Diploma in Business Management

This foundational module introduces students to the principles of management and the functions of a business manager. Students learn about planning, organizing, leading, and controlling (the POLC framework) and how these functions ensure organizational success. Modern management theories and leadership styles are explored through case studies. Crucially, the module examines how AI and data-driven tools are enhancing decision-making and operational efficiency in management. By understanding both classic principles and contemporary tech-driven practices, students gain a holistic view of what effective management entails in today's business environment.

• Explain fundamental management functions (planning, organizing, leading, controlling) and how they interrelate within an organization.
• Apply management theories and leadership styles to solve organizational problems and improve team performance.
• Demonstrate decision-making and problem-solving skills, incorporating ethical considerations in business scenarios.
• Evaluate the impact of AI and technology on modern management practices, describing how data analytics and automation support managers in planning and control.

This module explores the dynamic world of electronic commerce and digital business models. Students learn how businesses operate online, from launching digital storefronts to managing online customer experiences. Key facets include digital marketing strategies (SEO, social media marketing), online payment systems, and e-commerce operations such as fulfillment and supply chain logistics. The curriculum emphasizes data analytics and how AI enhances e-commerce – for example, through personalized recommendations, chatbots for customer service, and predictive analysis of consumer behavior. By the end, students will understand how to strategize and manage a business in the digital realm, leveraging technology to reach and engage customers globally.

• Understand e-commerce models and strategies, explaining how B2C, B2B, and other digital business models function and generate revenue.
• Develop basic digital marketing plans, using techniques like search engine optimization (SEO), social media campaigns, and content marketing to drive online traffic and sales.
• Manage core e-commerce operations, including website user experience, online payment processing, inventory management, and customer service in a digital context.
• Analyze e-commerce performance data to make informed decisions, utilizing analytics tools and AI (e.g., for customer segmentation or recommendation engines) to optimize online business performance.

In this module, students build competency in business statistics and data analysis. The focus is on understanding and applying statistical techniques to solve business problems and guide decision-making. Students learn how to collect data, summarize it meaningfully, and draw conclusions about larger trends or differences (inferential statistics). They will practice using statistical tools (such as spreadsheets or specialized software) to perform analyses like hypothesis tests and regression modeling. The integration of AI comes through exposure to how statistical foundations underlie modern analytics and machine learning – for example, how regression is used in predictive algorithms. By mastering statistics, students gain the quantitative reasoning skills essential for data-driven management in an AI-augmented business world.

• Apply descriptive statistics to business data, organizing and presenting information using tables, charts, and summary measures (mean, median, standard deviation, etc.).
• Use probability concepts to model uncertainty in business scenarios and calculate probabilities relevant to risk and forecasting.
• Perform inferential statistical analyses , including constructing confidence intervals and conducting hypothesis tests, to support business decisions (e.g., determining if a new strategy significantly improves sales).
• Build and interpret simple predictive models, such as linear regression for trend forecasting, and understand how these methods form the basis for AI-driven analytics and business intelligence tools.

This module provides a broad overview of economics, teaching students how economic principles govern business environments and influence decision-making at both micro and macro levels. In the microeconomics portion, students study how markets function – supply and demand, pricing, consumer behavior, and different market structures (competition vs. monopoly). In the macroeconomics portion, they examine factors that affect the economy as a whole, such as inflation, employment, economic growth, and government policies. The course ties these concepts to real-world business implications, including how companies adapt to economic changes. Additionally, it explores how technology and AI are impacting economic trends, for instance, through automation’s effect on labor markets or big data’s role in economic forecasting. By understanding economics, students can better strategize in business, anticipating market shifts and policy impacts.

• Explain core microeconomic concepts (such as supply and demand, elasticity, and market equilibrium) and how they determine prices and outputs in various markets.
• Analyze different market structures (perfect competition, monopolistic competition, oligopoly, monopoly) and understand the business strategies and outcomes associated with each.
• Understand key macroeconomic indicators (GDP, economic growth rates, inflation, unemployment, interest rates) and discuss how changes in these indicators can impact business operations and strategic planning.
• Discuss the influence of technology and AI on the economy, including topics like automation in industry, the digital economy, and how businesses and labor markets adapt to technological change.

This module covers the fundamentals of financial accounting and the interpretation of financial statements, crucial for any business manager. Students learn the accounting cycle – recording transactions, adjusting entries, and preparing key financial statements (Income Statement, Balance Sheet, Cash Flow Statement). With this foundation, they move into financial analysis, using tools like ratio analysis to evaluate a company’s performance and financial health. The module emphasizes practical skills like reading annual reports and making business decisions based on financial data. It also introduces the growing role of technology: how accounting software and AI-powered analytics are automating bookkeeping, detecting anomalies (fraud detection), and enabling real-time financial reporting. By mastering accounting principles and analytical techniques, students will be able to derive meaningful insights from financial data and support strategic business decisions.

• Understand and apply basic accounting principles, including the double-entry system and the accounting equation (Assets = Liabilities + Equity).
• Prepare fundamental financial statements (profit & loss statements, balance sheets, and cash flow statements) for a simple business scenario and explain the purpose of each statement.
• Perform financial statement analysis using ratios (such as liquidity, solvency, and profitability ratios) and interpret the results to assess business performance and inform decision-making.
• Recognize the role of technology in accounting, by exploring how software tools and AI applications improve efficiency and accuracy in financial reporting, forecasting, and compliance.

This module inspires and prepares students to think like an entrepreneur and innovate within a business context. It covers the journey of starting and growing a new venture – from generating a viable business idea to developing a business plan and securing resources. Students learn creative problem-solving techniques and how to apply them to design innovative products, services, or processes. They also study models of innovation within established companies (intrapreneurship) and how to foster a culture of innovation. A modern twist in this module is examining the role of technology and AI in entrepreneurship – how entrepreneurs leverage AI tools for market analysis, product development (e.g., AI in app development or service delivery), and how entire new business models are built around emerging tech. Through project work, learners may brainstorm startup ideas or innovative solutions to real problems, applying entrepreneurial principles learned in class.

• Identify and evaluate business opportunities by conducting market research and recognizing gaps or customer needs that could be addressed with a new product or service.
• Develop a basic business plan, outlining the value proposition, target market, marketing and operations strategy, and financial projections for a startup idea.
• Demonstrate creative thinking and innovation skills, using tools like brainstorming, design thinking, or lean startup methodology to refine ideas and solve problems in novel ways.
• Understand how emerging technologies (including AI) drive innovation and competitive advantage, and propose ways to incorporate such technologies into new ventures or existing business processes.
• Show entrepreneurial mindset traits, such as risk-taking, resilience, and adaptability, through class activities and a mini venture project.

This forward-looking module delves into Talent Management and Human Resources (HR) in the modern era, emphasizing how AI and digital tools are transforming the way organizations manage their most valuable asset – people. Students will learn the fundamentals of talent management, including workforce planning, recruitment and selection, training and development, performance management, and employee retention strategies. For each of these areas, the module highlights AI applications and data-driven approaches: for instance, using AI-powered recruitment software to screen candidates, employing people analytics to gauge employee engagement, or personalized learning platforms for staff development. The curriculum also addresses critical considerations such as ethical use of AI in HR (avoiding bias and ensuring privacy). By understanding both traditional HR practices and cutting-edge AI tools (sometimes called HR Tech or People Analytics), students will be prepared to contribute to building effective, tech-enabled talent infrastructure in any organization.

• Explain key components of talent management – from hiring and onboarding to training, performance evaluation, and retention – and why they are vital to organizational success.
• Utilize HR data and analytics to inform talent decisions, such as interpreting turnover metrics or employee satisfaction data to improve HR strategies.
• Evaluate AI tools in HR processes, understanding how technologies like Applicant Tracking Systems (ATS), AI interview bots, or performance analytics platforms can improve efficiency and outcomes in recruitment, development, and workforce planning.
• Design basic talent management initiatives (e.g., a recruitment plan or training program), integrating AI solutions where appropriate to enhance effectiveness.
• Discuss ethical and legal considerations related to AI in HR, including issues of algorithmic bias, data privacy, and maintaining a human touch in talent management.

This specialized module examines how Artificial Intelligence is applied in the world of business finance and financial services. Building on basic finance concepts, students will see how AI and machine learning enhance financial decision-making and operations. Topics include AI in financial analysis (like using algorithms to forecast sales or optimize budgets), in investments (such as algorithmic trading and robo-advisors), in risk management (fraud detection systems, credit scoring models), and in customer-facing financial services (chatbots in banking, personalized financial products). Students also gain an understanding of the broader FinTech landscape, exploring innovations like digital payments, blockchain, and how they disrupt traditional financial models. By blending finance fundamentals with technology insights, this module prepares students to navigate the rapidly evolving financial sector where analytical skills and tech-savvy go hand in hand.

• Understand core principles of business finance, including financial planning, investment decision criteria, and risk management techniques used by companies.
• Identify key applications of AI in finance, explaining how technologies are used for tasks such as financial forecasting, portfolio management, fraud detection, and improving customer experience in banking and finance.
• Analyze financial data using AI-driven tools, interpreting outputs from predictive models or analytics software to support budgeting, forecasting, or investment decisions.
• Discuss FinTech innovations (e.g., mobile payments, blockchain, cryptocurrency) and evaluate their impact on traditional business finance operations and strategies.
• Recognize ethical and regulatory considerations when implementing AI in finance, such as data security, privacy, and compliance with financial regulations.

After completing the taught modules, students will engage in a Final Practicum or Capstone Project that spans approximately 140 hours (the remaining hours of the program). This practicum is a crucial hands-on segment designed to consolidate the knowledge and skills gained throughout the diploma:

What It Involves: Students will undertake a substantial project or internship-like experience. Options may include working on an industry-supplied project, developing a comprehensive business plan for a startup idea, analyzing a real company’s data to solve a business problem, or implementing a small-scale e-commerce venture. Each student (or team) will be guided by a supervisor to ensure the project scope is meaningful and achievable within the timeframe.

Integration of Learning: The project must draw on multiple modules. For example, a student might create a business plan (using Entrepreneurship skills), set up a basic online storefront (E-Commerce skills), apply Marketing and Statistics to drive and analyze sales, use Accounting to manage finances, and even incorporate an AI tool (perhaps a simple chatbot or a predictive model for sales) to demonstrate AI integration.

AI and Innovation Focus: Students are encouraged to incorporate an AI component or digital innovation in their project, reflecting the program’s emphasis on technology. This could be using an existing AI service (like Google Analytics with AI features, or an open-source machine learning tool to analyze data) relevant to their project goals.

Deliverables: By the end of the practicum, students will produce a project report and a presentation. The report documents the project work – including objectives, methodology, results/analysis, and reflections. The presentation (or viva) allows students to showcase their project outcomes, demonstrate any developed prototypes or findings, and highlight how they applied course learning outcomes.

Mentorship and Evaluation: Throughout the practicum, faculty mentors or industry advisors (if it’s an internship/industry project) will provide guidance. The evaluation will consider the quality of the work, the integration of multidisciplinary knowledge, creativity and problem-solving shown, and the ability to utilize AI or analytical tools effectively. This practical evaluation carries significant weight as it demonstrates readiness for real-world employment or further study.

The practicum experience not only reinforces the student’s learning but also provides a portfolio piece or work experience that can be showcased to future employers. It emphasizes self-directed learning, professional development, and the ability to synthesize knowledge across business domains – a key outcome of this diploma program.

1. Developing a mini business plan and launching a mock e-commerce website for a product, then using social media campaigns to drive traffic and analyzing the results (covering E-Commerce, Marketing, Accounting for tracking finances, and AI via analytics tools).
2. Interning with a small business to improve one of their processes (e.g., implementing a simple inventory management system or customer feedback system) and reporting on efficiency gains.
3. Conducting a research project on how a company could adopt AI in their HR department – surveying literature and possibly designing a prototype AI tool for recruitment – then assessing potential impacts on the company.
4. Analyzing a dataset from a company (with permission or using public data) such as sales records or customer demographics to generate business insights using statistical methods and machine learning (e.g., building a basic predictive model for sales).