Subject Overview

Excel for Business Analytics develops the spreadsheet, analytical, managerial, and governance capabilities required to transform business data into reliable insights and decisions. The subject integrates Excel fundamentals, data preparation, formulas, statistical analysis, visualization, forecasting, optimization, automation, AI-enabled features, and responsible spreadsheet management.

Learning Goal

By completing the subject, students will be able to design accurate and auditable Excel models; acquire, clean, analyze, visualize, and interpret business data; apply statistical, forecasting, and optimization methods; communicate managerial insights; automate workflows; and use emerging Excel technologies responsibly in professional settings.

Syllabus

1. Excel Foundations and the Business Analytics Workflow

Learning Objective: Understand workbook architecture, worksheets, ranges, data types, references, operators, file formats, navigation, productivity tools, and the descriptive–diagnostic–predictive–prescriptive analytics framework. Apply a structured workflow from business question and data collection to analysis, communication, and action.

Category: foundation

2. Spreadsheet Design, Data Quality, and Error Control

Learning Objective: Design maintainable spreadsheets using separation of inputs, calculations, and outputs; consistent conventions; named ranges; validation; documentation; and error checks. Recognize data-quality dimensions, common spreadsheet risks, formula errors, circular references, and principles of reproducibility and auditability.

Category: foundation

3. Formulas, Functions, and Business Calculations

Learning Objective: Build calculations using relative, absolute, and mixed references; arithmetic, aggregation, rounding, date, time, text, financial, and error-handling functions. Apply formulas to revenue, cost, margin, growth, working capital, productivity, and other business metrics.

Category: core

4. Logical Analysis and Decision Rules

Learning Objective: Use IF, IFS, AND, OR, NOT, SWITCH, conditional aggregation, and nested logic to model business policies, classifications, exceptions, risk rules, and performance thresholds. Translate managerial requirements into transparent and testable decision logic.

Category: core

5. Data Organization, Tables, Lookups, and Dynamic Arrays

Learning Objective: Structure data as tidy Excel Tables and use sorting, filtering, structured references, XLOOKUP, INDEX–MATCH, FILTER, SORT, UNIQUE, SEQUENCE, and related array techniques. Integrate datasets, create reusable analyses, and distinguish appropriate lookup and data-structure methods.

Category: core

6. Data Acquisition and Transformation with Power Query

Learning Objective: Import data from files, folders, databases, web sources, and organizational systems; profile, clean, reshape, merge, append, and refresh data using Power Query. Understand extract–transform–load processes, query dependencies, repeatability, and preparation for digital analytics workflows.

Category: application

7. PivotTables, PivotCharts, and Multidimensional Analysis

Learning Objective: Summarize and explore large datasets using PivotTables, grouping, calculated fields, slicers, timelines, and PivotCharts. Conduct drill-down, segmentation, variance, cross-tabulation, and managerial performance analysis efficiently.

Category: core

8. Descriptive Statistics and Exploratory Data Analysis

Learning Objective: Apply measures of central tendency, dispersion, position, distribution, correlation, outlier detection, and frequency analysis. Use the Analysis ToolPak and appropriate visual methods while interpreting statistical results, limitations, and business significance.

Category: core

9. Business Visualization, Dashboards, and Data Storytelling

Learning Objective: Select and design charts, conditional formatting, KPI scorecards, interactive dashboards, and executive reports according to visualization principles. Communicate patterns, comparisons, uncertainty, and actionable insights while avoiding misleading displays.

Category: application

10. Financial and Managerial Analytics Models

Learning Objective: Develop models for budgeting, cash flow, break-even analysis, pricing, profitability, investment appraisal, loan analysis, customer and product performance, and variance analysis. Use model assumptions, drivers, KPIs, and managerial interpretation to support business decisions.

Category: application

11. What-If Analysis, Scenario Planning, and Sensitivity Analysis

Learning Objective: Use Goal Seek, one- and two-variable data tables, Scenario Manager, sensitivity analysis, and structured assumptions to evaluate uncertainty and alternatives. Compare base, best, and worst cases and communicate decision risks and trade-offs.

Category: application

12. Forecasting, Regression, and Predictive Analytics

Learning Objective: Apply trend analysis, moving averages, exponential smoothing, seasonality, FORECAST functions, regression, and forecast accuracy measures. Evaluate assumptions, distinguish association from causation, validate models, and interpret predictions for planning and control.

Category: advanced

13. Optimization and Prescriptive Decision Models

Learning Objective: Formulate decision variables, objective functions, and constraints; solve linear, integer, and nonlinear business problems using Solver. Apply optimization to product mix, allocation, scheduling, logistics, staffing, and financial decisions, including sensitivity and feasibility interpretation.

Category: advanced

14. Automation, Reusable Models, and Excel Ecosystems

Learning Objective: Automate repetitive work using macros, VBA fundamentals, Office Scripts, templates, reusable functions, and refreshable workflows. Understand connections with Power BI, databases, cloud collaboration, APIs, and enterprise systems, selecting automation approaches based on value, control, and maintainability.

Category: strategic

15. Governance, Ethics, AI, and the Future of Excel Analytics

Learning Objective: Apply spreadsheet governance, version control, access protection, privacy, security, lineage, review, and model-risk controls. Critically use Copilot and generative AI for formulas, analysis, and documentation; verify outputs and address bias, confidentiality, explainability, and accountability. Evaluate emerging developments in Python in Excel, connected data, cloud analytics, low-code automation, and augmented decision support.

Category: contemporary