Brescia University’s online MSIS is a 30-credit-hour graduate program designed for working professionals who want to advance into technology leadership roles without pausing their careers. Delivered entirely online in accelerated 8-week terms, the program fits around your schedule.
You will build practical skills in data analytics, information security, and emerging technologies, including artificial intelligence and machine learning — skills that employers across every industry are actively seeking.
Through hands-on coursework, you will learn to design, implement, and evaluate information systems solutions while strengthening your critical thinking, problem-solving, and communication skills. The program prepares you to address complex technology challenges across a variety of organizations and industries.
The MSIS culminates in a capstone project that allows you to apply your knowledge and skills to a real-world problem, demonstrating your ability to develop effective information systems solutions.
Graduates are prepared for careers such as systems analyst, software engineer, database administrator, network architect, cybersecurity analyst, data analyst, and artificial intelligence specialist. A strong emphasis on ethics and governance ensures you are ready to lead with responsibility and integrity in today’s technology-driven organizations.
Students who wish to apply to Brescia University’s MS in Information Systems program should submit the following:
- A FREE online application.
- Official transcript showing an earned bachelor’s degree from a college or university accredited by a recognized regional accrediting association.
- Undergraduate GPA of 2.5.
- Upon written request, applicants with a marginally lower GPA may be considered if they address remediation of the reason(s) for the low GPA and their ability to manage successfully the demands of a rigorous graduate program. To demonstrate their academic readiness, students may choose to submit any or all of the following examples:
- Successful completion of graduate coursework
- Strong GRE scores
- Strong writing skills
- Strong work history in the field (multiple years, with references)
- Students for whom English is a second language must meet the minimum acceptable score for the Test of English as a Foreign Language (TOEFL)
- 550 on the paper-based TOEFL, or
- 79 on the iBT TOEFL
The MS in Information Systems Program does not grant academic credit for life experience or previous work experience in lieu of academic courses. Note: The Program reserves the right to require an interview of any applicant.
Graduation Requirements
- Complete all coursework with a GPA of 3.0.
- Complete 30 credit hours of academic work.
- Apply for candidacy after completing a minimum of fifteen credit hours and before completing twenty-one credit hours.
- Students complete all requirements within five years.
Program Fundamentals
100% Online Flexibility – Designed specifically for working professionals, the program offers a high-quality, engaging online environment that adapts to student schedules
30 Total Credits – The degree is comprised of 18 hours of core curriculum, 9 hours of specialized concentration, and a 3-hour capstone project.
Career-Focused Outcomes – The program combines academic excellence with a focus on readiness for advancing roles in the dynamic field of information technology.
Specialized Pathways
Information Security – Focuses on security management, network security, and cybersecurity risk and compliance (MIS 610, 620, 630)
Data Analytics – Emphasizes visualization, big data technologies like Hadoop and Spark, and machine learning models (MIS 640, 650, 660)
Artificial Intelligence – Explores foundational AI techniques, deep learning, neural networks, and AI ethics (MIS 670, 680, 690)
The Core Foundation
MIS 510-530: Systems & Data
Covers 15 Foundations, Systems Analysis and Design, and Database Management Systems to build a strong technical base
MIS 540-550: Engineering & Cloud
Focuses on Software Engineering and Network and Cloud Computing, exploring architectures, protocols, and agile methodologies
MIS 560: Ethics & Governance
Addresses critical legal and societal issues, including privacy, regulatory compliance, and responsible decision-making frameworks
MIS 590: Culminating Capstone
Real-World Problem Solving: Students synthesize all knowledge and skills to address a genuine challenge faced by an organization or community
Mastery Demonstration: The capstone project serves as a final opportunity for students to showcase proficiency and mastery of the program’s learning outcomes
MIS 510: Foundations of Information Systems
This course introduces the fundamental concepts and principles of information systems, including hardware, software, data, and their interrelationships. Students will explore the role of information systems in organizations and society, as well as the challenges and opportunities presented by emerging technologies.
MIS 520: Systems Analysis and Design
This course provides an in-depth study of the processes and techniques used in the analysis, design, and development of information systems. Students will learn to apply various methodologies and tools to effectively gather user requirements, model system components, and create system specifications.
MIS 530: Database Management Systems
Covers the design, implementation, and management of relational databases. Topics include data modeling, normalization, SQL, and the use of DBMS software to manage and query large-scale data.
MIS 540: Software Engineering
Examines software development life cycles, agile methodologies, and software quality assurance. Students gain hands-on experience in designing, testing, and maintaining software systems.
MIS 550: Network and Cloud Computing
Explores network architectures, protocols, and cloud service models. Students learn to design, implement, and manage networked and cloud-based information systems, with a focus on security and scalability.
MIS 560: Information Systems Ethics and Governance
Addresses ethical, legal, and societal issues in IS, including privacy, intellectual property, and regulatory compliance. Emphasizes frameworks for responsible decision-making and IS governance.
MIS 590: Capstone Project in Information Systems
Students undertake a comprehensive project synthesizing knowledge and skills gained throughout the program. Includes problem identification, research, systems design, solution implementation, and presentation.
MIS 610: Information Security Management
Explores principles and practices of managing information security, including risk analysis, policy development, and implementation of security frameworks in organizational settings.
MIS 620: Network Security
Covers secure network design and implementation. Topics include encryption, authentication, firewalls, intrusion detection systems, and defense-in-depth strategies.
MIS 630: Cybersecurity Risk and Compliance
Introduces risk management frameworks, regulatory requirements (e.g., GDPR, HIPAA), and the development of compliance strategies within cybersecurity programs.
MIS 640: Data Analytics and Visualization
Covers statistical analysis, data wrangling, and visualization using modern analytics tools. Students develop models and dashboards to inform business and technical decisions.
MIS 650: Big Data Technologies
Focuses on technologies for storing, processing, and analyzing large-scale datasets, including Hadoop, Spark, and NoSQL databases.
MIS 660: Machine Learning for Data Analytics
Introduces supervised and unsupervised learning, classification, clustering, and regression models. Students implement models using Python and industry-standard libraries.
MIS 670: Introduction to Artificial Intelligence
Explores foundational AI techniques including search algorithms, logic, planning, and knowledge representation. Students gain exposure to modern AI applications across industries.
MIS 680: Machine Learning
Covers principles and applications of machine learning including supervised, unsupervised, and reinforcement learning. Emphasis on implementation and evaluation in applied settings.
MIS 690: Deep Learning and Neural Networks
Examines deep learning architectures such as CNNs, RNNs, and GANs. Students implement models in frameworks like TensorFlow and PyTorch to solve complex AI challenges.