Master of Artificial Intelligence in Business

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Program Overview

Course Overview

The Master of Artificial Intelligence in business program at UCAM is designed to empower future business leaders with the knowledge and skills to leverage the immense potential of AI technologies in the corporate world. The program is offered in a flexible online format, allowing students to continue to learn while still working. Master of Artificial Intelligence in Business (MAIB) is a comprehensive and interdisciplinary program designed to equip students with the skills they need to apply Artificial Intelligence (AI) techniques for solving complex business problems.

The program encompasses a broad curriculum, spanning data analytics, machine learning, business strategy, and HR management with AI. It combines foundational courses in AI algorithms and programming with advanced topics like deep learning and pattern recognition. Industry-specific courses on applications of AI in healthcare, transportation, and finance provide practical insights to the learner. The program ends with a practical capstone project, teaming up with industries to solve real business problems. Graduates of this course will be well-prepared for leadership in diverse fields like consulting, analytics, and research in application of AI in business.

Program Duration
12 Months
Learning Format
Blended Learning
+971 6 5310 843
(09:00am - 17:30pm)

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    Key Features

    Training Key Features

    • 200 hours of live instructor-led training
    • 3 industry based projects, 6 assignments, 6 Case Studies
    • 24/7 support and LMS Access
    • Hands on experience with latest tools and applied projects
    • Live engagement classes by seasoned academics and professionals
    • Internship/Projects
    • Flexible timing for working professionals
    • EMI option

    Tools/ Frameworks/ Libraries

    Anaconda, Jupyter Notebook, Google Colab, GitHub, Pycharm, Visual Code Studio, Numpy, Pandas, Scikit Learn, Seaborn, Spyder, Advanced Excel, Power BI, SQL, Tableau, Flourish

    Students seeking admission to the course may have to fulfill the following criteria/requirements. 

    • A Bachelor’s degree
    • Proficiency in the English language.
    • Fundamental computer literacy to navigate the digital learning environment.
    • The course is designed to accommodate beginners, providing grounding of AI in Business concepts before advancing to more complex topics.

    Skills Covered

    • Foundational Knowledge
    • Programming
    • Data Analytics
    • Data Science
    • Data Visualization
    • Machine Learning
    • Decision Making Using AI
    • Research Skills
    • Communication Skills
    • Capstone Projects

    Partners of this Programme

    About UCAM

    Universidad Católica de Murcia (UCAM), founded in 1996, is a fully-accredited European University based out of Murcia, Spain. With learning centres in the Middle East and Southeast Asia, UCAM aims to provide students with the knowledge and skills to serve society and contribute to the further expansion of human knowledge through research and development. The university offers various courses, including 30 official bachelor’s degrees, 30 master’s degrees and ten technical higher education qualifications through its Higher Vocational Training Institute, in addition to its in-house qualifications and language courses. The programmes offered are distinguished in Europe and worldwide, with good graduate employability prospects as well. UCAM is accredited by ANECA (National Agency for Quality Assessment and Accreditation of Spain) and the Ministry of Education regarding 17 of its undergraduate degrees.

    Why this Course ?


    Choosing a course of study where you are strongly inclined to pursue a European qualifying degree or for a skill set is a good start in pursuing your educational goals. At ECX, you would be empowered to lead the world.

    Place of Study

    To pursue your dream education, the key factor is that the students need ease in accessing the centre. At ECX, we come to your nearest city to overcome any challenges faced in commuting or travelling abroad without compromising on the quality of education.

    Affordable Fee

    Quality education abroad is highly expensive. At ECX, you benefit from enrolling on an affordable course with flexible payment options.

    Academic Support

    You get enrolled on a European degree, with blended teaching methodology and 360-degree academic assistance through our faculties with international standards for attaining a business management degree.

    Career Opportunities

    You become professionals in your respective field of study on completion of the degree as it brings in more of a realistic pursuit, thus transforming you with the better skill sets to approach the career market further.

    Course Resources

    For more detailed information about the course, please click on the links below.

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    Program Details

    This Section Provides Details Of The Structure, Content, And Learning Outcomes Of Core Modules In This Qualification.

    Learning Path

    Module 1
    Working With Data

    This module introduces students to Python, one of the key programming languages in data science. The journey begins with foundational Python knowledge, installation and setup of the programming environment, and a look into Python's simple syntax. Students learn about Python's variables, data types, and operators, essential for manipulating and storing data. The second part of this module explores control flow tools, and functions in python. Students get hands-on experience with lists, tuples, dictionaries, conditional statements, loops, and functions. Complementing the Python-specific curriculum, the module also intertwines mathematical concepts like number theory, vectors, and matrices, enabling students to use advanced Python libraries like NumPy, effectively for complex calculations and data analytics.

    • Students will be able to recall and explain the basic syntax, variables, data types, and operators in Python.
    • Demonstrate comprehension of Python's primary data structures (lists, tuples, dictionaries) and control flow tools (conditional statements, loops) by explaining their usage and significance in data manipulation and storage.
    • Students will comprehend the functionality and usage of Python libraries such as NumPy, pandas, and matplotlib for data treatment.
    • Utilize Python for problem-solving, integrating the mathematical concepts of number theory, vectors, and matrices to perform complex computations and analyses.
    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 addresses the principles of creating reliable spreadsheet models, translating conceptual models into mathematical models, and applying them in spreadsheets. It also demonstrates a knowledge of three analytic tools in Excel, Excel functions, and the process of auditing spreadsheet models to assure accuracy. Additionally covered in this module are Decision analysis, Payoff Tables, and Decision Trees. Microsoft Power BI helps users derive practical knowledge from data to solve business concerns, bringing analytical models to corporate decision-making. Learners acquire insight into advanced analytic features of Power BI, such as prediction, data visualizations, and data analysis expressions. Students will learn to create interactive dashboards and reports for data-driven decision-making.

    • Students will comprehend the importance of data visualization and the role of Advanced Excel in this process.
    • Apply the knowledge to select the appropriate visual representation for various datasets.
    • Create interactive dashboards and visual reports using PowerBI/ Advanced Excel.
    • Analyze and interpret data visualizations to derive meaningful insights and aid 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

    This module highlights the importance of database management in today's data-driven world and learn to design, create, and manage databases to store and retrieve data efficiently and securely. This module takes a deep dive into Data mining to discover patterns in large data sets. The course provides an opportunity to learn concepts, principles, and skills to practice and engage in scalable pattern discovery methods on massive data; discuss pattern evaluation measures; study methods for mining diverse kinds of frequent patterns, sequential patterns, and sub-graph patterns; and study constraint-based pattern mining, pattern-based classification, and explore their applications. Following the preprocessing, the course advances into Data cleaning and preprocessing are essential steps in the data mining process. Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in the data to improve its quality. It includes tasks such as removing duplicates, filling in missing values, and correcting invalid data. Preprocessing, on the other hand, involves transforming raw data into a format that is suitable for analysis.

    • Students will understand the importance of EDA and the process of data cleaning and preprocessing.
    • Apply various data preprocessing techniques and manage data quality issues effectively.
    • Develop Competency in Various Types of Data Cleaning and Preprocessing.
    • Acquire Practical Experience in Managing Numerical and Text Data.
    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 provides an in-depth understanding of established methods of artificial intelligence and machine learning techniques that enable computers to learn without being explicitly programmed. The module discusses various parts of artificial intelligence, which include ML (Machine Learning) and aims to explain real-world application. The module provides foundational understanding of AI concepts and terms, be able to describe several issues and ethical concerns surrounding AI, and articulate advice from experts about learning and starting a career in AI. Following the preprocessing, the course advances into algorithms such as linear regression, k-NN, decision trees, random forest, etc. for machine learning by supervised and unsupervised learning.

    • Understand Artificial Intelligence and Machine Learning fundamentals.
    • Understand the nature of the dataset and the methods for pre-processing it for machine learning.
    • Introduction to Supervised and Unsupervised Machine Learning Algorithms
    • Demonstrate a deep critical understanding of algorithms and mathematics behind established ML 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 is designed to explore the integration of artificial intelligence in operations management. It focuses on how AI can enhance efficiency, productivity, and decision-making in various operational processes. The course will cover recent applications of AI in operations management and supply chain management, with a focus on innovations in healthcare, manufacturing, and retail operations. The course will also discuss primary challenges and opportunities for utilizing AI in those industries. Additionally, the course will cover trending research topics with significant value potential in these areas.

    • Understand the key principles of operations management and how AI can be applied to optimize processes.
    • Learn to use AI to forecast demand, manage inventory, and improve supply chain operations.
    • Explore AI-driven quality control, maintenance, and resource allocation in operations.
    • Analyze case studies and implement AI strategies for 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 a transformative course that blends the realms of human resource management with the cutting-edge advancements of artificial intelligence (AI). This module is designed to provide students with a comprehensive understanding of the complexities associated with managing human resources on a global scale, while also exploring the ways in which AI can revolutionize traditional HR practices. Through a combination of theoretical concepts and practical applications, students will delve into topics such as cross-cultural management, international labour laws, talent acquisition, workforce diversity, and employee engagement. Furthermore, this course will explore the role of AI in HR functions, including the implementation of AI-driven recruitment tools, predictive analytics for talent management, and the ethical implications of AI adoption in the workplace. By the end of this module, students will be equipped with the knowledge and skills required to navigate the ever-evolving landscape of international HR management with AI and be poised to make impactful contributions in the field. LEARNING OUTCOMES Understand the role of AI in international HR management and its impact on global talent acquisition and retention. Explore AI-driven diversity and inclusion strategies in global HR. Analyse ethical considerations and regulatory aspects related to AI in international HR. Develop AI-enhanced HR policies and strategies for managing a diverse, international workforce. Content Covered: Fundamentals of Human Resource Development Principles, theories, and models of HRD Organizational change Organizational learning and loop concepts Strategic overview of developmental activities Establishing a strategic fit Selection, implementation, and evaluation of HRD initiatives Underpinning theories and empirical evidence in HRD and organizational change Case scenarios in Global HRD initiatives in MNC’s
    Module 7
    Industry Based Capstone Project

    The Industry-based Capstone Project in Master of Science in Artificial Intelligence in Business involves a close collaboration between students and industry mentors. This collaboration brings a real-world perspective and insights into the business challenges that the AI solution aims to solve. Students will learn how to identify business challenges, collect data, apply AI algorithms, and evaluate the performance of the designed solution. The course equips students with established best practices and methodological frameworks to manage project development processes and prioritize the challenges to tackle. It forms an explicit foundation for developing critical project management, time-management, problem-solving, and presentation skills. Upon completion of the Industry-Based Capstone Project, students will have acquired essential skills such as project management, communication, practical problem-solving, and data analysis. Through this course, students will attain significant industry network exposure and thus gain a competitive edge for desired professional experiences.

    • Apply AI methodologies and techniques to address complex business problems.
    • Develop and implement end-to-end AI solutions.
    • Collaborate effectively with business stakeholders.
    • Analyze the effectiveness of AI solutions.
    Content Covered:
    • Developing and implementing entire AI systems for their chosen industry
    • Communicate and collaborate in a professional manner with business stakeholders.
    • Understand business needs and requirements.
    • Propose solutions.
    • Evaluate the performance of the AI solutions that they develop and assess their impact on the business.
    • Measure the success of their solutions and identify areas for improvement in future.
    Capstone Projects:
    • Optimizing Marketing Spend Using AI
    • Customer Lifetime Value Prediction with Machine Learning
    • Dynamic Pricing Strategy for E-Commerce: An AI-driven Approach
    • Supply Chain Forecasting and Optimization using Artificial Intelligence
    • Sentiment Analysis in Social Media for Brand Management
    • Computer Vision for Retail: Automated Product Recognition
    • Predictive Maintenance for Manufacturing Equipment
    • Algorithmic Trading Strategies: AI-driven Portfolio Management
    • Predictive Analytics for Employee Retention
    • Healthcare Predictive Analytics: Patient Readmission Risk
    • Ethical AI Auditing Tool: Bias Detection and Mitigation

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