USCB Master’s in Computational Science

Program Overview

Department of Computer Science & Mathematics

The University of South Carolina Beaufort offers an MS in Computational Science. Instruction is available fully online, blended, or in-person on the Bluffton and Hilton Head Island campuses. Students may start in either the Fall or Spring semester. Class sizes are small—generally under 20—and the program admits up to 20 graduate students per year.

Machine Learning Focus

The curriculum explicitly includes machine learning through core courses in Advanced Statistical Methods, Data Mining covering classification and clustering techniques, Digital Image Processing, and Data Visualization. Students gain proficiency in scientific programming languages like Python, Java, and C++, along with computational tools such as MATLAB for modeling and simulation.

Curriculum

The curriculum covers advanced methods for solving real-world problems in the physical, biological, and engineering sciences and includes hands-on machine learning experience.

CSCI B500 – Practical Computing for Computational Scientists
This course introduces software engineering techniques for solving mathematical, scientific, and engineering problems. Students gain hands-on experience with UNIX/Linux operating systems while applying computational methods to real-world scenarios. Prerequisite: enrollment in the CSCI M.S. program or instructor consent.

CSCI B501 (STAT B501) – Advanced Statistical Methods
Students explore data description and analysis techniques, including regression, spectral methods, estimation, and forecasting. Emphasis is placed on graphical presentation and the use of statistical software throughout the course. Prerequisite: STAT B340 or instructor consent.

CSCI B502 (MATH B502) – Numerical Analysis for Computing
This course applies probabilistic methods, entropy concepts, linear algebra techniques, risk analysis, and optimization algorithms to discrete applied math problems. Students learn performance analysis and approximation methods critical to computational work. Prerequisites: CSCI B280 and MATH B240, or instructor consent.

CSCI B515 – Topics in Computational Science
A rotating seminar covering current themes and advanced problems in computational science. Students tackle emerging research topics and present solutions using specialized computational tools. Prerequisite: undergraduate programming experience, CSCI B500, or instructor consent.

CSCI B516 – Data Communications and Networking
Covers network architectures, protocols, topologies, and internetworking for LANs, MANs, and WANs. Students study network access control and advanced communication strategies in modern data networks. Prerequisite: programming experience, CSCI B500, or instructor consent.

CSCI B520 – Advanced Topics in Database Systems
Delves into sophisticated database design, implementation, and manipulation techniques. Students work with advanced data-processing tools and software to manage complex datasets. Prerequisite: CSCI B320 or instructor consent.

CSCI B522 – Data Mining
Introduces methods for preparing, analyzing, and extracting patterns from large datasets. Topics include feature abstraction, classification, clustering, association rules, spatial and sequence mining, and validation techniques. Prerequisites: CSCI B501 and CSCI B502.

CSCI B550 – Systems Modeling and Simulation
Students learn to build and analyze simulation models using computational tools. The course covers system dynamics, input/output analysis, and performance evaluation of complex systems. Prerequisites: CSCI B500, B501, B502, or instructor consent.

CSCI B563 – Digital Image Processing
Focuses on computational algorithms for image filtering, segmentation, and feature extraction. Students apply theory to process and analyze digital images for various applications. Prerequisites: CSCI B500, B501, and B502.

CSCI B566 – Data Visualization II
Teaches advanced visualization methods grounded in graphic design, perceptual psychology, and cognitive science. Students develop algorithms to create clear, effective visual representations of complex data. Co-requisites: CSCI B500 and B501, or instructor consent.

CSCI B569 – High Performance Computing
Introduces parallel algorithm design, implementation, and performance tuning on advanced computing architectures. Students gain experience with parallel languages and high-performance computing facilities. Prerequisites/Co-requisites: CSCI B500, B501, B502, or instructor consent.

CSCI B570 – Software Systems Design and Implementation
Covers the full software engineering lifecycle, including planning, design, implementation, testing, and documentation of real-world systems. Prerequisite: CSCI B500 or instructor consent.

CSCI B599 – Independent Study
Offers one to three credits for individually supervised research projects in computational science. Students propose and execute a focused study under faculty guidance.

CSCI B601 – Principles of Computer Security
Examines core concepts in operating system, network, software, and web security. Students learn to identify threats and implement protective measures for computing systems. Prerequisite: CSCI B201 or instructor consent.

CSCI B622 – Data Management and Analytics
Provides foundations in relational data modeling, querying, and management. Students apply analytics techniques to derive insights from structured data. Prerequisite: CSCI B520 or instructor consent.

CSCI B699 – Industrial or Research Internship
Grants one to three credits for full-time, practical work experience in computational science, arranged and approved by the department.

CSCI B797 – Research
Enables three to six credits of guided research in computational science, culminating in a report or publication. Students work closely with faculty mentors.

CSCI B799 – Thesis or Project
Allocates three to six credits for completion of a master’s thesis or capstone project. Students demonstrate mastery through original research or substantial software development.

Program Duration and Credit Requirements

Total Credit Requirement

  • 30 credit hours total
    • 6 credits in mathematics & statistics
    • 10 credits in core computational science courses
    • 9 credits in electives
    • 3–6 credits in capstone work

Completion Paths

  • MS Thesis Option
    • 24 coursework credits + thesis
    • Geared toward PhD preparation
  • MS Project Option
    • 24 coursework credits + software-development project
    • Geared toward EdD preparation
  • MS Coursework Option
    • 27 coursework credits + internships

Time to Degree

The typical completion time is two years, though accelerated BS-to-MS students can finish in one year following their bachelor’s degree by taking up to 12 graduate credits during their undergraduate program.

Tuition

Tuition for full-time resident students is $14,120 ($30,146 for non-residents, including fees).

Per-credit costs are $572.25 for residents and $1,240 for non-residents, making USCB the most affordable four-year university in South Carolina.

Graduate assistantships are available for selected students, providing financial support opportunities. Additional costs include books, supplies, parking fees ($25 per semester), and one-time new student and matriculation fees.

Ideal Candidates

Bachelor’s degree in one of:

  • Computer Science
  • Computer Engineering
  • Computational Science
  • Information Technology
  • Software Engineering

STEM graduates in:

  • Life Sciences
  • Mathematics
  • Statistics
  • Engineering
  • Physics
  • Chemistry

Admissions & Requirements

Academic Requirements

  • Minimum GPA: 3.0
  • Official transcripts from all institutions
  • GRE scores (waived for USCB graduates or professionals with ≥2 years’ relevant experience)

Supporting Documents

  • Two letters of recommendation
  • Current résumé

International Student Requirements

  • TOEFL iBT: minimum 77, or IELTS: minimum 6.0
  • Credential evaluation of foreign transcripts

Additional Coursework

  • May be required for applicants lacking foundational prerequisites

Career Outcomes

Graduates are prepared for careers in academia, government, and industry requiring expertise in programming, modeling, computing, and software system management, with strong emphasis on interdisciplinary applications across science and engineering fields.

Potential Career Paths

  • Computational Scientist
    Design and implement algorithms and simulations to solve complex problems in physics, engineering, or biology.
  • Data Scientist / Machine Learning Engineer
    Develop predictive models and data-driven solutions using statistical methods and machine learning techniques.
  • High-Performance Computing (HPC) Specialist
    Optimize and deploy parallel code on supercomputers or clusters for large-scale simulations and data processing.
  • Systems Modeling & Simulation Engineer
    Build and analyze dynamic models of real-world systems to forecast behavior and evaluate performance under varied conditions.
  • Scientific Software Developer
    Create and maintain domain-specific software tools in languages like Python, C++, or Java for research and industry applications.
  • Data Engineer / Database Architect
    Design, implement, and manage large-scale data storage systems and pipelines to support analytics and visualization.
  • Digital Image Processing Analyst
    Apply computational techniques to medical imaging, remote sensing, or computer vision projects in healthcare, defense, or environmental science.
  • Computational Biologist / Bioinformatics Analyst
    Use algorithms and statistical methods to interpret biological data, such as genomic sequences or protein structures.
  • Network & Security Analyst
    Leverage knowledge of computer security, communications protocols, and data management to protect and optimize networked systems.
  • Research & Development Scientist
    Conduct interdisciplinary R&D in government labs, academic centers, or private-sector innovation teams, translating scientific questions into computational experiments.