2025-2026 Academic Catalog

Aug 13, 2025

2025-2026 Academic Catalog

Computer Science, MSCS

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Master of Science in Computer Science Overview

Computer science professionals are at the forefront of technological innovation. There is a high demand for experts who can develop performance-optimized systems that drive business impact and/or social impact through technology. Clark University’s Master of Science in Computer Science (MSCS) program equips you with the tools to tackle complex challenges, master data structures, and apply these skills to advance your career.

Our rigorous, outcome-focused curriculum emphasizes core computer science competencies while exploring cutting-edge areas such as machine learning, data mining, human-computer interaction, and the principles and applications of artificial intelligence. We prioritize skills-based education, empowering you to “tell a story with data” and enhance your impact on the business world.

With access to a diverse portfolio of electives, you can tailor your educational experience to align with your career aspirations. Concentrations are available in Intelligent Systems and AI Solutions Design, Data Intelligence, Human-Computer Interaction (HCI), and Software Engineering. Our curriculum provides a robust foundation in artificial intelligence, covering topics such as machine learning algorithms, deep learning, natural language processing, computer vision, and intelligent agents, preparing you to contribute to the rapidly evolving field of AI.

Learning Outcomes

Analysis and Design of Algorithms: Design, analyze, and implement algorithms for real-world software applications. The focus is on the evaluation of space and time complexity for different algorithms utilized in software applications.
Systems and Programming Languages: Evaluate various computing platforms, cloud services, containerized microservices, and programming languages to design and develop software applications in different domains. Apply the design principles for building compute-intensive and data-intensive software applications utilizing modern computer hardware infrastructure, containers, and programming models.
Software Engineering: Design and deliver holistic software solutions that demonstrate a comprehensive knowledge of software engineering principles and practices encompassing requirements elicitation, analysis, design, development, testing, cloud deployment, and methodologies.
Ethics and Social Responsibility: Evaluate ethical considerations specific to computer science contexts, ensuring responsible conduct and positive societal impact in the development and implementation of software applications.
Communication: Develop proficiency in technical writing, teamwork, and presentations, showcasing the ability to communicate complex concepts through written documentation, collaborate effectively in teams, and deliver engaging presentations for diverse audiences.
Artificial Intelligence: Design and implement intelligent systems that leverage large language models (LLMs), generative AI, retrieval-augmented generation (RAG), and multimodal techniques to build context-aware, domain-specific applications using vector-based retrieval and advanced reasoning methods.

Internship

Completing an internship is a requirement in this graduate program. Students are responsible for securing their own internship. While students will gain valuable experience, they will not earn academic credit for their internship.

Internship requirements:

Must include a minimum of 120 hours
Must be directly related to the student’s program of study
Must be approved by the Director of Career and Professional Development

Exceptions:

Students who have seven (7) or more years of professional work experience directly related to their program of study may seek to waive the internship requirement.
Students who currently work full-time in a field/industry directly related to their program of study.

Students seeking to waive the internship requirement must receive permission from the Director of Career and Professional Development by the end of their second semester in the master’s program.

Review the Internship Handbook for more information. Please connect with your academic advisor to plan your studies accordingly.

Course of Study

The MCSC degree requires ten (10) courses: four (4) core and six (6) electives. Electives can be fulfilled by completing one of four concentrations (Data Intelligence; Human Computer Interaction; Software Engineering, Intelligent Systems and AI Solutions Design) plus two (2) courses from below “Electives” list or six (6) courses from the “Electives” list.

Core Requirements

List of Concentrations

Data Intelligence

The Data Intelligence concentration equips students with knowledge and technical skills to develop robust and effective approaches for collecting, maintaining, and extracting actionable insights from large datasets that permeate businesses and governments. The program focuses on the practical statistical approaches to data collection and analysis that enable students to become contributors from day one, while also educating students about regulatory and ethical frameworks that are expected of data professionals. Students learn and practice skills to effectively communicate technical results in a clear and accessible manner, which is critical to ensure that data-driven changes are implemented in organizations.

The concentration provides students a blend of theory and practice to prepare students for continuous success in the broad and dynamic field of data analytics. Furthermore, the program encourages debate on issues such as data privacy, changing regulatory landscape, efficient technical solutions and communication strategies, simulating the environment in today’s data-driven corporations.

Learning Outcomes:

Develop technical solutions for data warehousing using SQL Databases, ETL processes, XML and other data formats
Develop Python-based code to perform data intake and data scrubbing
Use Python-based code to perform Exploratory Data Analysis, data visualization, and build scalable and reusable models
Identify the appropriate models (A/B testing, linear regression, logit regression, clustering, decision trees, random forest) and success metrics for specific analytical goals and refine models’ accuracy based on the selected metrics
Present and explain the models and their predictions
Create a robust data analysis and data processing cycles to ensure compliance with changing regulations

Data Intelligence (Complete all 4)

MSCS 3045 - Applied Data Analytics

MSDA 3040 - Modern Data Engineering

MSDA 3050 - Applied Machine Learning

MSCS 3295 - Advanced Data Intelligence Concepts

Human Computer Interaction (HCI)

The Human-Computer Interaction concentration equips the students with knowledge and technical skills to design and develop user-centered software applications for products and services that are easy to interact with and have rich infographics. Students learn the persuasive design principles for User Interface (UI) and User Experience (UX) and the technologies to develop the user-centered solutions that integrate digital media and social media for digital transformation of business processes and the implementation of business strategies. Students also learn how to use modern technologies to execute online services and communicate content utilizing the digital public space. This concentration integrates concepts and methods from computer science, social science, artificial intelligence, data science, and graphic design.

Learning Outcomes:

User-Centered Design Proficiency: Apply user-centered design methods and digital media to proficiently generate digital content, create prototypes, design information, and enhance overall user experience.
Exploratory Interaction Interface Expertise: Build exploratory interaction user interfaces for the knowledge graph of social media by leveraging modern database systems and employing artificial intelligence methods.
UI and UX Development Skills: Develop User Interface (UI) and User Experience (UX) using contemporary programming languages, ensuring seamless and intuitive interaction for enhanced user satisfaction.
Low-Code/No-Code Proficiency: Utilize low-code/no-code tools for Exploratory Data Analysis, enhancing efficiency and agility in interface development.
Data-Driven Digital Transformation: Apply data-driven methods and digital media to drive the digital transformation of business processes and services, integrating insights from computer science, social science, data science, and graphic design.
Generative AI: Utilize generative AI models and large language models (LLM) to create embeddings and perform semantic searches.

Human Computer Interaction (Complete all 4)

MSDA 3060 - Data Visualization and Story Telling

MSCS 3021 - Human Computer Interaction

MSCS 3025 - Usability Engineering

MSCS 3027 - Social Informatics

Software Engineering

The Software Engineering concentration teaches students the software development processes, software development methodologies, software quality assurance and quality control, and software project management. The Software Engineering concentration prepares students to develop software products and effectively manage software projects.

Learning Outcomes:

Software Development Processes. Apply agile and traditional waterfall software development processes in the development of software products, continuous integration and continuous delivery (CI/CD) pipelines, and software configuration management.
Software Development Methodologies. Develop requirements specification, analysis, and design artifacts. Utilize the object-oriented development methods and technologies, modern software architectures and frameworks, design patterns, and Unified Modeling Language (UML) in the design and development of software products.
Software Quality Assurance and Quality Control. Create and execute the software testing plan and test cases. Collect and analyze the software product and process metrics for the improvement of defect removal effectiveness. Utilize modern test case automation tools to execute test cases. Review process maturity and quality management standards.
Software Project Management. Create the software project plan using different effort estimation techniques. Identify and select the software metrics used in the estimation of the size, cost, and schedule of the software projects. Build the skills to manage and lead the software development teams.
Software Systems and Platforms. Build on-premises software applications and containerized cloud-native microservices. Engineer software systems for responsiveness, reliability, availability, security, resilience, and scalability.

Software Engineering (Complete all 4):

MSCS 3250 - Software Design and Architecture

MSCS 3252 - Software Project Management

MSCS 3254 - Software Quality Assurance and Testing

MSCS 3290 - Advanced Software Engineering Concepts

Intelligent Systems and AI Solutions Design

This concentration enhances the MSCS curriculum by integrating advanced AI methodologies with core computing principles. The Intelligent Systems and AI Solutions Design concentration will focus on equipping students with the expertise to design, develop, and deploy intelligent systems capable of learning, reasoning, and autonomous decision-making.

Students in this concentration will gain proficiency in machine learning, neural networks, natural language processing, and generative AI, enabling them to create sophisticated AI-driven solutions. The curriculum bridges foundational computer science concepts with cognitive computing, multimodal AI, and automation, reinforcing the program’s commitment to both theoretical depth and applied innovation.

Learning Outcomes:

Master Theories and Algorithms in Generative, Retrieval-Augmented Generation (RAG), and Multimodal AI: Develop a robust understanding of foundational theories, algorithms, and techniques in generative AI, large language model (LLM), multimodal models, and RAG to effectively integrate and optimize these approaches in real-world applications.
Deploy and Optimize Business Automation with Generative AI and Vector Databases: Design and implement AI solutions that automate business processes, using vector databases for efficient data retrieval and RAG to enhance precision and relevance in automation tasks.
Develop Advanced Multimodal AI Agents: Build intelligent agents utilizing modern generative AI platforms and frameworks that leverage reasoning and inferencing capabilities, as well as vector databases for accurate and contextually relevant information retrieval across large datasets.
Apply Cross-Disciplinary Problem Solving with Generative and Retrieval-Augmented AI: Utilize vector retrieval and multimodal reasoning to solve industry-specific challenges across retail, supply chain, manufacturing, healthcare, finance, and social media, applying AI-driven inferencing to drive innovative, tailored solutions for customer service, marketing, sales, and distribution.

Intelligent Systems and AI Solutions Design (Complete all 4):

MSCS 3450 - Fundamentals of Artificial Intelligence

MSCS 3451 - Generative Artificial Intelligence

MSDA 3100 - Applied Deep Learning

MSCS 3453 - Developing LLM-Based AI Assistants

Electives

Any MSCS, MSDA, or MSIT course