Master in Data Science

This module focuses on applying data analytics tools to support business decision-making. Students learn to design reliable spreadsheet models, translate conceptual frameworks into mathematical representations, and audit spreadsheet models for accuracy. The module covers advanced Excel features, including formulas, functions, pivot tables, and decision analysis tools such as payoff tables and decision trees.
In addition, students gain hands-on experience with Microsoft Power BI, learning how to model data, create advanced visualizations, and build interactive dashboards and reports to support data-driven business insights.

Learning Outcomes

Students will be able to:

• Understand the importance of data visualization in business analytics.
• Select appropriate visual representations for different data types.
• Develop interactive dashboards using Advanced Excel and Power BI.
• Interpret analytical outputs to support managerial decision-making.

Content Covered

• Excel fundamentals and workbook management
• Cell formatting and worksheet operations
• Formulas, functions, and pivot tables
• Decision analysis techniques
• Introduction to Microsoft data analytics
• Data preparation and modeling in Power BI
• Visualization and reporting in Power BI
• Applied Power BI project

This module emphasizes the role of exploratory data analysis (EDA) and data mining in extracting value from large datasets. Students learn database fundamentals and scalable pattern discovery techniques, including frequent pattern mining, sequential patterns, and classification methods.
A strong focus is placed on data cleaning and preprocessing, where students learn to handle missing values, duplicates, inconsistencies, and unstructured data. Practical exercises enable learners to manage both numerical and textual datasets and prepare them for downstream analytics and machine learning tasks.

Learning Outcomes

Students will be able to:

• Explain the importance of EDA in data-driven decision-making.
• Apply data cleaning and preprocessing techniques effectively.
• Demonstrate competence in managing structured and unstructured data.
• Perform clustering and classification for pattern discovery.

Content Covered

• Hands-on practice with NumPy, pandas, and matplotlib
• Introduction to scikit-learn
• Data collection techniques
• Numerical data cleaning and preprocessing
• Text data cleaning and preprocessing
• Dataset-based practical exercises
• Pattern discovery
• Clustering and classification

This module provides a comprehensive introduction to artificial intelligence and machine learning concepts, focusing on practical business applications. Students explore supervised and unsupervised learning techniques and gain an understanding of ethical considerations and real-world implementation challenges.
The module covers key machine learning algorithms such as linear regression, k-nearest neighbors, decision trees, random forests, and logistic regression, along with model training, testing, and performance evaluation.

Learning Outcomes

Students will be able to:

• Demonstrate understanding of AI and machine learning fundamentals.
• Analyze datasets and apply preprocessing techniques for ML tasks.
• Explain supervised and unsupervised learning algorithms.
• Critically evaluate machine learning models and their mathematical foundations.

Content Covered

• Introduction to machine learning
• Supervised and unsupervised learning
• Regression (linear, univariate, multivariate)
• Algorithm selection strategies
• Decision trees and random forests
• Logistic regression
• Model training and testing
• Model evaluation (accuracy, precision, recall, F1-score)
• Industry-based project

This module examines the application of artificial intelligence in operations and supply chain management. Students explore how AI enhances forecasting, inventory management, quality control, maintenance, and resource optimization. The module highlights industry use cases across healthcare, manufacturing, and retail, while addressing strategic and operational challenges in AI adoption.

Learning Outcomes

Students will be able to:

• Understand core operations management principles and AI applications.
• Apply AI techniques to improve supply chain and operational efficiency.
• Analyze real-world case studies to support operational excellence.
• Develop AI-driven operational strategies.

Content Covered

• Goods and services operations
• Operations management functions
• Strategic objectives and decision-making
• Corporate and operational strategy formulation
• Domestic and global operations strategies
• AI-enabled operations strategy implementation

This module integrates human resource management with artificial intelligence in a global context. Students explore cross-cultural management, international labor regulations, workforce diversity, and employee engagement, alongside AI-driven HR tools such as predictive analytics and intelligent recruitment systems. Ethical and regulatory considerations of AI in HR are also examined.

Learning Outcomes

Students will be able to:

• Analyze the impact of AI on global talent management.
• Develop AI-enabled HR strategies for diverse workforces.
• Evaluate ethical and regulatory issues in AI-driven HR practices.
• Design HR policies aligned with international organizational goals.

Content Covered

• Human Resource Development fundamentals
• Organizational learning and change
• Strategic HRD planning
• AI applications in recruitment and talent management
• Global HR case studies
• HRD initiatives in multinational organizations

The Industry-Based Capstone Project provides students with hands-on experience through close collaboration with industry mentors. Students identify real business challenges, collect and analyze data, design AI-driven solutions, and evaluate their effectiveness. The project emphasizes professional skills such as project management, stakeholder communication, and solution presentation, while offering valuable industry exposure.

Learning Outcomes

Students will be able to:

• Apply AI methodologies to solve complex business problems.
• Design and implement end-to-end AI solutions.
• Collaborate effectively with industry stakeholders.
• Evaluate and improve AI solution performance.

Capstone Project Examples

• AI-driven marketing spend optimization
• Customer lifetime value prediction
• Dynamic pricing for e-commerce
• Supply chain forecasting and optimization
• Social media sentiment analysis
• Computer vision for retail automation
• Predictive maintenance in manufacturing
• Algorithmic trading and portfolio management
• Employee retention analytics
• Healthcare predictive analytics
• Ethical AI auditing and bias detection