Undergraduate degree

In order to successfully enter the Computer Science Program, the student must first enroll into First Year Experience. Once they have completed all of the requirements (listed below), they can transfer into the Computer Science Department. 

  1. ≥ 2.50 cumulative and UM cumulative GPA
  2. ≥ 2.50 cumulative and UM cumulative Computer Science GPA
  3. ≥ 2.25 cumulative Math, Science and Engineering GPA
    • This includes all math, science and engineering courses completed at time of application and all grades for any repeated courses (with no GPA adjustment for the grade replacement policy)
  4. Must have a C or better in all of FE 1100, Math 1214 and Math 1215
  5. Must have a C or better in both Comp Sci 1200 and Comp Sci 1570
  6. Must not be on probation nor deficiency

 

Grad Track Pathway

Undergraduates currently majoring in Computer Science at Missouri S&T may opt to apply for a Grad Track Pathway, which allows students to transfer nine credit hours from their Missouri S&T bachelor’s degree to their Computer Science master’s degree. In this pathway, a student can achieve both degrees faster than if pursuing the degrees separately. The benefits of the pathway for admitted students include:

  • nine hours of 5000-level or above Computer Science coursework may be transferred from their Missouri S&T bachelor’s degree to their Computer Science master’s degree
  • the credit hours taken as part of the pathway may be taken at the lower undergraduate tuition rate
  • the GRE is not required for admission into the master’s degree
  • thesis or non-thesis options are available 
  • work on a thesis project may begin before the bachelor degree requirements are completed (if thesis option is chosen)

 To be eligible for the Grad Track Pathway, a Computer Science undergraduate student must be:

  • one year from graduation of their bachelor’s degree (excluding the semester they are currently enrolled) (This is waived for Spring 2020 graduates)
  • have at least a 3.50 GPA in all CompSci courses taken at Missouri S&T
  • have a 3.0 cumulative GPA

 

Students wishing to submit GTP paperwork should send a completed form to csdept@mst.edu before obtaining signatures from advisors and administrators, with the exception that if a thesis option is chosen, then the student should consult with a potential thesis advisor first.

For questions, contact Dr. Taylor at taylor@mst.edu

 Admission and Course Approval Form

Admissions and Standards

Computer Science Request for Grad Track Pathway

 

New Summer Online Course Offerings

  • COMP SCI 1200 Discrete Mathematics for Computer Science
  • COMP SCI 1570 Introduction to C++ Programming
  • COMP SCI 1580 Introduction to Programming Lab
  • COMP SCI 1575 Data Structures
  • COMP SCI 1585 Data Structures Lab
  • COMP SCI 5001 Applied Social Network Analysis
  • COMP SCI 5402 Introduction to Data Mining

COMP SCI 1200 Discrete Mathematics for Computer Science

  • Instructor: Mike Gosnell
  • When: MTWRF 3pm-4pm

A rigorous treatment of topics from discrete mathematics which are essential to computer science. Principal topics include: formal logic (propositional & predicate), proof techniques, mathematical induction, program correctness, sets, combinatorics, probability, relations, functions, matrices, graph theory and graph algorithms. Prerequisite: A grade of "C" or better in both Comp Sci 1570 and one of Math 1120, Math 1140, Math 1208, and Math 1214.

COMP SCI 1570 Introduction to C++ Programming

  • Instructor: Mike Gosnell
  • When: MTWRF 10:20am-11:20am

Programming design and development using C++. Emphasis placed on problem solving methods using good programming practices and algorithm design and development. Topics included are syntax/semantics, logical, relational and arithmetic operators, decision branching, loops, functions, file I/O, arrays, output formatting, C-strings, and an introduction to Object-Oriented Programming including the development and use of classes. Prerequisite: Accompanied by Comp Sci 1580.

COMP SCI 1580 Introduction to Programming Lab

  • Instructor: Mike Gosnell
  • When: MTWR 11:30am-12:30pm

Practical applications of concepts learned in Computer Science 1570. Hands-on instruction in C++ developing, debugging, and testing programming projects. Prerequisite: Accompanied by Comp Sci 1570.

COMP SCI 1575 Data Structures

  • Instructor: Dr. Gerry Howser
  • When: MTWRF 1:50pm-2:50pm

A continuation of Object-Oriented Programming, with emphasis on the efficient organization of data through Abstract Data Types and Data Structures. Topics include Linked Lists, Vectors, Stacks, Queues, Trees, Hash Tables, Graphs and their use in a variety of algorithms. Recursive programming techniques are also covered. This course is programming intensive. Prerequisite: Grade of "C" or better in Comp Sci 1570.

COMP SCI 1585 Data Structures Lab

  • Instructor: Dr. Gerry Howser
  • When: MWF 3:30pm-4:50pm

Hands-on instruction in programming development tools such as version control systems, integrated development environments, debuggers, profilers, and event-based programming environments. Exercises will complement the concepts presented in Comp Sci 1575. Prerequisite: Preceded or accompanied by Comp Sci 1575.

 

COMP SCI 5001 Applied Social Network Analysis

  • Instructor: Dr. Jennifer Leopold
  • When: MTWRF 1:30pm-3:40pm July 1st- July 31st
  • Available for Enrollment Soon

In this course you will learn how to use networks to model and analyze relationships between people, artifacts, and ideas. Analyses will include identification of both communities and key individuals, and the modeling of diffusion processes such as in the spread of diseases. Methods will be practiced in programming assignments using Python or R.  Prerequisite: C or better in CS 2500

COMP SCI 5402 Introduction to Data Mining

  • Instructor: Perry Koob
  • When: MTWRF 12pm-1pm

The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real-world applications. The core topics to be covered in this course include classification, clustering, association analysis, data preprocessing, and outlier/novelty detection. Prerequisites: A grade of "C" or better in all of Comp Sci 2300, Comp Sci 2500, and one of Stat 3113, Stat 3115, Stat 3117 or Stat 5643.

Program information and accreditation

Courses and research

As an undergraduate student, you’ll take traditional compsci courses in programming, languages, algorithms, data structures, databases, object-oriented design, architecture, and operating systems.

For your advanced undergraduate work, under the guidance of experienced faculty, you can choose to study and conduct research in:

  • Data Science and Machine Learning
  • Artificial Intelligence
  • Algorithms
  • Database Systems
  • Graphics and Graphical User Interface
  • Compilers
  • Computer Networks
  • Cyber Security
  • Distributed Computing and Operating Systems

And your research will support the department’s crosscutting areas of excellence: big data, critical infrastructure protection, cyber physical systems, and smart computing.

The program prepares you to be a problem-solver and innovator that is able to analyze a problem and propose a computing solution.  You will learn not only the technical skills, but also develop abilities to communicate, work with teams of people, and make informed judgements about your computing solutions with respect to societal, legal, and ethical principles.