# Curriculum Guide This curriculum is designed to move a learner from **zero SQL confidence** to **expert-level analytical maturity**. The progression is intentional: - simple - guided - practical - cumulative - increasingly analytical --- # Stage 1 — Foundations ## Module 1: SQL Mindset and Relational Thinking **Goal:** Build conceptual clarity before syntax overload. ### Learner should understand - what SQL is - where SQL fits in analytics and data science - the meaning of table, row, column, record, field - keys and relationships - how to inspect a schema ### Why this module matters If learners skip this conceptual layer, joins and grouped analysis become mechanical memorisation instead of understanding. --- ## Module 2: SELECT, LIMIT, ORDER BY, and Aliases **Goal:** Retrieve data cleanly and readably. ### Topics - SELECT column(s) - FROM table - LIMIT - ORDER BY ASC and DESC - column aliases - avoiding overuse of `SELECT *` ### Outcome The learner can inspect data and create basic readable outputs. --- ## Module 3: Filtering with WHERE **Goal:** Ask focused questions of a dataset. ### Topics - comparison operators - AND / OR - BETWEEN - IN - LIKE - NULL checks - date filters ### Outcome The learner can narrow data to relevant records. --- ## Module 4: Calculated Columns, CASE Logic, and NULL Safety **Goal:** Transform raw data into analytical output. ### Topics - arithmetic in SQL - derived columns - CASE WHEN - COALESCE - simple categorisation logic - rounding ### Outcome The learner can add business meaning to raw data. --- # Stage 2 — Analyst Core ## Module 5: Aggregation **Goal:** Move from row-level inspection to summary-level analysis. ### Topics - COUNT - SUM - AVG - MIN - MAX - row-level versus summary-level thinking ### Outcome The learner can answer “how many,” “how much,” and “what is the average” questions. --- ## Module 6: GROUP BY and HAVING **Goal:** Compare segments and categories. ### Topics - grouping by one column - grouping by multiple columns - HAVING versus WHERE - grouped metrics for reports ### Outcome The learner can compare categories, cohorts, channels, and departments. --- ## Module 7: Joins for Real Analysis **Goal:** Connect related tables correctly. ### Topics - INNER JOIN - LEFT JOIN - join keys - grain awareness - duplicate risk in joins - multi-table joins - self joins ### Outcome The learner can combine datasets into one analytical result. --- ## Module 8: String, Date, and Numeric Functions **Goal:** Clean and standardise values. ### Topics - TRIM - UPPER / LOWER - LENGTH - concatenation - date extraction - numeric formatting ### Outcome The learner can prepare more analysis-ready fields directly in SQL. --- # Stage 3 — Data Science SQL ## Module 9: Subqueries and CTEs **Goal:** Handle multi-step analytical thinking. ### Topics - subqueries in WHERE - subqueries in FROM - nested logic - Common Table Expressions - readable multi-step query design ### Outcome The learner can break a complex problem into smaller logical steps. --- ## Module 10: Window Functions and Ranking **Goal:** Perform advanced row-aware calculations. ### Topics - ROW_NUMBER - RANK - DENSE_RANK - PARTITION BY - ORDER BY inside windows - LAG / LEAD - running totals ### Outcome The learner can rank, compare, and track trends without collapsing detail rows. --- ## Module 11: Data Cleaning and Quality Checks in SQL **Goal:** Improve reliability before reporting or modelling. ### Topics - duplicate detection - missing-value checks - suspicious values - audit summaries - quality flags with CASE - support-risk detection queries ### Outcome The learner stops trusting data blindly. --- ## Module 12: Exploratory Data Analysis with SQL **Goal:** Use SQL to investigate rather than merely report. ### Topics - segment analysis - cohort analysis - trend analysis - high versus low performers - outlier exploration - comparing business or learner groups ### Outcome The learner can use SQL as an exploratory reasoning tool. --- # Stage 4 — Advanced Analytics Engineering ## Module 13: Feature Engineering with SQL for Machine Learning **Goal:** Connect SQL directly to model-building workflows. ### Topics - entity-level feature tables - recency, frequency, averages, ratios - behaviour-based features - wide-table preparation for ML pipelines ### Outcome The learner can create model-ready variables using SQL. --- ## Module 14: Query Optimisation and Analytics Engineering Foundations **Goal:** Move from working SQL to maintainable SQL. ### Topics - query readability - CTE structure - indexing concepts - analytical grain - fact versus dimension thinking - naming and documentation ### Outcome The learner writes cleaner, more maintainable, more scalable SQL. --- ## Module 15: Capstone Labs, Portfolio Projects, and Interview Readiness **Goal:** Convert learning into evidence. ### Topics - scenario-based mini-projects - stakeholder-style explanation - dashboard-support queries - interview-style problems - portfolio packaging ### Outcome The learner can demonstrate SQL skill publicly and professionally. --- # Suggested teaching sequence ## Beginner pacing - 2 to 3 modules per week - daily short practice - one review day every week ## Intermediate pacing - one major module every 4 to 5 days - stronger focus on joins and grouped analysis - repeated practice using the built-in query packs ## Advanced pacing - project-first learning - challenge completion - business-question translation practice - explanation of SQL in plain English --- # Assessment design suggestion A strong SQL platform should assess at multiple levels. ## Level 1 — Recall and syntax confidence Examples: - select the right columns - filter correctly - sort correctly ## Level 2 — Analytical reasoning Examples: - group by category - compare revenue by channel - find low-performing cohorts ## Level 3 — Multi-step logic Examples: - subqueries - CTEs - joins with conditions - derived metrics ## Level 4 — Expert interpretation Examples: - explain the grain of a query - justify why a LEFT JOIN is required - discuss why a metric may mislead stakeholders --- # Recommended capstone project tracks ## Track A — Retail analytics Possible questions: - Which channel drives the highest revenue? - Which customers are above average in revenue? - Which product category performs best? ## Track B — EdTech analytics Possible questions: - Which learners are at support risk? - Which course has the best average score? - What attendance patterns predict weaker performance? ## Track C — Workforce analytics Possible questions: - Which departments pay the most? - Who reports to whom? - Who ranks highest by salary or performance within each department? --- # Why this curriculum works This curriculum is strong because it is: - cumulative - beginner-safe - practical - aligned with real data work - connected to business, education, and data science use cases - suitable for portfolio building It does not teach syntax in isolation. It teaches **thinking with SQL**.