The Master of Science in data analytics program at University of Maryland Global Campus is designed to meet the rising need for highly skilled professionals who can transform the growing amount of institutional data into valuable assets. You’ll gain hands-on experience with a variety of analytical tools and learn how to manage and manipulate data, create data visualizations, and make strategic data-driven recommendations to influence business outcomes. By using industry knowledge and contextual understanding and questioning existing assumptions, you’ll learn to uncover hidden solutions to business challenges, allowing your organization to build and sustain a long-term competitive advantage.

These program requirements are for students who enroll in the 2018–2019 academic year. For prior year academic requirements, visit the catalog archive in the Current Students section.

About the Data Analytics Master's Degree

What You'll Learn

Through your coursework, you will learn how to

  • Evaluate a business problem or opportunity to determine the extent to which data analytics can provide a viable solution and translate the business problem to a data analytics project
  • Manage data analytics projects to ensure delivery of a successful data analytics initiative throughout its life cycle
  • Create a data mining application specific to an individual domain or area (for example, finance; cybersecurity; biological, medical, or scientific applications; or retail)
  • Apply statistical and machine learning techniques for data analysis and interpret and communicate the results
  • Transform large data sets into actionable information in an easy-to-understand format to support organizational decision making through the use of advanced analytical tools
  • Apply big data analytics technology to a specific area such as healthcare; marketing; insurance; cybersecurity; or biological, medical, and scientific applications
  • Evaluate the appropriate methods and tools for data analysis (including selecting a modeling approach, building a model using appropriate tools, validating the model, and deploying the model for prediction and analysis) in specific organizational contexts

Coursework Examples

In past projects, students have had the opportunity to

  • Plan, design, and implement the data mining process, including data extraction, data cleaning, data load, and transformation
  • Identify and implement appropriate techniques for or approaches to a given situation for descriptive, predictive, and prescriptive analytics using a wide range of supervised and unsupervised data mining algorithms
  • Evaluate the accuracy and performance of classifiers and predictors
  • Integrate a data mining system with a database, distributed file system, or data warehouse system using emerging technology
  • Identify and apply techniques for stream, time-series, social networks, and multirelational data mining
  • Employ real-time analytics and business intelligence directly on massive-scale data, including streaming data
  • Identify and apply techniques for spatial, multimedia, text, web content, web structure, and web usage mining
  • Apply modern technology for text processing, natural language processing, and cognitive computing

Industry Certification

This program can help prepare you for the following certification exam(s):

Data Analytics Master's Degree Requirements

Our curriculum is designed with input from employers, industry experts, and scholars. You'll learn theories combined with real-world applications and practical skills you can apply on the job right away.

Master's Courses

Take the following courses in the order listed:

Initial Requirement

  • UCSP 615
    (to be taken within the first 6 credits of study)

Core Courses

  • DATA 610
  • DATA 620
  • DATA 630
  • DATA 640
  • DATA 650

Capstone Course

  • DATA 670

Other Requirements

  • You must maintain a GPA of 3.0 or higher at all times.
  • All degree requirements must be fulfilled within five consecutive years.
  • Any transfer credits must have been earned within the five-year time frame to be applied toward a graduate degree.

Please review our overview of overall master's degree requirements for additional considerations.

Career Preparation

This program is designed to help prepare you for work in the high-demand field of data science and analysis in a public- or private-sector organization. Potential career fields include data mining, machine learning, and predictive modeling for large data sets.

Experience Recommended for Success in the Program

We recommend a background in software programming and statistics. If you do not have demonstrated experience or prior coursework in programming, you may be required to complete additional coursework. If you have not taken programming courses, we strongly recommend you take UCSP 635 and UCSP 636 or equivalent. If you lack a background in statistics, you must take UCSP 630 or equivalent. We recommend UCSP 605 if you'd like to improve your graduate writing skills.

Program Admission Requirements

To be admitted to the program, you must meet the standard criteria for graduate admission at UMGC and provide one of the following:

  • An official transcript reflecting completion of coursework at the 200-level or higher in statistics and computer programming from a regionally accredited college or university (credit from other accredited institutions may be considered on a case-by-case basis)
  • Proof of industry certification, such as IBM certification in Cognos, Risk Analytics, SPSS, SAS certification, Microsoft certification, Certified Analytics Professional, Certified Business Intelligence Professional, or Certified Health Data Analyst

Note: The complete admission file must be reviewed before you can enroll in DATA 610.

Facts & Figures

Student Clubs and Organizations

    Type: Student organization
    Available To: Undergraduate and Graduate

    The Computing Club aims to create a dynamic environment where members can work collaboratively, share innovative ideas, enhance their career-readiness, and gain marketable experience in their respective fields.

    Available To: Undergraduate and Graduate

    This club provides opportunities for students to learn more about the field of marketing, discuss their shared interests, and network with classmates and faculty members.

Awards & Recognition

  • Top 50 Best Value Online Big Data Programs of 2016 | | 2016

Frequently Asked Questions About the Program

How big is big data? +

According to IBM, people create 2.5 quintillion bytes of data every day.

What is big data? +

Big data is used to describe a vast quantity of data that is so large that it becomes too difficult to manage using traditional technologies. Big data is commonly defined using the three Vs:

  • Volume: The vast amount of data available, which leads to storage and management issues.
  • Velocity: How fast data is being produced and aggregated and how fast the data must be processed to meet demand.
  • Variety: The number of types and sources of data, which can be structured and unstructured and can come from virtually anywhere.

Data analytics make it possible to extract a fourth V: Value.

What is data analytics? +

Data analytics is the process of transforming vast amounts of raw data into constructive, actionable information.

To derive insights from large amounts of data, a data analyst must store, manage, sort, structure, and mine the data using highly sophisticated tools. The analyst can then apply refined data analysis to discover trends and influence business decisions.

Where does big data come from? +

Just as the volume of big data keeps growing, so do the sources. From commercial transactions to social media posts, personal data to weather patterns, nearly all data today is being tracked and recorded. It is up to data analysts to uncover the real potential.

Why is big data important? +

Great strides have been made in the gathering, storage, and security of big data, but the real opportunity is in the data analysis.

Its application is virtually limitless, and will shape the world we live in, from the way we interact on a personal level to how businesses and governments evolve. The increased use of big data in virtually every sector has created a talent gap for data analysts.

Why is data analytics important? +

The ever-increasing generation of data has rendered it unmanageable by traditional means, and the diversity of data sources makes finding insights even more complex.