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SQL Mastery for Engineers & Analysts
Go from basic SELECT statements to writing production-grade analytical queries that drive decisions.
CREATED BY
R
Rhea B. [PLACEHOLDER] ★ 4.9
Product Manager at FinUPI | 6+ years of experience
About this Path
Designed for software engineers and data analysts who need real SQL fluency, not just syntax recall. You will progress from relational fundamentals through window functions, query optimization, and schema design. By the end you will write confident, performant SQL that passes senior-level interviews and handles production data at scale.
Path Overview
Beginner LevelCertificate of CompletionAbout 28 hours to completeEnglish language14+ curated videosLearn online at your own pace5 modules with resourcesGamified & interactive
Path Curriculum
Tables, Keys & Constraints
Primary keys, foreign keys, UNIQUE and NOT NULL constraints explained through real schemas.
SELECT, WHERE & ORDER BY
Filtering, sorting, and aliasing rows with precise predicate logic.
Aggregations & GROUP BY
COUNT, SUM, AVG, MIN, MAX with HAVING to filter aggregated results.
JOINs Deep Dive
INNER, LEFT, FULL OUTER, CROSS — when each applies, with Venn diagrams to results.
Subqueries vs CTEs
Rewriting correlated subqueries as cleaner WITH clauses for readability and planning.
Set Operations
UNION, INTERSECT, EXCEPT — deduplication rules and performance implications.
String & Date Functions
SUBSTRING, REGEXP, DATE_TRUNC, DATEDIFF across PostgreSQL and MySQL dialects.
NULL Handling
COALESCE, NULLIF, IS DISTINCT FROM — avoiding silent logic bugs in aggregations.
PARTITION BY & OVER Clause
Defining window frames and understanding how rows see each other.
Ranking Functions
ROW_NUMBER, RANK, DENSE_RANK — interview staples for deduplication and top-N problems.
Running Totals & Moving Averages
SUM OVER, AVG OVER with ROWS/RANGE BETWEEN frame specifications.
LAG, LEAD & FIRST_VALUE
Time-series deltas, period-over-period comparisons, and first-touch attribution.
1NF through 3NF
Eliminating repeating groups, partial and transitive dependencies with worked examples.
Star & Snowflake Schemas
Dimensional modeling for analytics — fact tables, dimension tables, surrogate keys.
When to Denormalize
Read-heavy OLAP patterns where duplication beats joins for query speed.
EXPLAIN & Execution Plans
Reading seq scans, index scans, hash joins, and sort nodes in PostgreSQL EXPLAIN ANALYZE.
Index Design
B-tree, composite, partial, and covering indexes — when each type pays off.
Analytical SQL Patterns
Retention curves, funnel conversion, cohort analysis, and rolling 30-day active users.
Transactions & Locking Basics
ACID guarantees, isolation levels, and avoiding deadlocks in write-heavy services.
What you'll learn
- ✓Write multi-table JOIN queries using INNER, LEFT, RIGHT, and FULL OUTER semantics without looking them up.
- ✓Use window functions (ROW_NUMBER, RANK, LAG, LEAD, SUM OVER) to solve ranking and time-series problems.
- ✓Design normalized schemas up to 3NF and know when to intentionally denormalize for read performance.
- ✓Interpret EXPLAIN / EXPLAIN ANALYZE output and add indexes that measurably reduce query cost.
- ✓Build reusable CTEs and recursive queries to replace nested subquery spaghetti.
- ✓Translate product analytics questions (retention, funnel, cohort) directly into working SQL.