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.
These program requirements are for students who enroll in the 2017–2018 academic year. For prior year academic requirements, visit the catalog archive in the Current Students section.
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; cyber security; 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
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.
- UCSP 615
(must be taken within the first 6 credits of study)
- DATA 610
- DATA 620
- DATA 630
- DATA 640
- DATA 650
Classes must be taken in the order listed.
- DATA 670
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 STAT 200 or equivalent. We recommend UCSP 605 if you'd like to improve your graduate writing skills.
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 (credit from other accredited institutions may be considered on a case-by-case basis); 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
Most companies have traditionally been very application-based. Now it’s all about how you drive revenue and make better decisions based on data. That’s a big culture change, and you really have to be confident in what you’re doing because you’re changing an organization. A degree from UMUC, coupled with my experience, has instilled the confidence to be able to speak authoritatively on the subject.
Michael DeGiule, MS
Information Technology Master's Degree with Database Systems Technology Specialization
I have had two phenomenal professors, and I'm looking forward to having them again in [upcoming] semesters.
Data Analytics Master's Degree
Explore how data analytics is creating a big data revolution.
Just as UMUC paved the path for cyber security education, the university is once again at the forefront with launch of the data analytics program. We are partnering with leading employers to develop a cutting-edge curriculum that prepares our students with the skills and knowledge to compete and thrive in this growing field.
Charles Knode, PhD
Professor of Data Analytics
I have been so happy with this program; I'm glad I made this decision. I recommended UMUC to a couple of my friends. It's just been such a great experience.
Senior Technical Director, AT&T
Data Analytics Master's Degree
Student Clubs and Organizations
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.
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
- No. 2, 2017 IBM Watson Analytics Global Competition | University of Maryland University College Data Analytics Team | Watson Analytics Global Academics Network | 2017
- Distance Education Innovation Award Finalist | Master of Science in Data Analytics | National University Technology Network | 2016
- Top 50 Best Value Online Big Data Programs of 2016 | ValueColleges.com | 2016
About the Faculty
Frequently Asked Questions About the Program
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.
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.
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.
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.
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.
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.