About Course

Programme Summary

The Diploma in Business Analytics develops the knowledge and practical capabilities required to collect, manage, analyze, interpret and communicate business data for evidence-based decision-making.

The programme integrates business analysis, statistics, data visualization, databases, predictive analytics, business intelligence, machine learning fundamentals and strategic analytics.

International Standards and Framework Alignment

The programme will be informed by internationally recognized frameworks and professional standards, including:

Association of Business Process Management Professionals (ABPMP) — Business Analysis and Process Management.
International Institute of Business Analysis (IIBA) — Business Analysis Body of Knowledge (BABOK).
DAMA International — Data Management Body of Knowledge (DAMA-DMBOK).
ISO 8000 — Data Quality.
ISO/IEC 27001 — Information Security Management.
OECD — Data Governance and Responsible Data Use.
NIST — AI Risk Management and cybersecurity-related frameworks.
IEEE — Standards and guidance relating to AI and data systems.
ACM — Computing and data-related professional principles.
UN SDGs — Data-driven sustainable development.
Leading academic and professional practices in business intelligence, statistics and analytics.

PROGRAMME LEARNING OUTCOMES

By the end of the Diploma, learners should be able to:

Explain the principles and applications of business analytics.
Identify and define business problems that can be addressed through data.
Collect, structure and manage business data.
Apply descriptive and inferential statistical techniques.
Use spreadsheets and databases for business analysis.
Develop meaningful data visualizations and dashboards.
Apply predictive analytics techniques.
Understand fundamental machine-learning concepts.
Interpret analytical results for business decision-making.
Communicate analytical findings to executives and other stakeholders.
Apply data governance, ethics, privacy and security principles.
Develop an evidence-based business analytics solution.

PROGRAMME STRUCTURE

TOPIC 1: FOUNDATIONS OF BUSINESS ANALYTICS

Topic Summary

Topic 1 introduces the principles, concepts and applications of business analytics. It establishes the relationship between business objectives, data and analytical decision-making.

Lessons

Lesson 1: Introduction to Business Analytics
Lesson 2: The Business Analytics Lifecycle
Lesson 3: Types of Data and Data Sources
Lesson 4: Business Problems, Questions and Analytical Thinking
Lesson 5: The Role of the Business Analyst and Analytics Professional

TOPIC 2: DATA MANAGEMENT AND DATA QUALITY

Topic Summary

Topic 2 examines how organizations acquire, organize, manage and maintain reliable business data.

Lessons

Lesson 1: Fundamentals of Data Management
Lesson 2: Data Collection and Data Sources
Lesson 3: Data Quality and Data Cleaning
Lesson 4: Data Governance, Privacy and Security
Lesson 5: Data Management for Business Analytics

TOPIC 3: BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS

Topic Summary

Topic 3 develops the statistical foundation required to analyze business data and distinguish meaningful patterns from random variation.

Lessons

Lesson 1: Fundamentals of Business Statistics
Lesson 2: Descriptive Statistics and Data Distributions
Lesson 3: Probability and Statistical Reasoning
Lesson 4: Sampling, Estimation and Confidence Intervals
Lesson 5: Hypothesis Testing and Business Decision-Making

TOPIC 4: SPREADSHEET ANALYTICS AND BUSINESS DATA ANALYSIS

Topic Summary

Topic 4 develops practical skills for analyzing business data using spreadsheet-based analytical techniques.

Lessons

Lesson 1: Spreadsheet Foundations for Business Analytics
Lesson 2: Data Preparation and Transformation in Spreadsheets
Lesson 3: Business Formulas, Functions and Analytical Models
Lesson 4: Pivot Tables, Advanced Analysis and What-If Analysis
Lesson 5: Spreadsheet-Based Business Decision Models

TOPIC 5: DATABASES AND SQL FOR BUSINESS ANALYTICS

Topic Summary

Topic 5 introduces relational databases and SQL as essential tools for accessing, transforming and analyzing organizational data.

Lessons

Lesson 1: Database Concepts for Business Analysts
Lesson 2: Relational Data Models and Database Design
Lesson 3: SQL Fundamentals for Business Analysis
Lesson 4: Advanced SQL Queries and Data Aggregation
Lesson 5: SQL-Based Business Analytics and Reporting

TOPIC 6: DATA VISUALIZATION AND BUSINESS INTELLIGENCE

Topic Summary

Topic 6 examines how analytical findings can be transformed into effective visualizations, reports and business intelligence dashboards.

Lessons

Lesson 1: Principles of Data Visualization
Lesson 2: Charts, Graphs and Visual Storytelling
Lesson 3: Business Intelligence Concepts and Architecture
Lesson 4: Dashboard Design and Executive Reporting
Lesson 5: Data Storytelling and Analytical Communication

TOPIC 7: PREDICTIVE ANALYTICS AND FORECASTING

Topic Summary

Topic 7 introduces techniques for using historical data to estimate future outcomes and support business planning.

Lessons

Lesson 1: Fundamentals of Predictive Analytics
Lesson 2: Correlation and Regression Analysis
Lesson 3: Time-Series Analysis and Forecasting
Lesson 4: Predictive Models and Model Evaluation
Lesson 5: Applying Predictive Analytics to Business Decisions

TOPIC 8: MACHINE LEARNING FOR BUSINESS

Topic Summary

Topic 8 introduces the fundamental concepts of machine learning and examines their application to business problems.

Lessons

Lesson 1: Introduction to Machine Learning
Lesson 2: Supervised and Unsupervised Learning
Lesson 3: Classification and Regression Models
Lesson 4: Clustering, Segmentation and Pattern Recognition
Lesson 5: Machine Learning Evaluation, Interpretation and Business Application

TOPIC 9: STRATEGIC BUSINESS ANALYTICS AND DECISION-MAKING

Topic Summary

Topic 9 integrates analytical techniques with strategic business decision-making across major organizational functions.

Lessons

Lesson 1: Analytics-Driven Strategic Decision-Making
Lesson 2: Customer and Marketing Analytics
Lesson 3: Financial and Operational Analytics
Lesson 4: Risk, Fraud and Performance Analytics
Lesson 5: Predictive and Prescriptive Analytics for Business Strategy

TOPIC 10: RESPONSIBLE ANALYTICS, ANALYTICS LEADERSHIP AND CAPSTONE PROJECT

Topic Summary

Topic 10 examines responsible use of analytics and develops the learner’s ability to manage an end-to-end business analytics project.

Lessons

Lesson 1: Data Ethics, Bias and Responsible Analytics
Lesson 2: AI Governance, Privacy and Analytical Risk
Lesson 3: Analytics Leadership and Building a Data-Driven Organization
Lesson 4: Designing and Managing a Business Analytics Project
Lesson 5: Business Analytics Capstone Project and Executive Presentation

Course Content

TOPIC 1: FOUNDATIONS OF BUSINESS ANALYTICS
Topic Summary Topic 1 introduces the fundamental principles of business analytics and establishes the foundation for using data to support effective organizational decision-making. The topic examines the relationship between business objectives, data, analytical methods and decisions. Learners will understand the different forms of analytics, the business analytics lifecycle, sources and types of data, analytical problem-solving, and the professional role of business analysts and analytics professionals. The topic covers: Meaning and scope of business analytics. Business analytics and evidence-based decision-making. Descriptive, diagnostic, predictive and prescriptive analytics. The business analytics lifecycle. Types and sources of business data. Business questions and analytical questions. Analytical thinking and problem definition. The role of business analysts and analytics professionals. Analytics in organizational decision-making. Limitations and risks of business analytics. Lessons Lesson 1: Introduction to Business Analytics Lesson 2: The Business Analytics Lifecycle Lesson 3: Types of Data and Data Sources Lesson 4: Business Problems, Questions and Analytical Thinking Lesson 5: The Role of the Business Analyst and Analytics Professional

TOPIC 2: DATA MANAGEMENT AND DATA QUALITY
Topic Summary Topic 2 examines the principles and practices required to ensure that organizational data is available, reliable, secure, well-governed and fit for analytical use. The topic moves beyond simply collecting data and examines how data is managed throughout its lifecycle. Learners will develop an understanding of data collection, data quality, data cleaning, governance, privacy, security and the role of effective data management in business analytics. The topic covers: ● Fundamentals of data management. ● Data management lifecycle. ● Data collection methods. ● Internal and external data acquisition. ● Data quality dimensions. ● Data profiling and validation. ● Data cleaning and transformation. ● Data governance. ● Data ownership and stewardship. ● Data privacy and protection. ● Data security. ● Data management for analytics. Lessons Lesson 2.1: Fundamentals of Data Management Lesson 2.2: Data Collection and Data Sources Lesson 2.3: Data Quality and Data Cleaning Lesson 2.4: Data Governance, Privacy and Security Lesson 2.5: Data Management for Business Analytics

TOPIC 3: DESCRIPTIVE AND DIAGNOSTIC ANALYTICS
Topic Summary Topic 3 introduces the analytical techniques organizations use to understand what has happened, what is happening, and why particular outcomes have occurred. The topic develops the learner's ability to transform business data into meaningful information through descriptive and diagnostic methods. It establishes the analytical foundation required before progressing to predictive and prescriptive analytics. The topic covers: Foundations of business analytics. Descriptive analytics. Diagnostic analytics. Data summarization. Measures of central tendency. Measures of dispersion. Frequency distributions. Data aggregation. Trend and variance analysis. Correlation and relationship analysis. Root-cause analysis. Business performance analysis. Analytical interpretation and communication. Lessons Lesson 3.1: Foundations of Descriptive and Diagnostic Analytics Lesson 3.2: Data Summarization and Descriptive Statistics Lesson 3.3: Diagnostic Analytics and Root-Cause Analysis Lesson 3.4: Trend, Variance and Comparative Analysis Lesson 3.5: Communicating Descriptive and Diagnostic Insights

TOPIC 4: DATA VISUALIZATION AND BUSINESS INTELLIGENCE
Topic Summary Topic 4 develops the learner's ability to transform business data into clear, meaningful and actionable visual information. It introduces principles of data visualization, dashboard design and business intelligence, with emphasis on selecting appropriate visual techniques for different business questions. The topic covers: Principles of effective data visualization. Data visualization techniques. Charts, graphs and analytical displays. Dashboard design. Key performance indicators. Business intelligence concepts. Interactive reporting. Data storytelling. Executive dashboards. Visualization ethics and avoiding misleading representations. Lessons Lesson 4.1: Principles and Techniques of Data Visualization Lesson 4.2: Charts, Graphs and Analytical Visualizations Lesson 4.3: Dashboard Design and Key Performance Indicators Lesson 4.4: Business Intelligence and Interactive Reporting Lesson 4.5: Data Storytelling, Visualization Ethics and Executive Communication

TOPIC 5: DATABASES AND SQL FOR BUSINESS ANALYTICS
Topic Summary Topic 5 introduces relational databases and SQL as essential tools for accessing, transforming and analyzing organizational data. The topic covers: Database concepts and terminology. Relational databases and relational data models. Tables, records, fields and keys. Primary and foreign keys. Database relationships. Database normalization and design. SQL syntax and query structure. Data retrieval and filtering. Sorting and conditional logic. Joins and multi-table analysis. Aggregate functions and grouping. Subqueries and advanced SQL techniques. Data manipulation and transformation. SQL-based business analysis. Business reporting using SQL. Data quality and validation. Practical interpretation of SQL results. Lessons Lesson 5.1: Database Concepts for Business Analysts Lesson 5.2: Relational Data Models and Database Design Lesson 5.3: SQL Fundamentals for Business Analysis Lesson 5.4: Advanced SQL Queries and Data Aggregation Lesson 5.5: SQL-Based Business Analytics and Reporting

TOPIC 6: DATA VISUALIZATION AND BUSINESS INTELLIGENCE
Topic Summary Topic 6 introduces the principles, techniques and tools used to transform analytical data into meaningful visual insights that support business decision-making. The topic covers: Principles of data visualization. Types of charts and graphs. Selecting appropriate visualizations. Data storytelling. Dashboard design. Business intelligence concepts. Interactive dashboards. KPI visualization. Visualization errors and misleading charts. Executive reporting and insight communication.

TOPIC 7: PREDICTIVE ANALYTICS AND FORECASTING
Topic Overview Predictive analytics extends business analytics beyond understanding what has happened to estimating what is likely to happen in the future. It combines statistical methods, historical data, business knowledge and analytical models to identify patterns and generate forecasts that can support managerial decision-making. Predictive analytics is increasingly applied across sales, marketing, finance, operations, risk management, customer analytics and human resources. However, predictive results are estimates rather than certainties. Their usefulness depends on the quality of the underlying data, the appropriateness of the analytical method, the assumptions made and the context in which predictions are applied. This topic develops learners' ability to understand predictive analytics, examine relationships between variables, construct and interpret forecasts, evaluate predictive models and translate predictive outputs into responsible business decisions. Lessons Lesson 7.1: Fundamentals of Predictive Analytics Lesson 7.2: Correlation and Regression Analysis Lesson 7.3: Time-Series Analysis and Forecasting Lesson 7.4: Predictive Models and Model Evaluation Lesson 7.5: Applying Predictive Analytics to Business Decisions

TOPIC 8: MACHINE LEARNING FOR BUSINESS
Topic Introduction and Summary Machine learning has become an increasingly important component of modern business analytics. Organizations generate large volumes of data from customers, transactions, operations, digital platforms, financial systems and other business processes. Machine learning provides techniques that enable organizations to identify patterns within this data, generate predictions, automate analytical tasks and support more informed business decisions. This topic introduces learners to the fundamental concepts, methods and business applications of machine learning. It builds upon the statistical, analytical and predictive concepts developed in earlier topics and progresses toward practical understanding of how machine learning models can be applied to real-world organizational problems. The topic begins by establishing the foundations of machine learning, including its relationship with business analytics, artificial intelligence and traditional statistical analysis. Learners then examine supervised and unsupervised learning, understanding when each approach is appropriate and how different learning problems are formulated. The topic subsequently explores classification and regression models, which are among the most widely used machine learning approaches in business. Learners will examine how these models can support applications such as customer classification, credit-risk assessment, fraud detection, sales prediction and customer retention. The topic then progresses to clustering, segmentation and pattern recognition, enabling learners to understand how organizations can discover previously unknown groups and patterns within their data. Finally, learners examine model evaluation, interpretation and the practical application of machine learning to business decision-making. Topic Learning Focus By completing Topic 8, learners will develop an understanding of: The fundamental concepts and principles of machine learning. The relationship between machine learning, business analytics, statistics and artificial intelligence. The distinction between supervised and unsupervised learning. Classification and regression as major machine learning approaches. Clustering, segmentation and pattern-recognition techniques. The importance of training, testing and evaluating machine learning models. The interpretation of machine learning results in a business context. The opportunities and limitations associated with machine learning in organizations. The application of machine learning to practical business problems. The importance of combining technical model outputs with business judgment and organizational objectives. Topic 8 Lessons Lesson 8.1: Introduction to Machine Learning Lesson 8.2: Supervised and Unsupervised Learning Lesson 8.3: Classification and Regression Models Lesson 8.4: Clustering, Segmentation and Pattern Recognition Lesson 8.5: Machine Learning Evaluation, Interpretation and Business Application Business Context Machine learning can support organizations across virtually every major business function. Examples include: Marketing: customer segmentation, recommendation systems and campaign targeting. Finance: credit-risk assessment, revenue forecasting and financial anomaly detection. Banking: fraud detection and customer risk analysis. Operations: demand forecasting, inventory optimization and predictive maintenance. Human Resources: workforce analytics and employee-retention analysis. Retail: product recommendations, customer behavior analysis and demand prediction. Healthcare: operational forecasting and analytical decision support. Insurance: claims analysis and risk assessment. Telecommunications: customer churn prediction and network optimization. The emphasis throughout this topic is not simply on understanding machine learning algorithms, but on understanding when, why and how machine learning creates business value.

TOPIC 9: STRATEGIC BUSINESS ANALYTICS AND DECISION-MAKING
Topic Summary Topic 9 integrates analytical techniques with strategic business decision-making across major organizational functions. Lessons Lesson 1: Analytics-Driven Strategic Decision-Making Lesson 2: Customer and Marketing Analytics Lesson 3: Financial and Operational Analytics Lesson 4: Risk, Fraud and Performance Analytics Lesson 5: Predictive and Prescriptive Analytics for Business Strategy

TOPIC 10: RESPONSIBLE ANALYTICS, ANALYTICS LEADERSHIP AND CAPSTONE PROJECT
Topic Summary Topic 10 examines responsible use of analytics and develops the learner's ability to manage an end-to-end business analytics project. Lessons Lesson 1: Data Ethics, Bias and Responsible Analytics Lesson 2: AI Governance, Privacy and Analytical Risk Lesson 3: Analytics Leadership and Building a Data-Driven Organization Lesson 4: Designing and Managing a Business Analytics Project Lesson 5: Business Analytics Capstone Project and Executive Presentation