have an understanding of how predictive analytics work. Whether the student is learning the basic concepts and mathematics behind the core predictive analyses, or applying the analyses with Python or R, this course can be tailored to all types of data analytics students. For instance, knowing which predictive models are best for certain problems and understanding how to make sense of the results, allows future data analysts to fully immerse themselves in the data. Most of these fundamental courses will also take the conceptual knowledge and apply it to specific business decisions.

    11. Data Visualization
    12. After a the data is analyzed and interpreted, most often, employers and big businesses will want an eye-catching report that captures the key points. From understanding which charts are better for specific business questions, to determining the proper color schemes and sizing techniques, this course can cover everything from the basic concepts underlying data visualization to the practical application. This course can begin with the basics, like knowing the difference between a bar chart and a histogram and culminate in things like interactive coding libraries and other visualization software like Power BI or Tableau.

    13. Business Intelligence
    14. No matter where a student ends up working as a data analyst or other related career, they need to understand the business implications of the data. This is why most programs will require students to learn all about business intelligence to get a more complete view of how the data analytics impacts business decisions and what questions can be answered. In addition, students may have the chance to focus on a specific business area of interest and learn more about the key performance indicators used in that field. This can include healthcare, marketing, ecommerce, nonprofits and more.

    15. Data Acquisition
    16. This course will usually cover an a complete overview of Structured Query Language (SQL), which is what allows the data analysts to acquire the data they need for their analyses. Many students will have some knowlege of SQL before beginning their masters program, but the graduate courses will allow them to explore more advanced areas like subqueries, different types of joins, window functions, and conceptual ideas about relational databases. Most often, there will be multiple courses covering data acquisition, including a broader introductory course and a more advance course for those looking to apply all the fundamentals.

    17. Exploratory Data Analysis EDA
    18. Sometimes, even though a data analyst sets out to answer a specific question, new ones arise when examining the data. This is when EDA becomes important. A data analyst must understand all aspects of the data before continuing with planned analyses. This course will usually go over things like descriptive statistics such as measures of central tendency and variation, as well as hypothesis testing and parametric tests like correlations.

    19. Data Analytics Capstone
    20. Most, if not all data analytics masters programs will culminate in a final capstone project. Typically, this gives students the chance to explore an area of data analytics that is more applicable to their career goals and offers a chance to build a professional portfolio for interviews. In some cases, students may present their project at a professional conference under their faculty supervisor.

    More Sample Courses

    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

    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

    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 Intelligence (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.

    What are the top admissions requirements for most online Master of Data Analytics programs?

    Every program is going to have different requirements for their students, but the three main requirements that are difficult to avoid include the following:

    • Bachelors Degree in a STEM Field or a Business Degree
    • Most Data Analytics Masters programs will require that a student first obtains a bachelor’s degree in either a STEM field or a Business degree with a quantitative focus. This is important because an intermediate understanding of statistics is required for even the most fundamental courses in these masters programs.

    • General Technology Requirements
    • Most programs will require that online students to have a Windows PC with a newer version of Excel. Without this core technology, students can’t be expeted to thrive in the program, so it is important that you meet the technology requirements for your specific program before beginning the coursework.

    • Standardized Testing
    • Though this isn’t required in all cases, having strong GRE or GMAT scores are beneficial when applying for Data Analytics masters programs, especially if a student has an extenuating circumstance regarding the previous academic record.

    What careers can you have with a Master of Data Analytics degree?

    The job outlook for data analytis careers is positive, with a 25% growth predicted from 2019-2029, according to Western Governors Univeristy Below is a list of some of the career options for those students who graduate from an accredited online Master of Data Analystic program: <\p>

    • Data Analyst
    • Management Analyst
    • Financial Analyst
    • Operations Research Analyst
    • Marketing Analyst
    • Business Intelligence Analyst
    • Statistician
    • Database Administrator
    • Computer and Information Research Scientist
    • Business Executive

    Data Analytics Career Options

    Management Analyst

    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.

    Healthcare Analyst

    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.

    Masters of Data Science Programs