MSc Business Analytics and Artificial Intelligence

1 Year On Campus Masters Program

University of Hull

Program Overview

The MSc Business Analytics and Artificial Intelligence at the University of Hull combines data-science, statistical modelling and AI with business analytics — preparing graduates to apply AI and data-driven decision-making in management, marketing, finance or operations. It suits students from business, economics, computing or quantitative backgrounds aiming for roles in data analytics, AI-enabled business strategy, or consultancy.

Curriculum structure

Students begin with core modules such as Business Analytics, Programming for Data Science and AI, and Statistical Methods & Data Mining, building skills in data handling, predictive analytics and algorithmic modelling. They then study Machine Learning & Artificial Intelligence for Business, applying ML and AI models to business datasets, forecasting, customer analytics and strategic decision-making. The programme concludes with an Individual Project / Dissertation, where each student designs an AI- or data-analytics-driven business solution or research project, integrating analytics and business insight.

Focus areas: “business analytics, data mining, machine learning, predictive modelling, AI in business strategy, data-driven decision making”

Learning outcomes: “analyse business data; build and deploy ML/AI models for business problems; interpret analytics for business insight; conduct independent data-driven business research or applications; prepare for data-analysis or business-strategy roles.”

Professional alignment: Prepares graduates for roles as business analysts, data scientists, AI consultants, or strategic planners in sectors reliant on data-driven decisions and AI.

Reputation (employability): The programme meets rising demand for analytics-skilled professionals; its dual focus on business and AI gives graduates competitive advantage in commerce, finance, consultancy and emerging data-intensive industries.

Experiential Learning (Research, Projects, Internships etc.)

Students gain practical skills by applying AI and analytics techniques to solve real-world business problems, using the University's high-performance computing infrastructure and business simulation tools. This applied learning is central to the curriculum:

Software: Training in Python, R, and SQL with key AI and analytics libraries (TensorFlow, Scikit-learn, Pandas).
Business Analytics Projects: Hands-on work with business datasets for forecasting, optimization, and decision support.
Research Project: An individual dissertation focusing on an AI application in business analytics.
Computing Facilities: Access to the University's High-Performance Computing (HPC) resources.
Industry Tools: Exposure to business intelligence platforms and data visualization tools used in industry.

Progression & Future Opportunities

Graduates of the University of Hull's MSc Business Analytics and Artificial Intelligence develop expertise in Python, SQL, Power BI, machine learning, and AI-driven decision-making, enabling them to transform complex data into strategic business insights across sectors like finance, retail, and logistics. The program emphasizes practical projects, ethical AI, and commercial awareness, preparing alumni for high-impact roles in a data-driven economy amid growing demand for analytics professionals. Typical job roles: AI Business Analyst, Business Intelligence Analyst, Analytics Consultant, Operations Analyst.

University services: Careers service offers CV support, interview practice, job fairs, Handshake platform, and industry projects.
Employment stats/salary: High demand in analytics; roles start at £35,000–£50,000.
Partnerships: Ties to Hull's business school network and sector employers for real-world challenges.
Accreditation value: AMBA/AACSB-accredited business school ensures lasting prestige.
Outcomes: Careers in consulting, BI, optimization; further study or leadership.

Further Academic Progression: Pursue PhD in business analytics, AI, or data science at Hull, leveraging dissertation for advanced research.

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