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🧭 About This Repository

This repository documents my complete 40-day structured journey learning SQL — from writing my very first SELECT query to building production-grade analytics on real BigQuery public datasets. Every phase contains real-world business problems, hands-on SQL exercises written from scratch, and AI-assisted workflows using ChatGPT, Claude, and Gemini.

The goal: be fully capable of working as a confident, interview-ready Data Analyst by Day 41.

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📐 Learning Structure

3 Portfolio Projects  |  7 Phases

Phase	Days	Focus	Level
Phase 1 — Foundations	Days 1–5	SELECT, WHERE, ORDER BY, DISTINCT, NULL Handling	Beginner → Informed
Phase 2 — Core SQL Mechanics	Days 6–10	Aggregations, GROUP BY, HAVING, Date & String Functions	Informed → Functional
Phase 3 — JOINs & Subqueries	Days 11–15	INNER/LEFT/FULL JOINs, Subqueries, EXISTS, Correlated	Functional → Competent
Phase 4 — CTEs & Advanced Filtering	Days 16–18	WITH, CASE WHEN, COALESCE, UNION, INTERSECT, EXCEPT	Competent → Proficient
Phase 5 — Window Functions	Days 19–25	ROW_NUMBER, RANK, LAG/LEAD, NTILE, Running Totals	Proficient → Advanced
Phase 6 — Business Analytics	Days 26–30	Cohort Analysis, Funnels, Retention, RFM, Revenue	Advanced → Professional
Phase 7 — Portfolio Projects	Days 31–35	3 End-to-End Projects, GitHub READMEs	Professional → Portfolio-Ready

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📊 Progress Tracker

Phase	Status	Progress
Phase 1 — Foundations	🔄 In Progress	2 / 5 days
Phase 2 — Core SQL Mechanics	⏳ Upcoming	0 / 5 days
Phase 3 — JOINs & Subqueries	⏳ Upcoming	0 / 5 days
Phase 4 — CTEs & Advanced Filtering	⏳ Upcoming	0 / 3 days
Phase 5 — Window Functions	⏳ Upcoming	0 / 7 days
Phase 6 — Business Analytics	⏳ Upcoming	0 / 5 days
Phase 7 — Portfolio Projects	⏳ Upcoming	0 / 5 days

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🔬 Skills Covered

Phase 1 — Foundations & Environment (Days 1–5)

• BigQuery Sandbox setup and public dataset access
• SELECT, FROM, LIMIT — basic row retrieval
• WHERE with all comparison operators: =, !=, <, >, <=, >=
• Logic operators: AND, OR, NOT with correct operator precedence
• BETWEEN for range filtering, IN for set filtering
• DISTINCT — single column and multi-column unique values
• ORDER BY — multi-column sorting with mixed ASC/DESC
• COUNT(*) vs COUNT(column) — critical NULL distinction
• LIKE with % and _ for pattern matching
• Arithmetic in SELECT for calculated columns
• SQL's 9-step logical execution order (the most misunderstood concept)
• Business-to-SQL translation mental model

Phase 2 — Core SQL Mechanics (Days 6–10)

• COUNT, SUM, AVG, MIN, MAX with NULL edge cases
• GROUP BY — single-column and multi-column grouping
• HAVING vs WHERE — when each runs and why it matters
• DATE_TRUNC, EXTRACT, DATE_DIFF for time-based analytics
• UPPER, LOWER, TRIM, CONCAT, SUBSTR, REPLACE for string manipulation
• IS NULL, IS NOT NULL, COALESCE for safe null handling
• Monthly revenue trends, category-level aggregations

Phase 3 — JOINs & Subqueries (Days 11–15)

• INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
• LEFT JOIN + IS NULL to find unmatched records
• Multi-table JOIN chains across 3+ tables
• SELF JOIN for hierarchical and comparative queries
• CROSS JOIN for combination generation
• Scalar subqueries in SELECT
• Derived table subqueries in FROM
• Subqueries in WHERE with IN and NOT IN
• Correlated subqueries referencing the outer query
• EXISTS and NOT EXISTS — and when to prefer them over IN

Phase 4 — CTEs & Advanced Filtering (Days 16–18)

• WITH Common Table Expressions — single and chained
• Recursive CTEs for hierarchical data
• CASE WHEN for conditional labeling and tiering
• CASE WHEN inside aggregate functions (pivot-style)
• COALESCE for null-safe output
• NULLIF to prevent division-by-zero
• UNION ALL, UNION — stacking result sets
• INTERSECT, EXCEPT — set algebra on query results

Phase 5 — Window Functions (Days 19–25)

• ROW_NUMBER() — deduplication, exact top-N per group
• RANK() and DENSE_RANK() — tie-handling differences explained
• LAG() and LEAD() — period-over-period calculations
• NTILE(n) — quartile and percentile segmentation
• SUM() OVER() — cumulative running totals
• AVG() OVER() with ROWS BETWEEN — moving averages
• PARTITION BY + ORDER BY + frame specification combined
• Top-N per group pattern — most common window function interview question

Phase 6 — Business Analytics with SQL (Days 26–30)

• Cohort analysis — monthly cohort construction from raw data
• Multi-step conversion funnel with drop-off rates
• Day-7 and Day-30 user retention using date arithmetic
• RFM scoring and customer segment labeling
• Revenue MoM and YoY comparison using LAG()
• NULLIF in all percentage calculations (division-by-zero protection)
• Translating a stakeholder's question into a complete analytics query

Phase 7 — Portfolio Projects (Days 31–35)

• End-to-end project scoping from a business question
• Independent dataset, table, and query strategy selection
• Professional GitHub README writing for non-technical readers
• 3 distinct business domains across 3 published projects
• Findings written in plain English for stakeholder presentation

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🏆 Portfolio Projects

#	Project	Domain	Key SQL Techniques
⭐	E-Commerce Analytics Deep Dive	E-Commerce	Cohort, Retention, CTEs, Window Functions
⭐	Urban Mobility Analytics	Transportation	EXTRACT, RANK(), Aggregation, Trends
⭐	Customer Segmentation & RFM	Marketing Analytics	NTILE, LAG, RFM Scoring, Multi-CTE

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🗃️ Datasets Used

Dataset	Domain	Phases
bigquery-public-data.thelook_ecommerce	E-Commerce	Phases 1–3, Projects 1 & 3
bigquery-public-data.chicago_taxi_trips	Transportation	Phase 2, Project 2
bigquery-public-data.austin_bikeshare	Urban Mobility	Phase 1, Project 2
bigquery-public-data.chicago_crime	Public Safety	Phase 1

All datasets are 100% free via Google BigQuery Sandbox — no credit card required.

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🛠️ Tools & Technologies

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💼 Connect With Me

_{Built with 💙 | Google BigQuery · Standard SQL · AI-Assisted Learning · 2026}