SQL Data Analysis Project — DecodeLabs Week 3

Overview

Analyzed a structured retail sales dataset using SQL to extract business insights through filtering, sorting, grouping, and aggregation. This project simulates the core analytical workflow a data analyst performs when exploring a relational database to answer business questions.

Tools: SQL · SQLite · DB Browser for SQLite

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Business Questions Explored

• Which records meet specific criteria? (filtering with WHERE)
• How does the data rank when sorted by key metrics? (ORDER BY)
• What are the counts across different categories? (GROUP BY + COUNT)
• What are the average and total values across the dataset? (AVG, SUM)

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Key SQL Queries

Filtering records with WHERE

Isolated specific subsets of data to focus analysis on relevant segments.

Ranking results with ORDER BY

Sorted records by key fields to identify top and bottom performers.

Category breakdown with GROUP BY + COUNT

Grouped records by category to understand distribution across the dataset.

Averages with AVG

Calculated mean values to establish baselines and identify outliers.

Totals with SUM

Aggregated values to quantify overall volume and compare across groups.

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What I Demonstrated

• Writing and executing SQL queries against a relational database
• Filtering, sorting, and grouping structured data
• Using aggregate functions (COUNT, AVG, SUM) to summarize datasets
• Drawing analytical conclusions from query results
• Managing a SQLite database using DB Browser

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Project Files

File	Description
dataset_week3.csv	Raw dataset used for analysis
week3_data_analytics.db	SQLite database file
sql_where_clause.png	WHERE clause query + result
sql_order_by.png	ORDER BY query + result
sql_group_by_count.png	GROUP BY + COUNT query + result
sql_avg_function.png	AVG function query + result
sql_sum_function.png	SUM function query + result

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Author

Eddy Bartolome
Data Analyst | Python · SQL · Power BI · Excel
LinkedIn · GitHub