MSc Data Science with Artificial Intelligence (AI)

University of Sunderland

We've designed this course to meet the current needs of employers. You'll study topics including:

• Applied statistics
• Big data technologies and ethics
• Applied artificial intelligence
• Business intelligence and data visualisation.

You'll gain practical experience throughout the course, working with industry‑standard tools and technologies. This hands‑on approach will give you the confidence to be productive and effective when you go out into the workplace.

You'll also hear from external experts, whose experience will enrich your learning. They'll share their knowledge through guest lectures, master classes, seminars, and careers sessions.

By the end of the course, you'll:

• Have advanced expertise in AI, enabling you to design, evaluate, and apply intelligent models to real‑world problems
• Develop strong analytical and statistical skills. These will enable you to interpret complex datasets and support evidence‑based decision‑making
• Gain in‑depth understanding of modern data technologies and ethical considerations, preparing you to work responsibly with large‑scale data
• Be able to design data visualisation and business intelligence solutions that present insights effectively
• Be prepared to contribute to data‑driven projects and research in a meaningful way
• Be able to apply advanced technical, problem‑solving and collaborative skills in professional environments.

On completion of this course, you'll be able to progress in some of the most attractive fields and industries. You could advance your career in a wide range of roles, including:

• Roles in data science, artificial intelligence, and analytics: For example, data scientist, AI specialist, machine learning engineer, or data analyst
• Positions across data‑driven industries: For example, data engineer, business intelligence developer, or insight analyst.
• Careers in emerging AI and data‑intensive fields: For example, from AI consultancy to big‑data engineering and predictive modelling.