Data Analytics and Systems Engineering

One of the newest and most exciting fields of study

Data Analytics and Systems Engineering are combined in this Bachelor of Science degree.  At first glance, these two fields may seem unrelated, but it turns out that they are closely related.

  • Data Analytics describes how to analyze complex data to reach actionable conclusions.
  • Systems Engineering describes how engineers conceive, design, integrate, and test complex systems.
Real World Case
Consider a self-driving vehicle. The vehicle itself represents a systems engineering challenge as sensors are integrated with the objective of operating safely and efficiently.

The self-driving vehicle also represents a data analytics problem as the data from the sensors must be analyzed to, for example, detect hazards and navigate in differing weather, during the day and at night, and in urban and non-urban environments.
Have you ever wondered:
• Can computers perceive the environment? How could terrestrial and airborne robots perceive the environment and interact with humans to accomplish difficult and chaotic tasks like combatting a wildfire?

• Can a social robot interact with a child to diagnose and treat autism or other learning disorders?

• How does a space vehicle company meet hundreds of requirements such as range, payload, and maintainability? It is not luck – learn how companies manage the development of technology to engineer complex systems

• What is “big data” and what is it used for?

• How can statistics and probability be applied to problems that do not have a clear answer?

• What is “risk?” Can we manage risk? If so, how?

The Bachelor of Science in Data Analytics and Systems Engineering will prepare you to answer these questions and engineer systems that satisfy user needs. Create systems and solve difficult problems.

Customize your Degree

This program develops and uses complex systems by employing data analytics and systems engineering tools and techniques.  A variety of systems will be used in the program, such as quadrotor drones and robots, including the NAO social robot.

The DASE curriculum is flexible; if your interest leans more toward Data Analytics or Systems Engineering, you can orient your course of study to reflect your interests by selecting a single track or the general combination track.


  •  Data Analytics – select 18 credit hours of your degree focused on data analytics, such as operations research, design and analysis of algorithms, data visualization, data mining, and machine learning.

  • Systems Engineering - select 18 credit hours of your degree focused on systems engineering, such as systems engineering, optimization, project design, mechanical systems, and electronics.

  • General – select 9 credit hours each of data analytics and systems engineering to include topics and courses from both areas equally.


Career Outlook

Data Analytics and Systems Engineering is the perfect career for people who like to solve practical problems in a variety of working environments and develop systems to improve quality and productivity.

Graduates may work in a variety of industries and have job titles such as

  • Data analyst, data scientist, business intelligence analyst, data engineer
  • Systems engineer, integration engineer, operations engineer, development engineer, dev ops engineer
Robotic arm controlled by tablet device


student studying while drinking coffee

The curriculum includes mathematics through calculus III, computational linear algebra with differential equations and probability and statistics; a programming sequence, coursework in mechanical engineering, computer science, and electrical engineering, and specialized core analysis and systems engineering courses. Tracks will include additional courses in Data Analytics and/or Systems Engineering.

2020-2021 Advising Guide

Example Course of Study


Adham Atyabi
Assistant Professor
My PhD was in application of computational modelling, machine learning and transfer learning in Brain Computer Interfacing (BCI) – analyzing brain activity using computers. My study was focused on improving BCI experience in paralyzed patients by developing techniques for isolating brain wave patterns. This work earned several awards. I have also developed data analytics and computational modeling solutions for autonomous systems and smart environments. Since earning my PhD, I have developed techniques for identifying Biomarkers for Autism Spectrum Disorder using both EEG (“brain waves”) and eye-tracking data. I have also developed modeling techniques for diagnosing autism based on how a child interacts with mobile apps. Conducting this work on Seattle Children’s Hospital and University of Washington, I also worked on multiple autism-based neuro-behavioral studies. I have also studied the gaming habits of children with the possibility of diagnosing autism. It is my pleasure to contribute my data analytic, experimental acquisition/development, and technical infrastructure creation capabilities to these and other projects in the future.
William Michael
My background is in the test and development of fixed-wing aircraft and associated sensor systems. Before joining the UCCS DASE faculty, I was the Systems Engineer for a classified satellite program, a military officer, and an Engineering Test Pilot/Program Manager for a major defense contractor. In the DASE program, we use the techniques of systems engineering and data analytics to learn how we can understand complex data and use this knowledge to engineer systems to exploit this data. My academic interests include computer vision, Bayesian probability and the development of hardware architectures to solve complex problems. In my spare time, I enjoy running, flying, and spending time with my family.

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