SQL for Analytical Reporting – the Direct Route to Your Data (SQLDA)

Course content:

• Why SQL and How to Think About Data
1. Where you encounter SQL: Power Query, Python, CRM/ERP, and BI tools
2. Mental model: table, column, row, and data type
3. The difference between a database, schema, table, and view
4. How to communicate with IT/DBAs—what access, a view, and a schema mean

• The Anatomy of a SELECT Query
1. Four questions before every query: where from → what to filter → what to display → how to sort
2. The FROM, WHERE, SELECT, ORDER BY, and LIMIT / TOP clauses
3. The logical versus written order of query evaluation
4. Filtering operators: =, <, BETWEEN, IN, LIKE
5. Working habit: test the query on a small sample before running it in full
6. Reading error messages without panicking

• Aggregation as a Pivot Table
1. Aggregate functions: SUM, COUNT, AVG, MIN, MAX
2. The GROUP BY clause as a direct analogy to pivot table row fields
3. WHERE vs. HAVING—filtering before aggregation versus after aggregation
4. COUNT(*) vs. COUNT(column)—why they are not the same
5. Column aliases and result readability for further processing

• Data Hygiene and Common Problems
1. The unknown value NULL: how to work with “empty cells” that behave differently from zero and how to handle them safely
2. Detecting duplicates using GROUP BY + HAVING COUNT(*) > 1
3. Cleaning functions: DISTINCT, TRIM, LOWER/UPPER, COALESCE
4. Typical pitfalls: inconsistencies in text values and different representations of the same status
5. Checking whether results make sense before using them further in a report

• Joining Tables—Putting Relationships Together
1. Why data does not live in a single table—an introduction to normalization
2. INNER JOIN (intersection)—selecting records that have matching pairs in both tables
3. LEFT JOIN (the XLOOKUP/VLOOKUP analogy)—how to add corresponding information to the main data and handle unmatched records
4. UNION ALL (stacking vertically)—combining data from identical structures
5. The analyst’s decision tree—how to identify which type of join you need at a given moment so that no data is lost

• SQL and AI—Effective Collaboration
1. When AI helps with SQL (boilerplate, translating requirements, fixing errors) and when it fails (unknown schema, business logic, performance)
2. How to give AI the right context: table schemas, sample data, and the business semantics of columns
3. Four prompt types: generate, fix, explain, rewrite
4. Typical hallucinations in SQL output and how to detect them (invented column, incorrect JOIN type, silent filtering of NULL)
5. Verification checklist—applying everything from the previous modules
6. Iterative workflow: requirements → AI proposal → sample test → verification → correction

• Demonstration of Connecting SQL to Analytical and Reporting Tools