Excel Projects - Data Technician Bootcamp

Welcome to my Excel projects page.

Overview

Excel is one of my core tools for data analysis, reporting, and automation. I use it to clean datasets, build dynamic formulas, create dashboards, and perform statistical analysis. My Excel work focuses on transforming raw data into clear insights using advanced functions, conditional logic, and interactive visualisations.

Core Skills

Advanced Formulas — SUMIFS, COUNTIFS, AVERAGEIFS, VLOOKUP/XLOOKUP, nested IF logic
Data Cleaning — validation, formatting, removing duplicates, structured tables
Dashboards & Visualisation — charts, conditional formatting, slicers, KPIs
Pivot Tables — grouping, summarising, filtering, calculated fields
Statistical Analysis — CORREL, descriptive statistics, trend analysis
Aggregation & Lookups — category totals, demographic breakdowns, multi‑criteria analysis

Retail Sales Data Set

This project analyses a retail dataset to uncover customer behaviour, product performance, and commission trends. Using advanced Excel functions, I built dynamic logic to classify transactions, aggregated sales across categories, and applied statistical methods to explore relationships between age and spending. The dataset was transformed into a structured, insight‑driven report that highlights value tiers, demographic patterns, and overall sales performance.

Key Methods:

Conditional Logic: Built dynamic nested IF statements to flag transactions as High Value, Standard, or Low Value.
Lookups & Aggregation: Leveraged SUMIF, SUMIFS, and AVERAGE to aggregate revenue across product categories (Clothing, Beauty, Electronics) and demographics.
Statistical Analysis: Applied CORREL() to measure linear relationships between customer age and total purchase values.

Summary Table

The summary table consolidates key retail metrics across product categories, providing a clear comparison of sales performance. It includes total sales per category, gender‑based product quantities, and average total sales. This table offers a quick, structured view of how each category performs and how customer demographics influence purchasing behaviour.

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