The state-of-the-art graduate data analytics program is designed with input from leading employers to give you a competitive advantage in the job market.

The Master of Science in data analytics at University of Maryland University College combines study in technical and business disciplines to make you a powerful data analyst with strong career potential. You'll learn how to manage and manipulate data, create data visualizations, and make strategic data-driven recommendations to influence business outcomes.

Build your skills in a career-focused graduate data analytics program that includes the foundations and application of data mining, predictive modeling, and visual analytics using large data sets, and more.

About the Data Analytics Master's Degree

The curriculum for the master's degree in data analytics is crafted, reviewed, and updated by a team of advisors and industry experts to ensure that what you learn aligns with the trends and technologies in the workplace today.

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

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

Introductory Course

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

We also recommend UCSP 605 if you'd like to improve your graduate writing skills.

Core Courses

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

Classes must be taken in the order listed.


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.
 

Program Admission Requirements

You must provide one of the following criteria:

  • A score in the 75th percentile on the quantitative section of the Graduate Record Exam, known as GRE, or the Graduate Management Aptitude Test, known as GMAT
  • One of the following industry certifications:
    • IBM certification in Cognos, Risk Analytics, or SPSS
    • SAS certification in Foundation, Analytics, Administration, Data Management, or Enterprise Business Intelligence
    • Microsoft certification, such as MCITP, MCSA, MCSE, MCSM, or MCDBA
    • Certified Business Intelligence Professional
    • Certified Analytics Professional
    • Certified Data Management Professional
    • Certified Health Data Analyst
  • Coursework at the 200-level or higher in linear algebra, calculus, discrete mathematics, probability, statistics, hypothesis testing, estimation, computer programming, data structures, database development, or data mining from a regionally accredited college or university with a minimum grade of B; official transcripts are required

If you do not have demonstrated experience or prior coursework in software programming, you may be admitted but required to complete additional coursework.

Facts & Figures

Student Clubs and Organizations

Business and Management Club

Type: Academic club
Available To: Undergraduate and Graduate

This club allows students to network and discuss their shared interests with one another and their faculty members, enabling them to learn more about the field.

Computing Club

Type: Academic club
Available To: Undergraduate and Graduate

The Computing Club allows its student members to share experiences and offer each other guidance on academic major and career options. Club events provide opportunities for professional socialization and networking, and members are privy to club resources that will help them further their education and careers.

Marketing Club

Type: Academic club
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

Frequently Asked Questions About the Program

How big is big data? +

On average, 1.8 zetabytes of information are created globally in a year, and that amount is expected to double annually. This volume of data is the equivalent of 200 billion two-hour HD movies, which would take one person 47 million years to watch, according to TechAmerica.

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. According to Gartner, big data is 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 the 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.