🎓 5-Day Micro-Learning Syllabus: Basic SQL Joins (INNER, LEFT, RIGHT) 🎯 Course Overview Target Audience: Junior marketers and data analysts who know basic SELECT statements but struggle combining multiple tables Time Commitment: ~15-20 minutes per day End Goal: By Day 5, you will be able to independently write a multi-table query that pulls campaign performance data alongside customer records, choosing the right join type and correctly handling missing matches. 📅 The 5-Day Curriculum Day 1: Why Joins Exist (Thinking in Related Tables) 🧠 The Concept (5 mins): Databases spread data across separate tables to avoid duplication — a customers table and an orders table share a common key (like customer_id) instead of repeating customer info on every order row. A join is how you temporarily stitch those tables back together for a query. 💡 Key Takeaway: A join connects rows from two tables based on a shared key column — nothing more, nothing less. 🛠️ Actionable Exercise (10 mins): Sketch (on paper or in a doc) two tiny tables: customers (customer_id, name) with 4 rows, and orders (order_id, customer_id, amount) with 6 rows, where one customer has 2 orders and one customer has 0 orders. Manually draw lines connecting matching customer_id values. This visual "matching" is exactly what a JOIN automates. Day 2: INNER JOIN — Only the Matches 🧠 The Concept (5 mins): INNER JOIN returns only rows where the key exists in both tables. If a customer has no orders, they simply disappear from the result. Syntax: SELECT c.name, o.amount FROM customers c INNER JOIN orders o ON c.customer_id = o.customer_id; 💡 Key Takeaway: INNER JOIN = "give me only the rows that match on both sides." 🛠️ Actionable Exercise (10 mins): Using your Day 1 sketch, manually write out the result table you'd expect from an INNER JOIN of customers and orders. Count the rows — the customer with 0 orders should be missing entirely. Then, if you have access to a SQL sandbox (e.g., SQLite Online, BigQuery sandbox, or your company's dev environment), write and run this INNER JOIN against real or sample tables. Day 3: LEFT JOIN — Keep Everything on the Left 🧠 The Concept (5 mins): LEFT JOIN returns all rows from the left (first) table, plus matching data from the right table — filling in NULL where there's no match. This is the marketer's best friend for questions like "show me all customers, even ones who never purchased." Syntax: SELECT c.name, o.amount FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id; 💡 Key Takeaway: LEFT JOIN = "keep every row on the left, match what you can from the right." 🛠️ Actionable Exercise (10 mins): Rewrite your Day 2 query as a LEFT JOIN. Predict how many rows will now appear (hint: it should include the customer with 0 orders, showing NULL for amount). Run it in a sandbox if possible and confirm your prediction. Bonus: add WHERE o.order_id IS NULL to isolate customers who never ordered — a classic "inactive customer" report. Day 4: RIGHT JOIN — Keep Everything on the Right (and Spotting the Pattern) 🧠 The Concept (5 mins): RIGHT JOIN is the mirror image of LEFT JOIN — it keeps all rows from the right table and fills NULLs for missing left-side matches. In practice, most analysts avoid RIGHT JOIN and just swap table order to use LEFT JOIN instead, but recognizing it is essential for reading queries written by others. 💡 Key Takeaway: RIGHT JOIN = LEFT JOIN with the tables flipped — same logic, opposite direction. 🛠️ Actionable Exercise (10 mins): Take your Day 3 LEFT JOIN query and rewrite it as an equivalent RIGHT JOIN by swapping which table comes first (FROM orders o RIGHT JOIN customers c ...). Confirm the results are identical. Then find one real example (ask a teammate or search your team's SQL codebase/docs) of a query using RIGHT JOIN, and rewrite it as a LEFT JOIN for practice. Day 5: Capstone — Building a Marketing Report Query 🧠 The Concept (5 mins): Real reporting questions combine everything: choosing the right join type depends on the question you're answering. "Which customers bought nothing this quarter?" needs LEFT JOIN + NULL check. "What's total revenue per customer who ordered?" needs INNER JOIN. Always ask: "Do I need rows with no match, or only the matches?" 💡 Key Takeaway: The question you're answering — not habit — determines which join you use. 🏆 Capstone Challenge (15 mins): Using your customers and orders tables (or your real company data if accessible), write two queries from scratch without looking back at earlier days: (1) an INNER JOIN showing total spend per customer who has ordered, using SUM() and GROUP BY; (2) a LEFT JOIN showing all customers with a spend column that reads 0 (not NULL) for non-buyers, using COALESCE() or IFNULL(). This combines joins with aggregation — a skill you'll use in nearlyly every marketing reportview. 🚀 Next Steps Resource Recommendation: Practice on a free interactive platform like SQLZoo or Mode Analytics' SQL Tutorial, both of which have dedicated join-focused exercises with instant feedback. Once joins feel natural, explore GROUP BY + multi-join queries, which are the backbone of most marketing dashboards.