Business Data Analysis Course

Lesson 1 • Framing Analytical Questions

Translates vague business problems into precise, answerable analytical questions. Provides a structured problem-framing methodology used throughout the course.

Lesson 2 • The Role of Data in Business

Examines how data drives decisions across business functions. Establishes the analyst's role within organisational decision-making workflows.

Lesson 3 • Types and Sources of Business Data

Classifies structured, unstructured, and semi-structured data. Connects data source selection to analytical accuracy and business relevance.

Lesson 1 • Assessing Data Quality

Introduces six dimensions of data quality: accuracy, completeness, consistency, timeliness, validity, and uniqueness. Teaches systematic quality auditing.

Lesson 2 • Data Collection Methods

Surveys primary and secondary collection approaches for business contexts. Links collection method choice to data reliability and analytical goals.

Lesson 3 • Data Governance Fundamentals

Explains ownership, stewardship, and access control policies for business data. Grounds analysts in compliance and ethical data handling responsibilities.

Lesson 1 • Correlation and Covariance

Quantifies linear relationships between business variables using correlation coefficients. Establishes the conceptual foundation for regression analysis in Chapter 5.

Lesson 2 • Measures of Variability

Covers range, variance, standard deviation, and interquartile range. Connects variability measures to risk assessment and performance benchmarking.

Lesson 3 • Distributions and Shape

Explains normal, skewed, and bimodal distributions using business data. Prepares students to select appropriate statistical tests in subsequent chapters.

Lesson 4 • Summarising Data with Pivot Tables

Uses pivot tables to aggregate and cross-tabulate business data efficiently. Bridges descriptive statistics to the data visualisation techniques in the next chapter.

Lesson 1 • Storytelling with Data

Structures analytical findings into a narrative arc with a clear business recommendation. Prepares students to present data-driven arguments to non-technical audiences.

Lesson 2 • Principles of Effective Visualisation

Establishes visual encoding rules, pre-attentive attributes, and data-ink principles. Prevents common chart design errors that mislead business stakeholders.

Lesson 3 • Designing Business Dashboards

Covers layout hierarchy, KPI tiles, and interactivity for executive dashboards. Connects visualisation design to the decision-making workflows introduced in Chapter 1.

Lesson 1 • Interpreting and Acting on Test Results

Translates statistical test outputs into business recommendations with appropriate caveats. Addresses practical significance versus statistical significance for decision-making.

Lesson 2 • Sample Size and Statistical Power

Calculates required sample sizes to detect meaningful business effects reliably. Prevents underpowered tests that produce inconclusive or misleading results.

Lesson 3 • Common Statistical Tests for Business

Covers t-tests, chi-square tests, and ANOVA for comparing business groups. Connects test selection to data type and the distributional knowledge from Chapter 3.

Lesson 4 • Foundations of Hypothesis Testing

Introduces null and alternative hypotheses, significance levels, and p-values. Grounds statistical inference in the business decision context established in Chapter 1.

Lesson 1 • Simple Linear Regression

Fits a single-predictor regression model to business data and interprets slope and intercept. Builds directly on the correlation concepts introduced in Chapter 3.

Lesson 2 • Regression Diagnostics

Validates model assumptions through residual analysis and diagnostic plots. Ensures forecasts are statistically sound before use in business decisions.

Lesson 3 • Multiple Linear Regression

Extends regression to multiple predictors for complex business scenarios. Addresses multicollinearity and variable selection to improve model reliability.

Lesson 4 • Forecasting with Regression Models

Generates point and interval forecasts from fitted regression models. Communicates forecast uncertainty to business stakeholders using confidence intervals.

Lesson 1 • RFM Analysis for Customer Segmentation

Scores customers on recency, frequency, and monetary value to prioritise engagement. Produces actionable customer tiers directly applicable to marketing decisions.

Lesson 2 • Business Segmentation Strategy

Defines segmentation objectives across marketing, operations, and finance. Connects segment design to the KPI framework established in Chapter 1.

Lesson 3 • K-Means Clustering

Applies k-means to partition datasets into homogeneous clusters without predefined labels. Teaches cluster number selection using the elbow method and silhouette scores.

Lesson 1 • Ethics and Responsible Analytics

Addresses bias, fairness, transparency, and accountability in business analytics. Ensures analytical outputs meet ethical standards and stakeholder trust requirements.

Lesson 2 • Measuring ROI of Analytics Initiatives

Quantifies the financial and operational value generated by analytical projects. Connects measurement frameworks to the KPI and regression skills from earlier chapters.

Lesson 3 • Embedding Analytics in Decision Processes

Redesigns decision workflows to incorporate data checkpoints and analytical outputs. Reduces reliance on intuition by institutionalising evidence-based decision protocols.

Lesson 4 • Building an Analytics Strategy

Aligns analytics investments with business priorities using a capability maturity framework. Produces a roadmap for scaling analytical competency across the organisation.