Data Analytics for Accountancy Course

Lesson 1 • The Analytics Workflow

Maps the end-to-end process from business question to actionable insight. Provides a repeatable framework applied throughout the course.

Lesson 2 • Ethics and Governance in Data Use

Covers data privacy principles, ethical obligations, and governance frameworks relevant to accountants. Grounds all subsequent technical work in professional responsibility.

Lesson 3 • Data Types and Structures

Distinguishes structured, semi-structured, and unstructured data relevant to accounting. Prepares students to recognise appropriate data sources for analysis.

Lesson 4 • The Accountant's Evolving Data Role

Defines how digital transformation reshapes accounting responsibilities. Establishes why analytical competency is now a core professional requirement.

Lesson 1 • Data Cleaning and Standardisation

Covers methods to correct, impute, deduplicate, and standardise financial data. Produces consistent datasets ready for downstream analysis.

Lesson 2 • Data Quality Dimensions

Defines accuracy, completeness, consistency, timeliness, and validity as measurable quality dimensions. Teaches students to assess datasets against each dimension systematically.

Lesson 3 • Identifying and Profiling Data Issues

Applies profiling techniques to detect missing values, duplicates, and outliers in financial datasets. Builds diagnostic skills before remediation is introduced.

Lesson 4 • Accounting Data Sources and Systems

Surveys enterprise resource planning systems, general ledgers, and subsidiary records as primary data sources. Establishes where accounting data originates before analysis begins.

Lesson 1 • Advanced Formulas and Functions

Extends beyond basic arithmetic to lookup, logical, text, and date functions essential for accounting workflows. Reduces manual effort and formula errors.

Lesson 2 • Financial Modelling Techniques

Constructs structured financial models using best-practice layout, assumptions, and scenario controls. Prepares students for budgeting and forecasting applications.

Lesson 3 • Data Validation and Auditing Tools

Applies built-in auditing features to trace precedents, dependents, and validation rules in financial spreadsheets. Strengthens spreadsheet integrity and review efficiency.

Lesson 4 • PivotTables for Financial Summarisation

Uses PivotTables to aggregate, slice, and drill into large transaction datasets. Directly supports trial balance review and variance analysis tasks.

Lesson 1 • Relational Database Fundamentals

Explains tables, keys, relationships, and schemas as they appear in accounting systems. Provides the conceptual map needed before writing any queries.

Lesson 2 • Aggregation and Grouping

Uses GROUP BY, HAVING, and aggregate functions to summarise transaction data by account, period, or entity. Directly replicates trial balance and ledger summary logic.

Lesson 3 • Core SELECT Query Techniques

Covers SELECT, WHERE, ORDER BY, and LIMIT clauses to retrieve targeted financial records. Forms the foundation for all subsequent query complexity.

Lesson 4 • Window Functions for Period Analysis

Applies ranking, running totals, and lag functions to compute period-over-period financial metrics. Enables trend analysis directly within SQL without post-processing.

Lesson 5 • Joining Multiple Tables

Applies INNER, LEFT, and other joins to combine ledger, customer, and vendor tables. Enables multi-source financial reporting from normalised databases.

Lesson 1 • Building Dashboards in Visualisation Tools

Constructs multi-panel dashboards connecting financial KPIs, trends, and breakdowns in a single view. Applies layout and interactivity best practices for executive reporting.

Lesson 2 • Selecting the Right Chart Type

Maps financial analysis goals to appropriate chart types including bar, line, waterfall, and scatter plots. Ensures visual form matches the analytical message.

Lesson 3 • Storytelling with Financial Data

Structures a data narrative with a clear finding, supporting evidence, and recommended action. Bridges analytical output to stakeholder communication and decision support.

Lesson 4 • Visualising Budget vs. Actual Analysis

Applies visualisation techniques specifically to variance and budget-versus-actual reporting scenarios. Reinforces analytical skills from prior chapters through visual output.

Lesson 1 • Benford's Law and Anomaly Detection

Applies Benford's Law and statistical thresholds to flag unusual patterns in financial data. Introduces a foundational fraud detection technique grounded in statistical theory.

Lesson 2 • Descriptive Statistics for Financial Data

Computes measures of central tendency, dispersion, and distribution shape for financial datasets. Provides the statistical baseline for all subsequent inferential work.

Lesson 3 • Hypothesis Testing in Accounting

Applies t-tests and chi-square tests to compare financial figures and test assumptions. Enables accountants to validate or challenge reported figures with statistical evidence.

Lesson 4 • Correlation and Regression Analysis

Measures relationships between financial variables and builds simple regression models for forecasting. Directly supports cost behaviour analysis and revenue prediction.

Lesson 1 • Accounts Payable and Receivable Analytics

Detects duplicate payments, fictitious vendors, and aging anomalies in payables and receivables data. Applies query and profiling skills to high-risk transaction cycles.

Lesson 2 • Analytics-Driven Audit Planning

Integrates data analytics into risk assessment and audit scope decisions. Shifts audit focus from sample-based to full-population testing where feasible.

Lesson 3 • Fraud Risk Scoring Models

Builds weighted risk scores by combining multiple fraud indicators into a composite flag. Prioritises audit effort toward highest-risk transactions and entities.

Lesson 4 • Continuous Auditing Frameworks

Designs automated, recurring analytics routines that monitor controls and transactions in near real time. Extends audit coverage beyond point-in-time engagements.

Lesson 1 • Classification Models for Credit and Risk

Introduces logistic regression and decision trees to classify customers by credit risk or default probability. Supports credit management and provisioning decisions.

Lesson 2 • Regression-Based Financial Forecasting

Extends simple regression to multiple predictors for cost, revenue, and cash flow forecasting. Applies model validation techniques to ensure reliable predictions.

Lesson 3 • Time Series Forecasting Methods

Applies moving averages, exponential smoothing, and trend decomposition to forecast revenue and costs. Builds on regression skills to handle temporal data structures.

Lesson 4 • Integrating Forecasts into Management Reporting

Embeds predictive model outputs into rolling forecasts and management dashboards. Closes the loop between analytical models and executive decision-making processes.