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Master of Artificial Intelligence in Business

Accredited By
UCAM University, Spain
Intakes
January | May | September
Duration
1 Year

Where Artificial Intelligence Meets Business Leadership.

The Master of Artificial Intelligence in Business program  at UCAM & Delivered by Exeed ECX,is designed to equip future business leaders with the knowledge and capabilities required to harness the transformative power of AI within modern organizations. Delivered through a flexible online learning format, the program enables students to continue their professional commitments while advancing their education.

The MAIB is a comprehensive and interdisciplinary program that prepares learners to apply artificial intelligence techniques to address complex business challenges. The curriculum covers a wide range of subjects, including data analytics, machine learning, AI-driven business strategy, and human resource management enhanced by AI.

Students gain a strong foundation in AI algorithms and programming, alongside advanced topics such as deep learning and pattern recognition. The program also offers industry-focused modules exploring the application of AI across sectors such as healthcare, transportation, and finance, providing valuable real-world perspectives.

The program culminates in an applied capstone project, where students collaborate with industry partners to develop AI-based solutions for real business problems. Graduates are well-positioned for leadership roles in areas such as consulting, business analytics, and AI-driven research and innovation.

Students also have the opportunity to participate in the annual graduation ceremony, held either at the nearest satellite centre or at the Plymouth Marjon University campus in the United Kingdom.

Why Choose This Program?

Master of Artificial Intelligence in Business course stands out by offering AI education specifically tailored for real business impact, combining industry driven and practical learning with a flexible online format suited for working professionals, while developing future ready skills with a strong focus on leadership and strategic decision-making.

Here are the five primary reasons to choose this program:

  • Broad and versatile business education
  • Industry relevant curriculum
  • Strong employability and career flexibility
  • Focus on leadership, ethics, and global business
  • Pathway to advanced studies and entrepreneurship

Programme Structure & Curriculum

This section outlines the structure, content, and learning outcomes of the core modules within this qualification.

Module 1: Working With Data

This module introduces students to Python, a core programming language widely used in data science. It begins with the fundamentals, including environment setup, basic syntax, variables, data types, and operators required for data storage and manipulation. The module then progresses to control flow concepts and functions, providing hands-on experience with lists, tuples, dictionaries, conditional statements, loops, and function design. Alongside programming skills, students are introduced to essential mathematical concepts such as number theory, vectors, and matrices, enabling them to apply advanced Python libraries like NumPy for complex calculations and data analytics.

Learning Outcomes

By the end of this module, students will be able to:

  • Explain Python’s basic syntax, variables, data types, and operators.

  • Describe and apply core data structures and control flow tools for effective data manipulation.

  • Understand and use Python libraries such as NumPy, pandas, and matplotlib for data processing and visualization.

  • Apply Python programming alongside mathematical concepts to solve complex computational and analytical problems.

If you want this shorter, more student-friendly, or aligned with a course handbook, I can refine it further.

Content Covered:

  • Python introduction and setup.
  • Python basic syntax.
  • Python variables and data types.
  • Usage of Python operators.
  • Python data structures.
  • Python conditional statements.
  • Implementation of Python loops.
  • Creation of Python functions.
  • Mathematical concepts integration.
  • NumPy for numerical computations.
  • Data manipulation with pandas.
  • Data visualization using matplotlib.
  • Descriptive and inferential statistics.
  • Probability theory application.
  • Understanding Bayes’ theorem.
  • Correlation and Regression Analysis

Module 2: Data Analytics In Business Process

This module focuses on the principles of developing accurate and reliable spreadsheet models by converting conceptual ideas into mathematical models and implementing them effectively using spreadsheets. Students gain practical knowledge of key analytical tools in Excel, advanced Excel functions, and techniques for auditing spreadsheet models to ensure accuracy and reliability. The module also covers decision-making tools such as decision analysis, payoff tables, and decision trees. In addition, learners explore Microsoft Power BI to transform data into meaningful insights for business problem-solving. The module introduces advanced Power BI features, including predictive analytics, data visualization, and data analysis expressions, enabling students to design interactive dashboards and reports that support data-driven decision-making.

Learning Outcomes

By the end of this module, students will be able to:

  • Understand the importance of data visualization and the role of Advanced Excel in analytical processes.

  • Select appropriate visual representations for different types of datasets.

  • Develop interactive dashboards and visual reports using Power BI and Advanced Excel.

  • Analyze and interpret data visualizations to generate insights that support effective decision-making.

Content Covered:

  • Getting started with Excel.
  • Backstage view
  • Understanding OneDrive
  • Creating and opening workbooks
  • Saving and sharing workbooks
  • Cell Basics
  • Modifying Columns, Rows and Cells
  • Formatting Cells
  • Working with Multiple Worksheets
  • Introduction to Formulas
  • Cell referencing
  • Introduction to Basic Functions
  • Pivot Tables
  • Get started with Microsoft data analytics.
  • Prepare data for analysis.
  • Model data in Power BI
  • Visualize data in Power BI
  • Complete Project on Power BI

Module 3: Exploratory Data Analysis For Business

his module emphasizes the critical role of database management in today’s data-driven environment, enabling students to design, create, and manage databases for secure and efficient data storage and retrieval. It provides an in-depth exploration of data mining techniques used to uncover meaningful patterns in large and complex datasets. Students learn scalable pattern discovery methods, pattern evaluation measures, and techniques for mining frequent patterns, sequential patterns, and sub-graph patterns. The module also covers constraint-based pattern mining and pattern-based classification, along with their practical applications. In addition, the course addresses data cleaning and preprocessing, essential steps in the data mining process, focusing on improving data quality through error correction, handling missing values, removing duplicates, and transforming raw data into analysis-ready formats.

Learning Outcomes

By the end of this module, students will be able to:

  • Understand the importance of exploratory data analysis (EDA) and the role of data cleaning and preprocessing.

  • Apply a range of data preprocessing techniques to effectively manage data quality issues.

  • Demonstrate proficiency in different data cleaning and preprocessing methods.

  • Gain practical experience in managing and preparing both numerical and textual data for analysis.

Content Covered:

  • Hands on with NumPy library
  • Hands on with Pandas Library
  • Hands on with Matplotlib Library
  • Introduction to scikit-learn Library
  • Data Collection
  • Numerical Data cleaning and Preprocessing
  • Hands on learning in dataset
  • Text Data Cleaning and preprocessing
  • Hands on learning in dataset
  • Pattern Discovery
  • Clustering and Classification

Module 4: Machine Learning For Business Applications

This module offers an in-depth introduction to established artificial intelligence (AI) and machine learning (ML) techniques that enable systems to learn and improve without explicit programming. It explores key areas of AI, with a focus on machine learning and its real-world applications. Students develop a strong foundational understanding of core AI concepts and terminology, while also examining key challenges and ethical considerations associated with AI. The module further provides guidance from industry experts on learning pathways and career opportunities in AI. Building on data preprocessing concepts, the course progresses to supervised and unsupervised machine learning algorithms, including linear regression, k-nearest neighbors (k-NN), decision trees, and random forests, along with the mathematical principles that underpin these methods.

Learning Outcomes

By the end of this module, students will be able to:

  • Demonstrate an understanding of fundamental concepts in artificial intelligence and machine learning.

  • Identify the nature of datasets and apply appropriate preprocessing techniques for machine learning tasks.

  • Explain the principles of supervised and unsupervised machine learning algorithms.

  • Critically analyze the algorithms and mathematical foundations behind established machine learning approaches.

Content Covered:

  • Introduction to Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Regression
  • Linear
  • Univariate
  • Multivariate
  • Selection of an Algorithm
  • Random Forest
  • Decision Tree
  • Logistic Regression
  • Training and Testing models
  • Checking F1-score, precision, and Accuracy for models
  • Industry Based project

Module 5: Operations Management With AI

This course examines the application of artificial intelligence in operations management, highlighting how AI can improve efficiency, productivity, and decision-making across operational functions. It explores contemporary AI applications in operations and supply chain management, with particular emphasis on innovations within healthcare, manufacturing, and retail sectors. The course also addresses key challenges and opportunities associated with adopting AI in these industries, while introducing emerging research topics with high potential value and practical relevance.

Learning Outcomes

By the end of this course, students will be able to:

  • Understand core principles of operations management and the role of AI in process optimization.

  • Apply AI techniques to demand forecasting, inventory management, and supply chain improvement.

  • Explore AI-enabled approaches to quality control, maintenance, and resource allocation.

  • Evaluate real-world case studies and implement AI strategies to achieve operational excellence.

Content Covered:

  • Good & Service industry
  • Types of Operations Management
  • Three Basic Functions of an Organization
  • Operational Interfaces with other departments
  • Key Decisions of Operations Managers
  • Strategic objective
  • Strategy Formulation, the process
  • Defining Corporate Strategy
  • Business/Operational Strategy
  • Flow of Operational Strategy
  • Global vs. Regional Strategies
  • Identifying and implementing operations strategy in domestic and global context

Module 6: International HR Management With AI

International HR Management with AI is an innovative course that integrates core human resource management principles with the latest advancements in artificial intelligence (AI). The module provides students with a comprehensive understanding of the challenges of managing human resources in a global context, while examining how AI technologies are transforming traditional HR practices. Through a balance of theory and practical application, students explore topics such as cross-cultural management, international labour regulations, global talent acquisition, workforce diversity, and employee engagement. The course also examines the use of AI in HR functions, including AI-enabled recruitment systems, predictive analytics for talent management, and the ethical and regulatory implications of AI adoption in the workplace. By the end of the module, students will be equipped with the knowledge and skills to effectively manage international workforces and contribute strategically to AI-driven HR transformation.

Learning Outcomes

By the end of this course, students will be able to:

  • Understand the role of AI in international HR management and its impact on global talent acquisition and retention.

  • Examine AI-enabled approaches to diversity, equity, and inclusion in global HR contexts.

  • Analyze ethical, legal, and regulatory considerations related to the use of AI in international HR.

  • Develop AI-enhanced HR policies and strategies for managing a diverse, global workforce.

Content Covered

  • Fundamentals of Human Resource Development (HRD)

  • HRD principles, theories, and models

  • Organizational change and organizational learning concepts

  • Strategic alignment of HR development activities

  • Design, implementation, and evaluation of HRD initiatives

  • Theoretical and empirical foundations of HRD and organizational change

  • Case studies of global HRD initiatives in multinational corporations (MNCs)

Module 7: Industry Based Capstone Project

The Industry-Based Capstone Project for the Master of Science in Artificial Intelligence in Business is a collaborative initiative that brings students together with industry mentors to address real-world business challenges using AI solutions. Through this partnership, students gain practical industry insights and a deeper understanding of how AI can be applied to solve complex organizational problems. The project guides students through the full AI solution lifecycle, including problem identification, data collection, model development, implementation, and performance evaluation. It also introduces established methodologies and best practices for managing project workflows, prioritizing business challenges, and delivering impactful outcomes. This capstone strengthens critical skills such as project management, time management, analytical thinking, problem-solving, and professional communication. Upon completion, students gain hands-on experience, practical data analysis skills, and valuable industry exposure, enhancing their employability and competitive advantage.

Learning Outcomes

By the end of this project, students will be able to:

  • Apply AI methodologies and techniques to solve complex business problems.

  • Design, develop, and deploy end-to-end AI solutions.

  • Collaborate effectively with business stakeholders and industry mentors.

  • Evaluate and analyze the performance and business impact of AI solutions.

Content Covered

  • End-to-end development and implementation of AI systems for selected industries

  • Professional communication and collaboration with business stakeholders

  • Translating business needs into AI-driven solutions

  • Performance evaluation and impact assessment of AI solutions

  • Measuring success and identifying opportunities for continuous improvement

Sample Capstone Project Topics

  • Optimizing Marketing Spend Using AI

  • Customer Lifetime Value Prediction with Machine Learning

  • AI-Driven Dynamic Pricing Strategies for E-Commerce

  • Supply Chain Forecasting and Optimization Using AI

  • Social Media Sentiment Analysis for Brand Management

  • Computer Vision for Automated Product Recognition in Retail

  • Predictive Maintenance for Manufacturing Equipment

  • AI-Based Algorithmic Trading and Portfolio Management

  • Predictive Analytics for Employee Retention

  • Healthcare Predictive Analytics for Patient Readmission Risk

  • Ethical AI Auditing Tools for Bias Detection and Mitigation

Overview
Tuition Fees
Entry Requirements
Financial Support

Master Degree

The Master of Science in Artificial Intelligence in Business equips professionals with the skills to apply AI technologies to solve real world business challenges. Combining core business knowledge with data analytics, machine learning, and ethical AI practices, the program emphasizes practical, industry focused learning through an applied capstone project. Graduates are prepared to drive innovation and lead AI enabled decision-making across diverse business environments.

Tuition Fees

The total tuition fees for the BBA (Honours) in Tourism & Hospitality Management:

Year 1: 1200 £
Year 2: 1200 £
Year 3: 1600 £

Tuition fees: 4000 £

Application fee: 100 £ (non-refundable)

* This price includes all online program related costs. Additional costs may apply in some circumstances.

Entry Requirements

Bachelor’s Degree from a recognised University

Proficiency in the English language (IELTS/TOEFL is not mandatory)

Learners without a Bachelor’s degree will be considered to enter through a ‘Mature Entry Route’ subject to having 4/5 years of professional work experience.

Financial Support

We understand that funding your education can be challenging, which is why we provide convenient instalment plans to support you. These flexible payment options help you manage your tuition fees comfortably while staying focused on your learning journey.

By offering flexible payment schedules, we ensure that learners can access high‑quality education without compromising their financial stability. For further information on setting up a flexible payment plan, please connect with our admissions team. They look forward to supporting you as you begin your chosen programme.

 

UCAM University, Spain

Founded in 1996, Universidad Católica de Murcia (UCAM) is a fully accredited European university committed to developing socially responsible graduates through education, research, and innovation. With over 16,000 students and 1,000 faculty members, UCAM offers a wide range of official European programs, including bachelor’s, master’s, and doctoral degrees. The university provides a holistic education that blends strong theoretical foundations, practical learning, and value-based development. UCAM is accredited by ANECA and has received high global recognition, including a student satisfaction rating above 4.4/5 and significant advancement in international research rankings.

ApplyRequest Programme Details Today

With a Master of Artificial Intelligence in Business, you can pursue a wide range of career paths that combine AI expertise with business strategy and decision-making.

  • AI Specialist / AI Engineer
  • Machine Learning Engineer
  • Data Scientist (AI-focused)
  • Business Intelligence Developer / Analyst
  • AI Business Analyst
  • AI Consultant / AI Strategy Consultant
  • AI Project Manager
  • Technology Advisor
  • AI Ethics & Governance Specialist
  • AI Researcher or Specialist

After completing a Master of Artificial Intelligence in Business, the next qualification typically depends on whether you want to go academic, executive, or highly specialized. Here are the most common and logical pathways:

Teaching & Learning Approach

Interactive online learning

Applied projects and case-based assignments

Research-driven assessments

Practical business and industry scenarios

Continuous academic guidance and support

Assessment Method

Internally assessed modules

Criteria-based evaluation aligned with learning outcomes

Focus on assignments, projects, and applied research

No rote memorisation, emphasis on real-world skills

Who Should Enroll?

This programme is ideal for:


Want to bridge the gap between technical AI and business strategy

Are interested in data-driven decision-making and innovation

Aim for leadership roles in AI-enabled organizations

Seek careers in consulting, analytics, product management, or digital transformation

Consultants and strategy professionals who advise organizations on technology, innovation, and transformation.

Industries That Hire

Graduates can work in many sectors, including:

  • Technology & Software

  • Finance & Banking

  • Healthcare & Biotech

  • Retail & E-commerce

  • Manufacturing & Supply Chain

  • Consulting & Professional Services

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