Excel Course for Data Analysis

Lesson 1 • Data Entry and Validation Basics

Introduces structured data entry, AutoFill, and basic input validation rules. Clean entry habits prevent errors that compound in later analysis steps.

Lesson 2 • Workbook and Worksheet Management

This module teaches you to create, rename, move, and protect sheets within a workbook. It provides the organisational foundation for multi-sheet data projects.

Lesson 3 • Saving, Sharing, and File Formats

This section covers file format options, AutoSave, and cloud-based sharing settings. It ensures your work is preserved and accessible across collaborative environments.

Lesson 4 • Navigating the Excel Environment

This section covers the Ribbon, Quick Access Toolbar, and worksheet navigation shortcuts. It establishes the spatial awareness needed for all subsequent data work.

Lesson 1 • Sorting and Filtering Data

This section applies single-level and multi-level sorts and AutoFilter to isolate subsets. These skills are prerequisites for aggregation and pivot analysis.

Lesson 2 • Cell and Number Formatting

This module applies number, date, currency, and custom formats to cells. Proper formatting ensures data is interpreted correctly in calculations and charts.

Lesson 3 • Converting Ranges to Excel Tables

This section converts data ranges into structured Excel Tables with automatic expansion and filtering. Tables are the preferred container for all analytical datasets.

Lesson 4 • Data Cleaning Techniques

This module removes duplicates, trims whitespace, and standardises inconsistent entries. Clean data is the prerequisite for accurate formulas and summaries.

Lesson 1 • Logical Functions and Nested Logic

This section builds IF, AND, OR, and nested logical statements for conditional calculations. Logical functions enable dynamic outputs based on data conditions.

Lesson 2 • Text Functions for Data Manipulation

This module applies LEFT, RIGHT, MID, CONCATENATE, and TEXT to reshape string data. Text functions are essential for parsing and reformatting imported datasets.

Lesson 3 • Date and Time Functions

This module calculates durations, extracts date parts, and builds date-based conditions. Date functions support time-series analysis and deadline tracking.

Lesson 4 • Mathematical and Statistical Functions

This section covers SUM, AVERAGE, COUNT, MIN, MAX, and rounding functions. These form the arithmetic backbone of every analytical model in the course.

Lesson 1 • VLOOKUP and HLOOKUP Fundamentals

This section explains vertical and horizontal lookup syntax, match types, and common errors. It provides the baseline lookup skill before introducing modern alternatives.

Lesson 2 • INDEX and MATCH for Flexible Lookups

This module combines INDEX and MATCH to retrieve values from any column or row direction. It overcomes VLOOKUP's column-order limitation for complex datasets.

Lesson 3 • XLOOKUP and Modern Lookup Functions

This module uses XLOOKUP for cleaner syntax, default values, and reverse-direction searches. It positions you to use the current best-practice lookup approach.

Lesson 4 • Reference Functions for Dynamic Ranges

This section applies OFFSET, INDIRECT, and CHOOSE to build dynamic, self-adjusting references. These functions enable dashboards and models that update automatically.

Lesson 1 • Calculated Fields and Items

This module creates custom metrics inside PivotTables using calculated fields and items. It extends built-in aggregations without altering the source dataset.

Lesson 2 • Slicers and Timelines for Interactivity

This section adds slicers and timeline controls to filter PivotTables visually. It transforms static summaries into interactive reporting tools for stakeholders.

Lesson 3 • Value Field Settings and Show Values As

This section configures percentage of total, running totals, and rank calculations. These settings reveal proportional and trend insights within the same PivotTable.

Lesson 4 • Grouping and Summarizing Data

This module groups dates, numbers, and text fields to create meaningful aggregation levels. Grouping transforms transactional data into analytical summaries.

Lesson 1 • Sparklines and In-Cell Visuals

This module inserts sparklines and data bars directly into cells for compact trend display. In-cell visuals complement full charts in dense summary tables.

Lesson 2 • Choosing the Right Chart Type

This section maps analytical questions to appropriate chart types using a decision framework. Correct chart selection prevents misrepresentation of data relationships.

Lesson 3 • Formatting Charts for Clarity

This module applies titles, legends, gridlines, and colour schemes to maximise readability. Formatting choices directly affect how quickly audiences extract insight.

Lesson 4 • Building and Editing Charts

This section creates charts from table data and edits series, axes, and data labels. Hands-on construction reinforces the link between data structure and visual output.

Lesson 1 • Dynamic Array Functions Overview

This section introduces spill behaviour, the spill range operator, and implicit intersection changes. Understanding spill is essential before using any dynamic array function.

Lesson 2 • Sequence and Random Array Generation

This module generates numeric sequences and random samples using SEQUENCE and RANDARRAY. It supports simulation, test data creation, and dynamic numbering.

Lesson 3 • Unique Values and Frequency Analysis

This module applies UNIQUE and FREQUENCY to identify distinct values and distribution patterns. These functions replace manual deduplication and histogram binning.

Lesson 4 • Filtering and Sorting with Formulas

This module uses FILTER, SORT, and SORTBY to extract and order data programmatically. Formula-based filtering updates automatically when source data changes.

Lesson 1 • Formatting and Protecting the Final Report

This module applies consistent styling, hides gridlines, and locks cells to protect the layout. A polished, protected dashboard builds stakeholder trust and prevents errors.

Lesson 2 • Linking Charts and PivotTables to a Dashboard

This section connects PivotCharts and formula-driven charts to a single display sheet. Centralised linking ensures all visuals refresh from one data source.

Lesson 3 • Dashboard Planning and Layout Design

This section defines audience needs, KPIs, and grid-based layout before building. Planning prevents redesign and ensures the dashboard answers real business questions.

Lesson 4 • Automating Refresh and Data Updates

This section configures PivotTable refresh, Power Query load settings, and connection refresh. Automation reduces manual maintenance and keeps dashboards current.