Foundations of Analytics and Artificial Intelligence

Introduction to Artificial Intelligence: meaning, evolution, scope, and types of AI; AI vs traditional computing; overview of machine learning, deep learning, and data science; role of AI in business decision-making; introduction to business analytics (descriptive, diagnostic, predictive, prescriptive); data-driven decision-making; data lifecycle in organisations; ethical, legal, and managerial challenges of AI adoption.

Types of business data: structured and unstructured; introduction to data analytics tools for managers; types of charts for data visualisation; introduction to dashboards, and KPIs; Excel-based analytics fundamentals; correlation, trend analysis, and forecasting basics; interpretation of data for managerial decision-making.

Introduction to Generative AI and Large Language Models (LLMs); applications of AI tools in business; principles of prompt engineering; structure of effective prompts (role, context, instruction, format); iterative prompting techniques; use cases in marketing, HR, finance, and reporting; limitations including bias, hallucination, and data privacy risks.

AI in marketing (customer segmentation, recommendation systems, sentiment analysis); AI in finance (fraud detection, credit scoring, risk assessment); AI in HR (recruitment analytics, attrition prediction, engagement analysis); AI in operations and supply chain (demand forecasting, inventory optimisation, logistics automation); AI in customer relationship management (chatbots, personalisation); overview of Industry 4.0 and emerging trends in intelligent enterprises.

AI in strategic management and digital transformation; competitive advantage through AI adoption; AI governance frameworks; ethical issues in AI (bias, fairness, transparency); data privacy and compliance; impact of AI on future of work; organisational readiness for AI transformation; responsible AI practices; emerging trends such as AI agents and autonomous decision systems. Practical exercises Use spreadsheet software to create charts and dashboards from given datasets and interpret the findings for managerial decision-making. Develop prompts for marketing campaign creation, HR job description drafting, and financial summary generation, and compare outputs across different prompt styles. Identify AI applications in a selected company across marketing, finance, HR, and operations, and prepare a brief analytical report. Analyse a given business problem and propose suitable AI-based solutions (e.g., customer churn prediction, demand forecasting, recruitment challenges).