Computing systems are integrated devices that input, output, process, and store data and information. Computing systems encompass a wide range, from simple sensors and hardware components to phones, laptops, desktops, and entire data centers. Computing systems specialists are challenged to provide ever increasing levels of performance from these systems.

The Computing Systems concentration provides students the necessary tools to solve important and demanding systems problems at scale. Students will learn how to design and assess computer systems from a holistic perspective that encompasses distributed and parallel algorithms, big data, systems software, networking, compiler design, and artificial intelligence/machine learning.

Data is our most valuable resource. Large scale data are being generated by programs, sensors, and simulations. Drawing timely and effective insights from these data are at the heart of modern problems in computer science and society in general. The Computing Systems concentration includes courses that teach you how to accomplish this goal, from storing, transporting, organizing, and extracting insights from data to expressing programs that execute in parallel and distributed environments encompassing hundreds of thousands of cores.

Learning Outcomes

Upon completing this program, students will be able to:

  • Design scalable systems for computational and data intensive problems.
  • Design distributed and parallel algorithms to analyze large data sets.
  • Leverage diverse computing architectures in support of problem solutions.
  • Program accelerators/coprocessors (e.g., for deep learning).
  • Confidently pursue graduate studies or professional employment in computer systems and computer science.

Potential Occupations

In addition to the career opportunities open to all computer science graduates, the Computing Systems concentration opens career paths that include:

Cloud applications designer, systems designer, data scientist, big data analyst, compiler designer, database specialist, and supercomputing applications specialist.

Effective Fall 2020

A minimum grade of C (2.000) is required in CO 150 and in all CS, DSCI, MATH, STAT and departmental Technical Elective courses which are required for graduation​.

CO 150College Composition (GT-CO2)1A3
CS 165CS2--Data Structures 4
MATH 160Calculus for Physical Scientists I (GT-MA1)1B4
Select one course from the following: 4
CS1---No Prior Programming Experience  
CS1--Prior Programming Experience  
Select at least two courses totaling a minimum of 7 credits from the following (one course must be or include the sequenced laboratory): 7
Introduction to Astronomy (GT-SC2)3A 
Human Origins and Variation (GT-SC2)3A 
Principles of Animal Biology (GT-SC2)3A 
Principles of Plant Biology (GT-SC1)3A 
Fundamentals of Chemistry (GT-SC2)3A 
General Chemistry I (GT-SC2)3A 
Exploring Earth - Physical Geology (GT-SC2)3A 
The Blue Planet - Geology of Our Environment (GT-SC2)3A 
Geology of Natural Resources (GT-SC2)3A 
Physical Geology for Scientists and Engineers3A 
Honors Seminar: Knowing in the Sciences3A 
Attributes of Living Systems (GT-SC1)3A 
Biology of Organisms-Animals and Plants (GT-SC1)3A 
Introductory Genetics: Applied/Population/Conservation/Ecological (GT-SC2)3A 
Introductory Genetics: Molecular/Immunological/Developmental (GT-SC2)3A 
Fundamentals of Ecology (GT-SC2)3A 
Oceanography (GT-SC2)3A 
General Physics I (GT-SC1)3A 
General Physics II (GT-SC1)3A 
Physics for Scientists and Engineers I (GT-SC1)3A 
Physics for Scientists and Engineers II (GT-SC1)3A 
Arts and Humanities3B3
Diversity and Global Awareness3E3
Electives 2
 Total Credits 30
CS 201/PHIL 201Ethical Computing Systems (GT-AH3)3B3
CS 220Discrete Structures and their Applications 4
CS 253Software Development with C++ 4
CS 270Computer Organization 4
Select one course from the following: 3-4
Linear Algebra for Data Science  
Linear Algebra I  
Select one course from the following: 3
Introduction to Applied Statistical Methods  
Introduction to Biostatistics  
Intro to Theory and Practice of Statistics  
Historical Perspectives3D3
Social and Behavioral Sciences3C3
Electives 2-3
 Total Credits 30
CS 314Software Engineering4A,4B3
CS 320Algorithms--Theory and Practice 3
CS 370Operating Systems 3
Systems Elective - select one course from the following: 4
Database Systems  
Introduction to Artificial Intelligence  
Introduction to Machine Learning  
Automata, Logic, and Computation  
Two CS courses numbered 300- or above, excluding 380-399 and 480-499 6-8
Technical Elective (see list below) 3-4
Advanced Writing23
Electives 2-5
 Total Credits 30
Systems Courses - select four courses from the following (one of the selected courses will fulfill AUCC 4C): 16
Introduction to Big Data4C 
Introduction to Compiler Construction4C 
Introduction to Distributed Systems4C 
Computer Networks and the Internet4C 
Parallel Programming4C 
Electives1 14
 Total Credits 30
 Program Total Credits: 120

Technical Electives

Any CS, CT, DSCI, IDEA, MATH, or STAT courses numbered 300- or above, excluding 380-399 and 480-499
BZ 350Molecular and General Genetics4
BZ 360Bioinformatics and Genomics3
CIS 320Project Management for Information Systems3
CIS 350Operating Systems and Networks3
CIS 360Systems Analysis and Design3
CIS 413Advanced Networking and Security3
CIS 455Advanced Database Management3
ECE 452Computer Organization and Architecture3
ENGR 422Technology Entrepreneurship3
JTC 372Advanced Web Design and Management3
MATH 161Calculus for Physical Scientists II (GT-MA1)4
or MATH 255 Calculus for Biological Scientists II
MGT 330Creativity, Innovation, and Value Creation3
MGT 340Fundamentals of Entrepreneurship3
MGT 420New Venture Creation3
NR 322Introduction to Geographic Information Systems4
PHIL 410Gödel's Incompleteness Theorems3
PHIL 411Logic in Philosophy and Beyond3
PHIL 415Logic and Scientific Method3
PSY 252Mind, Brain, and Behavior3
PSY 352Learning and Memory3
PSY 452Cognitive Psychology3
PSY 454Biological Psychology3
PSY 456Sensation and Perception3
PSY 458Cognitive Neuroscience3

Distinctive Requirements for Degree Program:

To prepare for first semester: The curriculum for the Computer Science major assumes students enter college prepared to take calculus. Entering students who are not prepared to take calculus will need to fulfill pre-calculus requirements in the first semester. All students must maintain a C (2.000) or better in CO 150 and in all CS, DSCI, MATH, STAT and departmental Technical Elective courses which are required for graduation.

Semester 1CriticalRecommendedAUCCCredits
MATH 160Calculus for Physical Scientists I (GT-MA1) X1B4
Select one course from the following:   4
CS1---No Prior Programming Experience X  
CS1--Prior Programming Experience X  
Department Approved Science (See list on Concentration Requirements Tab)  3A3
Arts and Humanities  3B3
Elective   1
MATH 124 and MATH 126 may be necessary for some students to fulfill pre-calculus requirements.X   
 Total Credits   15
Semester 2CriticalRecommendedAUCCCredits
CO 150College Composition (GT-CO2)  1A3
CS 165CS2--Data Structures X 4
Department Approved Science with Lab (See list on Concentration Requirements Tab)  3A4
Diversity and Global Awareness  3E3
Elective   1
CO 150 must be completed by the end of Semester 2 with a grade of C or better.X   
CS 163 or CS 164 must be completed by the end of Semester 2.X   
 Total Credits   15
Semester 3CriticalRecommendedAUCCCredits
CS 220Discrete Structures and their Applications X 4
CS 270Computer Organization X 4
Select one course from the following:   3
Introduction to Applied Statistical Methods    
Introduction to Biostatistics    
Intro to Theory and Practice of Statistics    
Historical Perspectives  3D3
 Total Credits   14
Semester 4CriticalRecommendedAUCCCredits
CS 201/PHIL 201Ethical Computing Systems (GT-AH3) X3B3
CS 253Software Development with C++ X 4
Social and Behavioral Sciences  3C3
Select one course from the following:   3-4
Linear Algebra for Data ScienceX   
Linear Algebra IX   
Electives   2-3
CS 165 and CS 220 and CS 270 must be completed by the end of Semester 4.X   
MATH 160 and MATH 369 or DSCI 369 must be completed by the end of Semester 4.X   
 Total Credits   16
Semester 5CriticalRecommendedAUCCCredits
CS 314Software Engineering  4A,4B3
CS 320Algorithms--Theory and Practice X 3
CS 370Operating Systems X 3
Technical Elective (See list on Concentration Requirements Tab)   3-4
Advanced Writing  23
CS 253 must be completed by the end of Semester 5.X   
 Total Credits   15-16
Semester 6CriticalRecommendedAUCCCredits
Two CS courses numbered 300- or above, excluding 380-399 and 480-499 X 6-8
Systems Elective (See list on Concentration Requirements Tab)   4
Electives   2-5
CS 314 and CS 320 and CS 370 must be completed by the end of Semester 6.X   
 Total Credits   14-16
Semester 7CriticalRecommendedAUCCCredits
Systems Courses (See list on the Concentration Requirements Tab)   8
Electives   7
At least four Upper-Division CS classes must be completed by the end of Semester 7.X   
 Total Credits   15
Semester 8CriticalRecommendedAUCCCredits
Systems Courses (See list on the Concentration Requirements Tab)X  8
ElectivesX  7
The benchmark courses for the 8th semester are the remaining courses in the entire program of study.X   
 Total Credits   15
 Program Total Credits:   120