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MASTERY CHECKLIST · SQL

SQL Mastery Checklist — Data Analyst Roadmap

The complete SQL journey for aspiring data analysts. 32 priority-ranked topics, query patterns, a 30-60-90 day plan, 11 portfolio projects, real-world scenarios, and interview prep — everything you need to be job-ready.

Beginner → Advanced ~130 hrs totalIndustry-focused · Interview-ready
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Priority-Based Topic Checklist

Ordered by recommended learning sequence. Expand each for context; check off as you master them.

  • 01.SELECT, FROM, WHERE — SQL Core Syntax

    Most Critical
    Easy 2–3 hrsOrder #1● Critical

  • 02.Filtering & Conditional Logic — AND, OR, IN, BETWEEN, LIKE, CASE WHEN

    Must Know
    Easy 3–4 hrsOrder #2● Critical

  • 03.Aggregate Functions — COUNT, SUM, AVG, MIN, MAX, GROUP BY, HAVING

    Must Know
    Easy 3–4 hrsOrder #3● Critical

  • 04.SQL Joins — INNER, LEFT, RIGHT, FULL, SELF, CROSS

    Most Critical
    Medium 6–8 hrsOrder #4● Critical

  • 05.NULL Handling & Data Cleaning in SQL

    Must Know
    Easy 3–4 hrsOrder #5● Critical

  • 06.Subqueries & Correlated Subqueries

    Must Know
    Medium 4–5 hrsOrder #6● High

  • 07.CTEs (Common Table Expressions) — WITH Clause

    Must Know
    Medium 4–5 hrsOrder #7● Critical

  • 08.Window Functions — ROW_NUMBER, RANK, LEAD, LAG, Running Totals

    Most Critical
    Hard 5–6 hrsOrder #8● Critical

  • 09.Date & Time Functions — DATE_TRUNC, DATEDIFF, EXTRACT, Time Series

    Must Know
    Medium 4–5 hrsOrder #9● Critical

  • 10.String Functions — CONCAT, SUBSTRING, TRIM, LOWER, REPLACE, REGEXP

    Must Know
    Easy 3–4 hrsOrder #10● High

  • 11.UNION, UNION ALL, INTERSECT, EXCEPT — Set Operations

    Must Know
    Easy 2–3 hrsOrder #11● High

  • 12.Views, Temp Tables & Materialized Views

    Must Know
    Medium 3–4 hrsOrder #12● High

  • 13.Cohort Analysis SQL — Retention & Lifetime Value

    Must Know
    Hard 6–8 hrsOrder #13● Critical

  • 14.Funnel Analysis SQL — Conversion & Drop-off

    Hard 5–6 hrsOrder #14● High

  • 15.Customer Segmentation SQL — RFM & Behavioral

    Hard 5–6 hrsOrder #15● High

  • 16.Revenue & KPI Analytics SQL

    Medium 5–6 hrsOrder #16● High

  • 17.Query Optimization & Execution Plans

    Hard 6–8 hrsOrder #17● Critical

  • 18.Indexing Fundamentals for Analysts

    Medium 3–4 hrsOrder #18● High

  • 19.Data Warehouse Concepts — Star Schema, Fact/Dim Tables, OLAP vs OLTP

    Medium 4–5 hrsOrder #19● High

  • 20.Product Analytics SQL — DAU, MAU, Engagement Metrics

    Hard 5–6 hrsOrder #20● High

  • 21.Finance Analytics SQL — P&L, Revenue, Cost Analysis

    Hard 5–6 hrsOrder #21● High

  • 22.Marketing Analytics SQL — Campaign, Attribution & CAC

    Medium 4–5 hrsOrder #22● High

  • 23.Logistics & Operations Analytics SQL

    Medium 4–5 hrsOrder #23● High

  • 24.Advanced CASE WHEN — Pivoting & Conditional Aggregation

    Medium 3–4 hrsOrder #24● High

  • 25.Advanced Window Frame Clauses — ROWS BETWEEN, RANGE BETWEEN

    Hard 4–5 hrsOrder #25● High

  • 26.SQL for Dashboard Reporting — Reporting-Ready Queries

    Medium 4–5 hrsOrder #26● High

  • 27.PIVOT / UNPIVOT & Dynamic SQL Concepts

    Hard 4–5 hrsOrder #27● High

  • 28.Cloud SQL Platforms — BigQuery, Redshift, Snowflake, Databricks

    Medium 5–6 hrsOrder #28● High

  • 29.ETL / ELT SQL Patterns — Data Pipeline Queries

    Hard 5–6 hrsOrder #29● High

  • 30.Stored Procedures & User-Defined Functions

    Hard 4–5 hrsOrder #30● High

  • 31.SQL for A/B Testing & Experimentation

    Hard 5–6 hrsOrder #31● High

  • 32.Advanced Analytical Patterns — Sessionization, Gap-and-Island, Running Balance

    Hard 5–6 hrsOrder #32● High
Non-Negotiable

Must Know for Every Data Analyst

You will use all of these in the first week of any data analyst role.

Query Fundamentals

  • ›SELECT with explicit columns (never SELECT *)
  • ›WHERE with all filter operators
  • ›GROUP BY + HAVING for aggregations
  • ›ORDER BY with multiple columns
  • ›LIMIT for sampling and testing
  • ›Aliases for readability

Joins (Most Critical)

  • ›INNER JOIN — matching rows only
  • ›LEFT JOIN — preserve all left rows
  • ›Anti-join pattern (LEFT JOIN + WHERE IS NULL)
  • ›Multi-table joins (3+ tables)
  • ›Validate row counts before/after every join

Aggregation

  • ›COUNT(*) vs COUNT(col) vs COUNT(DISTINCT)
  • ›SUM, AVG, MIN, MAX
  • ›GROUP BY with correct column rules
  • ›HAVING for post-aggregation filters
  • ›Conditional aggregation with CASE WHEN + SUM

Date Operations

  • ›DATE_TRUNC for period grouping
  • ›DATEDIFF for interval calculation
  • ›EXTRACT for year, month, day of week
  • ›Filter on date range: > AND <
  • ›CURRENT_DATE / NOW() for relative dates

Query Structure

  • ›CTEs (WITH clause) for readability
  • ›Subqueries in FROM and WHERE
  • ›UNION ALL for combining datasets
  • ›CASE WHEN for conditional logic
  • ›NULL handling: IS NULL, COALESCE, NULLIF

Window Functions (Senior-Level)

  • ›ROW_NUMBER() for deduplication and ranking
  • ›RANK/DENSE_RANK for leaderboards
  • ›LAG/LEAD for period-over-period
  • ›SUM() OVER for running totals
  • ›PARTITION BY vs GROUP BY distinction
Advanced

Window Functions — Deep Dive Reference

The single most important advanced SQL skill. Master these patterns and you'll stand out in every interview.

Running & Rolling Calculations

  • SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total
  • AVG(daily_revenue) OVER (ORDER BY report_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS rolling_7d_avg
  • ROUND((revenue - LAG(revenue) OVER(ORDER BY month)) / NULLIF(LAG(revenue) OVER(ORDER BY month), 0) * 100, 2) AS mom_growth_pct
  • ROUND(revenue / SUM(revenue) OVER(PARTITION BY category) * 100, 2) AS pct_of_category

Ranking Functions

  • ROW_NUMBER(): always unique — 1, 2, 3 even on ties
  • RANK(): gaps on ties — 1, 1, 3
  • DENSE_RANK(): no gaps on ties — 1, 1, 2
  • NTILE(4): bucket rows into quartiles 1–4
  • PERCENT_RANK(): 0 to 1 percentile rank
  • CUME_DIST(): cumulative distribution 0–1

Navigation Functions

  • LAG(col, 1): value from previous row (MoM comparison)
  • LEAD(col, 1): value from next row (next-period preview)
  • FIRST_VALUE(col): first value in window (first order date)
  • LAST_VALUE(col) OVER (... ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
  • NTH_VALUE(col, n): nth value in window

Common Window Patterns

  • Deduplication: ROW_NUMBER() PARTITION BY key ORDER BY updated_at DESC — WHERE rn = 1
  • Top-N per group: RANK() PARTITION BY category ORDER BY revenue DESC — WHERE rnk <= 3
  • MoM growth: LAG(revenue) OVER (ORDER BY month) — calculate % change
  • First-purchase flag: ROW_NUMBER() PARTITION BY customer ORDER BY order_date — WHERE rn = 1
  • % of total: SUM(revenue) OVER () as denominator (no PARTITION)
Industry

Business Analytics SQL — Industry Patterns

The analytics patterns every product, growth, and business analyst writes regularly in industry.

E-Commerce Analytics

  • ›Revenue by category, brand, channel
  • ›Average basket size and average order value
  • ›Repeat purchase rate: customers with 2+ orders / total
  • ›Cart abandonment: added to cart but no purchase
  • ›Product affinity: frequently bought together
  • ›Discount impact on margin

SaaS/Subscription

  • ›MRR: SUM of recurring revenue this month
  • ›Churn rate: cancelled / start-of-period customers
  • ›Expansion MRR: upgrades within period
  • ›Net Revenue Retention (NRR)
  • ›Trial-to-paid conversion rate
  • ›Feature adoption by plan tier

Mobile/App Analytics

  • ›DAU/MAU ratio (stickiness)
  • ›Day-1/7/30 retention after install
  • ›Session duration distribution
  • ›Push notification click-through rate
  • ›User path analysis (event sequences)
  • ›Crash / error rate per app version

Finance Analytics

  • ›Gross margin %: (Rev - COGS) / Rev
  • ›Accounts receivable aging buckets
  • ›Budget vs Actual variance
  • ›Cash conversion cycle
  • ›Revenue by cost centre and GL code
  • ›YoY and MoM revenue trends

Logistics Analytics

  • ›On-time delivery rate by carrier
  • ›Average lead time by route/region
  • ›SLA breach count and % by week
  • ›Return rate by product category
  • ›Inventory turnover and stockout flags
  • ›Warehouse throughput by shift

Marketing Analytics

  • ›Cost per acquisition by channel
  • ›Campaign ROI: (Rev - Spend) / Spend
  • ›Email conversion rate by segment
  • ›Attribution modelling (first/last touch)
  • ›ROAS by ad set and audience
  • ›Multi-touch attribution with window functions
Structured

30-60-90 Day SQL Learning Plan

For a working analyst putting in 1–2 hrs/day. Weekends include 3–4 hr project sessions.

Days 1–30

Foundation + Joins + Aggregations

Target: 80% of real-world SQL work covered

Days 1–3
SQL Syntax Fundamentals
SELECT, WHERE, ORDER BY, LIMIT, aliases, DISTINCT — 20 exercises
Days 4–6
Filtering & CASE WHEN
AND/OR/IN/BETWEEN/LIKE/IS NULL, COALESCE, CASE WHEN segmentation
Days 7–9
Aggregate Functions
COUNT/SUM/AVG/MIN/MAX, GROUP BY, HAVING — 15 business exercises
Days 10–14
JOINS Mastery
All 5 join types, anti-join, multi-table, row count validation
Days 15–16
Weekend Project #1
Sales analysis: 3 tables, 10 business questions, written in SQL
Days 17–19
Date & String Functions
DATE_TRUNC, DATEDIFF, EXTRACT, text cleaning patterns
Days 20–22
Subqueries & CTEs
Scalar, table subqueries, EXISTS, multi-step CTEs — rewrite subqueries as CTEs
Days 23–25
NULL Handling & Cleaning
Data quality profiling, deduplication, standardization
Days 26–27
UNION / UNION ALL
Set operations, combining datasets, INTERSECT, EXCEPT
Days 28–30
Weekend Project #2
Customer analytics: joins + aggregation + CTEs + date functions
Days 31–60

Window Functions + Business Analytics + Optimization

Target: senior-level query writing and business analytics

Days 31–36
Window Functions
ROW_NUMBER, RANK, DENSE_RANK, NTILE, LAG, LEAD, SUM OVER, rolling avg — 25 exercises
Days 37–38
WF Project
Revenue dashboard: MoM growth, running total, Top-N per category, ranking
Days 39–43
Business Analytics SQL
Cohort retention, funnel analysis, RFM segmentation, KPI queries
Days 44–46
Domain Analytics
Finance SQL, product analytics (DAU/MAU), marketing attribution
Days 47–51
Query Optimization
Execution plans, EXPLAIN ANALYZE, indexing, partition pruning, cost reduction
Days 52–54
Data Warehouse Concepts
Star schema, fact/dim tables, OLTP vs OLAP, SCD, ETL patterns
Days 55–60
Capstone Project
End-to-end: 5-table model, cohort analysis, funnel, MoM trends, dashboard queries
Days 61–90

Advanced Patterns + Cloud SQL + Interview Prep

Target: production-ready SQL + interview confidence

Days 61–65
Advanced Patterns
Gap-and-island, sessionization, PIVOT patterns, recursive CTEs, A/B test queries
Days 66–68
Cloud SQL Platforms
BigQuery, Snowflake, or Redshift — dialect differences and best practices
Days 69–72
ETL / Pipeline SQL
MERGE, upserts, incremental loads, SCD Type 2, data quality gates
Days 73–75
Views & Materializations
Create views, materialized views, refresh strategies, dashboard views
Days 76–82
Interview Prep Sprint
LeetCode SQL 50, stratascratch top questions, mock interviews, explain out loud
Days 83–87
Portfolio Projects
2 portfolio-quality SQL projects with documentation and GitHub
Days 88–90
Review & Consolidate
Fill gaps, mock interview practice, review all patterns cold
Interview Prep

4-Phase Interview Preparation Roadmap

Expect live SQL exercises — practice for speed and accuracy.

1

Foundation — Core SQL Cold

Week 1 · Daily 1.5 hrs
Must write from memory
  • ›INNER JOIN, LEFT JOIN, anti-join pattern
  • ›GROUP BY + HAVING with aggregate filter
  • ›CASE WHEN for segmentation
  • ›CTE with 2+ steps
  • ›ROW_NUMBER() deduplication
Practice method
  • ›LeetCode SQL — Medium difficulty, timed
  • ›Write solution, then optimize it
  • ›Narrate your thinking out loud
  • ›stratascratch.com for real company questions
2

Window Functions Sprint

Week 2 · Daily 2 hrs
Master these patterns
  • ›ROW_NUMBER() deduplication
  • ›RANK/DENSE_RANK for leaderboards and Top-N
  • ›LAG for MoM/YoY comparison
  • ›SUM OVER for running totals
  • ›Rolling 7-day average with frame clause
  • ›FIRST_VALUE for first-touch attribution
Interview tips
  • ›Explain PARTITION BY vs GROUP BY when asked
  • ›Mention ROW_NUMBER vs RANK difference proactively
  • ›Discuss frame clause (ROWS BETWEEN) for rolling calcs
  • ›Note that window functions can't be used in WHERE — use CTE
3

Business Analytics Problems

Week 3 · Daily 1.5 hrs
Common scenario questions
  • ›Write a cohort retention query for monthly signups
  • ›Find the 2nd highest salary in each department
  • ›Calculate 7-day rolling average of daily revenue
  • ›Find customers who placed an order in Jan but not Feb
  • ›Write a funnel query for our sign-up flow
Answer framework
  • ›Restate the question + clarify assumptions
  • ›Describe your approach before coding
  • ›Build step-by-step using CTEs
  • ›Test with edge cases: NULLs, duplicates, empty groups
  • ›Mention performance considerations at the end
4

Top 40 Interview Questions

Week 4 · Daily 1 hr
Fundamentals & Joins
  • ›SQL query execution order? (FROM → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT)
  • ›Difference between WHERE and HAVING?
  • ›INNER JOIN vs LEFT JOIN — when does each produce different results?
  • ›How to find rows in Table A with no match in Table B?
  • ›A JOIN produces more rows than either input. Why and how to fix it?
  • ›What is a self-join? Give a business example.
  • ›COUNT(*) vs COUNT(col) vs COUNT(DISTINCT col)?
  • ›Why can't you use WHERE col = NULL? What do you use instead?
Business Analytics
  • ›Write a cohort retention query showing Month-0 to Month-3 retention.
  • ›How do you build a funnel analysis query?
  • ›Find month-over-month revenue growth %.
  • ›How do you find the second-highest salary per department?
  • ›Find customers who ordered in January but not in February.
  • ›How do you calculate DAU, WAU, and MAU?
  • ›Write an RFM segmentation query using NTILE.
  • ›How do you deduplicate records keeping only the most recent?

Top SQL Interview Questions

Common questions asked at data analyst interviews across tech, finance, and consulting.

CTEs & Subqueries

  1. What is a CTE and how is it different from a subquery?
  2. When would you use a temp table over a CTE?
  3. What is a correlated subquery? Why is it slow?
  4. Can you reference a CTE multiple times in the same query?
  5. What is a recursive CTE? Give a use case.

Window Functions

  1. Difference between ROW_NUMBER, RANK, and DENSE_RANK?
  2. What is PARTITION BY vs GROUP BY?
  3. How do you calculate a 7-day rolling average in SQL?
  4. Write a query to find the first order for each customer.
  5. How do you get the Top-3 products per category using window functions?
  6. Write a query to detect consecutive active days (streak).

Performance & Architecture

  1. A query that took 10s now takes 10 minutes. How do you debug it?
  2. What is an execution plan and what are hash joins vs nested loops?
  3. Why does applying a function to an indexed column hurt performance?
  4. What is the difference between a view and a materialized view?
  5. What is a star schema? Why is it preferred for analytics?
  6. What is the difference between OLTP and OLAP databases?
  7. What is partition pruning and how does it save query cost?
  8. When would you create an index on a table?
Portfolio

Hands-On Projects & Portfolio Ideas

Build these to prove real-world SQL mastery. Each covers multiple topic areas.

P1Beginner 3–4hrs

Sales Data Exploration

SELECT, WHERE, ORDER BY, LIMIT, simple aggregations, GROUP BY on a 10K-row sales dataset.

P2Beginner 4–5hrs

Customer Segmentation by Region

JOINs (customers + orders), CASE WHEN for segmenting, COUNT and SUM aggregations by region.

P3Beginner 3–4hrs

Monthly Revenue Report

DATE_TRUNC, GROUP BY month, SUM revenue, LAG for MoM change, COALESCE for NULL handling.

P4Intermediate 6–8hrs

Cohort Retention Analysis

CTEs for cohort definition, DATEDIFF for cohort age, cohort retention matrix with COUNT DISTINCT.

P5Intermediate 6–7hrs

Funnel Conversion Analysis

Sessionization CTE, event sequencing, funnel step conversion rates with LEAD/LAG.

P6Intermediate 7–9hrs

RFM Customer Segmentation

NTILE(5) for recency, frequency, monetary scores; combined RFM segment assignment.

P7Intermediate 5–6hrs

Product Sales Dashboard SQL

Window functions for running totals, RANK for top products, revenue per category.

P8Advanced 8–10hrs

User Retention & LTV Analysis

Cohort retention + LTV calculation using SUM OVER, cohort cumulative revenue.

P9Advanced 8–10hrs

Marketing Attribution Model

Multi-touch attribution with window functions, FIRST_VALUE for first touch, LAG for sequence.

P10Advanced 10–12hrs

Logistics Performance Analytics

On-time delivery, SLA breach analysis, carrier performance ranking (RANK), lead time distribution (NTILE).

P11Advanced 12–15hrs

Finance P&L Analytics

Revenue by cost centre, YoY/MoM trends, gross margin, budget vs actual with CASE WHEN.

Real-World

Business Scenarios You'll Actually Face

Common asks from actual analyst roles. Know how to solve each end-to-end.

Scenario 1 — 'Revenue dropped 15% last week - find out why'

Investigation steps
  • ›Break down by: region, product, channel, customer tier
  • ›Compare last week vs prior week using LAG or date filter
  • ›Check new vs returning customer mix
  • ›Identify if one segment drove entire drop
SQL approach
  • ›GROUP BY region + week using DATE_TRUNC
  • ›CASE WHEN to flag current vs prior week
  • ›SUM + conditional aggregation for side-by-side
  • ›Window LAG for % change per dimension
Output
  • ›Segment-level variance report
  • ›% contribution to drop per segment
  • ›Executive summary of findings
  • ›Recommended next steps to investigate

Scenario 2 — 'How many users are still active 30/60/90 days after signing up?'

Approach
  • ›CTE 1: first sign-up date per user
  • ›CTE 2: JOIN to activity table
  • ›Check if activity_date between signup and signup+30/60/90
  • ›COUNT(DISTINCT user_id) per retention window
Key functions
  • ›MIN(event_date) OVER — first activity
  • ›DATEDIFF / DATE_ADD for window boundaries
  • ›CASE WHEN for day-30/60/90 flag
  • ›COUNT(DISTINCT) for unique active users
Output
  • ›Month 0–3 retention matrix
  • ›Retention curve visualization
  • ›Churn flags by cohort
  • ›Executive retention dashboard

Scenario 3 — 'Identify our top 20% of customers by revenue'

Approach
  • ›SUM revenue per customer
  • ›NTILE(5) to segment into quintiles
  • ›Filter where quintile = 5 (top 20%)
  • ›Join back to orders for behaviour detail
Metrics to extract
  • ›Avg order frequency, AOV, preferred category
  • ›Days since last order (recency)
  • ›Geographic distribution
  • ›Acquisition channel using first-touch attribution
Output
  • ›VIP customer list
  • ›Behavioural profile of top customers
  • ›Targeted marketing recommendations
  • ›Retention strategy for VIPs

Scenario 4 — 'Find customers who bought in January but not in February'

Approach
  • ›CTE jan_buyers: DISTINCT customers who ordered in Jan
  • ›CTE feb_buyers: DISTINCT customers who ordered in Feb
  • ›EXCEPT / LEFT JOIN anti-join to find jan_buyers not in feb_buyers
  • ›Add profile info from customer table
Extension
  • ›Add their Jan revenue (how much are we losing?)
  • ›Categorize by last order value tier
  • ›Filter to specific product categories
  • ›This is a winback list for CRM campaigns
Output
  • ›Winback customer list
  • ›Revenue at risk report
  • ›Segment-specific offer recommendations
  • ›CRM campaign targeting file
Standards

SQL Best Practices Used at Companies

Production-standard SQL habits followed by analytics teams at tech companies, consulting firms, and data-driven organisations.

Code Style & Readability

  • ›Uppercase SQL keywords: SELECT, FROM, WHERE, JOIN
  • ›One clause per line, consistent indentation
  • ›Meaningful table aliases: cust not c, monthly_rev not t1
  • ›CTEs for every multi-step query — no deeply nested subqueries
  • ›Comment the WHY: -- Exclude test users (user_id < 8)
  • ›Add a header comment: date, author, purpose, dependencies

Data Integrity Checks

  • ›Row count before and after every JOIN
  • ›Validate primary key uniqueness: COUNT(*) vs COUNT(DISTINCT id)
  • ›Check for NULLs in key columns before aggregating
  • ›Verify date ranges cover expected period
  • ›Add ASSERT-style checks in data pipeline SQL
  • ›Never assume data is clean — always profile first

Performance Standards

  • ›Never SELECT * in production — name all columns
  • ›Filter on partition/index columns in WHERE
  • ›Push filters as early as possible (before joins)
  • ›Use LIMIT 100 during development/testing
  • ›UNION ALL over UNION unless deduplication is needed
  • ›Run EXPLAIN/EXPLAIN ANALYZE before submitting production queries

Production SQL Standards

  • ›Use views for reusable business logic
  • ›Materialized views for dashboard-serving queries
  • ›Parameterize dates — never hardcode '2024-01-01'
  • ›Version control SQL files in Git (dbt, GitHub)
  • ›Write idempotent queries (safe to re-run)
  • ›Document table grain: 'one row per customer per day'

Debugging Approach

  • ›Start simple — add complexity incrementally
  • ›Test CTEs individually before combining
  • ›Use SELECT * on intermediate CTEs to inspect results
  • ›Validate join cardinality at each step
  • ›Check that CASE WHEN ELSE handles all edge cases
  • ›Use NULLIF to prevent division-by-zero errors

Anti-Patterns to Avoid

  • ›Applying functions to indexed columns in WHERE
  • ›Correlated subqueries that run per row (use CTE + JOIN)
  • ›Using DISTINCT as a lazy fix for duplicate rows
  • ›Non-deterministic ORDER BY (same-value ties)
  • ›String concatenation for date filters
  • ›Magic numbers without comments: WHERE status = 4 (what is 4?)
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