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Online Master of Science in Data Science Curriculum

Curriculum Details

30 Total Credits Required

You will take 10 courses to earn your Master of Science in Data Science online at Concordia University, St. Paul. The program includes specialized data science courses that explore topics in AI technologies, machine learning, data science software, natural language processing (NLP), Agile methods, DevOps perspectives, and more.

Although you can complete this program in 18 months, your eligible transfer credits could affect the time it takes you to finish. You can transfer up to 15 credits to the online Master of Science in Data Science program.

Required Courses

Credits

This course provides a comprehensive exploration of Enterprise Architecture (EA), focusing on strategic planning and technical research. It equips students with the skills to design, implement, and manage architectures that align an organization’s business strategy, processes, information systems, and technology infrastructure to achieve success.

The CSP Project Management, Systems Development & Risk course offers an integrated study of project management principles, systems development methodologies, and risk management practices within the context of complex global IT projects. This course is designed for students and professionals seeking a comprehensive understanding of how to effectively manage projects, develop robust IT systems, lead diverse multi-disciplined teams and proactively address project risks for successful project delivery.

The CSP Database Systems course offers a comprehensive study of the principles, design methodologies, and practical applications of database management systems to support the enterprise of the future (DBMS). This course is designed for students and professionals interested in understanding the core concepts of database systems and developing the skills to design, implement, and manage databases and datasets for various applications.

The CSP Artificial Intelligence and High-Performance Computing and Ethical Considerations course offers an integrated study of the principles, techniques, ethical considerations and applications of artificial intelligence (AI) in combination with high-performance computing (HPC). This course is designed for students and professionals interested in harnessing the power of advanced computing technologies to develop and deploy AI solutions that can process vast amounts of data and solve complex problems at scale.

The CSP Cloud Architecture and Infrastructure course provides a comprehensive study of cloud computing principles, design methodologies, and best practices for architecting scalable and reliable cloud-based, hybrid, and multi-cloud solutions assisting global organizations design and build the architectures and infrastructures of the future. This course is designed for students and professionals interested in understanding how to design, deploy, and manage cloud infrastructure to meet the demands of modern global enterprises utilizing advanced applications and services.

The CSP Machine Learning and Artificial Intelligence (AI) course offers an in-depth study of the principles, algorithms, and applications of machine learning and AI technologies. This course is designed for students and professionals seeking to develop a strong foundation in both machine learning and AI and to apply these powerful techniques in solving real-world challenges across diverse domains.
The CSP Data Science Tools and Technologies course provides an in-depth exploration of the essential tools, software, and programming languages used in the field of data science. This course is designed for students and professionals seeking to gain hands-on experience with the tools that support the end-to-end data science workflow, from data acquisition and preprocessing to analysis, modeling, and visualization.
The CSP Natural Language Processing (NLP) course offers a comprehensive study of the principles, methodologies, and applications of NLP, a branch of artificial intelligence that focuses on the interaction between computers and human language. This course is designed for students and professionals interested in understanding and developing technologies that enable machines to understand, interpret, and generate human language driving comprehensive business and industry outcomes.
The CSP DataOps – Data Operations for Agile Data Management course provides a comprehensive study of the principles, practices, and tools related to DataOps, a methodology that combines the principles of DevOps with data management to enable efficient and agile data and business processes. This course is designed for students and professionals interested in streamlining data workflows, improving collaboration between data teams, and maximizing the value of data in an organization through insights and automation.
The CSP Reinforcement Learning course offers an in-depth exploration of the principles, algorithms, and applications of reinforcement learning, a subfield of artificial intelligence focused on training agents to make decisions in dynamic and uncertain environments based in a reward like framework. This course is designed for students and professionals interested in understanding and implementing advanced machine learning techniques that enable agents to learn from interaction and achieve complex tasks through trial, error, and rewards based frameworks.

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