Data Engineering Master's

Program type:

Major

Format:

On Campus

Est. time to complete:

2 years

Credit Hours:

Design and develop large-scale information systems with modern techniques and state-of-the-art technology.

UNT's Master of Science program in Data Engineering allows students from STEM disciplines to focus their analytical, programming and engineering skills to integrate messy data into clean, usable datasets; organize, retrieve large data efficiently, and creatively solve data-related analytical problems.This degree program is interdisciplinary, allowing students to leverage their existing skillset and experience by combining it with skills in data engineering. Graduate students in our program have the opportunity to focus their entire degree on data engineering or specialize in biomedical engineering.

Requirements Tuition & Aid How to apply

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Why Earn a degree in Data Engineering?

Considering a career in one of technology’s most in-demand fields? The College of Engineering’s new Master of Science in Data Engineering may be for you. According to the Dice 2020 Tech Job Report, jobs in data engineering are growing by 50% each year, making data engineering the fastest-growing job in technology.

The Master of Science program in Data Engineering allows you to focus on your analytical, programming and engineering skills to:

Creatively solve data-related analytical problems
Integrate messy data into clean, usable data sets
Organize and retrieve large data efficiently

Core courses give students a solid foundation in data engineering and will dive into big data, data analytics, data visualization and database systems.

Concentration areas focus on data engineering and biomedical engineering.

Students who graduate from this program will be able to:

Build and maintain data pipeline systems
Improve data reliability, efficiency and quality
Prepare complex datasets to solve difficult problems
Understand efficient algorithms and data structures
Apply data engineering skills to their field of study

Marketable Skills

Efficiently visualize data
Effectively communicate technical information
Quickly adapt to new technologies
Collaborate to solve problems
Understand and use data software

Data Engineering Master's Highlights

Our program is open to students from STEM disciplines including engineering, computer science, math and other science-related backgrounds.

Students learn skills to develop software and manage hardware to efficiently collect, store and process data in large-scale SQL and NoSQL database environments.

The program is designed to meet the needs of the industry, so graduates will be able to not only solve complex data problems but also communicate and collaborate within a team environment.

Teaching and research assistantships provide support for many graduate students. In addition to a monthly stipend, assistantships also qualify students for in-state tuition rates, and many students receive tuition and fee support. Scholarships are available to graduate students as well.

UNT is a Carnegie-ranked Tier One public research university, where students can work with faculty exploring new frontiers.

The College of Engineering has state-of-the-art instructional facilities and laboratories containing cutting-edge research equipment. Top-ranked research labs offer exciting possibilities for study and discovery.

What Can You Do With A Degree in Data Engineering?

While estimates vary, a recent report from O’Reilly states companies typically need a minimum 2-3 data engineers for every data scientist to successfully complete projects, and the current job market is struggling to keep up with this demand. Our M.S. in Data Engineering program prepares students to enter this thriving job market right out of college.

The most in-demand jobs are data engineers, data architects, business intelligence architects, machine learning engineers, and data warehouse engineers/developers, with many data engineers working in many different fields.

Data Engineering Master's Courses You Could Take

Introduction to applied analysis. Topics include concepts in the design of empirical computer science research and the application of the appropriate associated statistical analysis methods; the nature and importance of scientific hypotheses in computer science, the design of valid experiments to test such hypotheses and the basic techniques of applied statistical analysis.

Introduction to visualization methods in data exploration. Topics include the use of space, form and color to communicate information; visualization of multi-dimensional data; data reduction methods such as principal component analysis and regression; methods for special domains such as geographic data and large graphs; and designing and implementing interactive interfaces.

Introduction to the design and use of database systems. Topics include data models, database query languages, logical database design and dependency theory.

Provides fundamental and advanced concepts and techniques of these systems which have become important issues not only in academia, but also in industries for the study and development of large scale database systems. Prepares students for research in the area of database systems.

Introduction to natural language processing; modern theories of syntax; context-free parsing; transformational syntax and parsing; augmented transition networks; and survey of natural language processing systems.

Introduction to Big Data and Data Science including an overview of the field, technical challenges, computational approaches, practical applications, structured and unstructured data processing, empirical methods in computer science, data analytics and learning, data visualization, privacy and ethics. Emphasis on Big Data and its effect on other topics within Data Science, its technical characteristics, and state-of-the-art Big Data analytics architectures and tools.