HippoCampus
Technology & ComputingSpiralIntermediate14 chapters

SQL for analysts

HippoCampus
· starter course
I'm a data analyst who needs SQL for real work: SELECT and filtering, joins, aggregation, subqueries, CTEs and window functions, practised on realistic business questions.

What you'll learn

14 chapters
  1. 1

    SQL for Analytical Work: Purpose, Workflow, and Data Context

    • Explain what SQL is used for in analytics and how it differs from application-oriented database use.
    • Identify the kinds of business questions SQL helps answer.
    • Describe the basic workflow of querying tables, inspecting results, and refining analysis.
  2. 2

    Reading Data with SELECT

    • Use SELECT to retrieve columns from a table.
    • Distinguish between selecting all columns and choosing only needed fields.
    • Recognize how column choice shapes the usefulness of results.
  3. 3

    Filtering Rows to Match a Business Need

    • Apply filters to narrow result sets to relevant records.
    • Use common comparison and logical operators to express business conditions.
    • Recognize the role of filtering in data quality and analytical focus.
  4. 4

    Sorting and Limiting Results for Inspection and Reporting

    • Order results to make trends, extremes, and priorities visible.
    • Use row limiting to inspect samples or produce top-N outputs.
    • Explain how sorted outputs support analytical interpretation.
  5. 5

    Working with Derived Values and Clean Output

    • Create simple calculated fields in query output.
    • Use expressions to turn raw columns into more analysis-ready values.
    • Improve readability of results through naming and output shaping.
  6. 6

    Combining Tables with Joins

    • Explain why analysts join tables and what matching keys do.
    • Compare common join types and their effect on results.
    • Recognize risks such as dropped rows or duplicated records when joining.
  7. 7

    Summarising Data with Aggregation

    • Use aggregate functions to summarize raw rows into business metrics.
    • Explain how grouping changes the grain of a result.
    • Differentiate row-level querying from grouped analysis.
  8. 8

    Filtering Aggregated Results

    • Apply group-level conditions after aggregation.
    • Distinguish clearly between pre-aggregation and post-aggregation filtering.
    • Use grouped filtering to answer threshold-based business questions.
  9. 9

    Using Conditional Logic in Analysis

    • Use conditional expressions to categorize and label data.
    • Translate business rules into simple SQL logic.
    • Recognize how conditional logic supports reporting and segmentation.
  10. 10

    Subqueries as Building Blocks

    • Describe what a subquery is and why analysts use one.
    • Recognize common patterns where a query depends on the result of another query.
    • Compare direct querying with nested-query approaches at a high level.
  11. 11

    Organising Multi-Step Logic with CTEs

    • Explain what a CTE is and how it improves query structure.
    • Break a larger analytical task into named steps.
    • Recognize CTEs as a readability and maintenance tool for analysts.
  12. 12

    Analytical Row-by-Row Comparison with Window Functions

    • Describe what window functions do at a high level.
    • Differentiate grouped summaries from row-preserving analytical calculations.
    • Recognize common analyst uses such as ranking, running totals, and comparisons within groups.
  13. 13

    Answering Realistic Business Questions with SQL Patterns

    • Match common business questions to appropriate SQL techniques.
    • Recognize recurring analytical patterns across domains.
    • See how multiple SQL features combine in practical analysis.
  14. 14

    Checking Query Quality, Accuracy, and Readability

    • Develop habits for validating results and avoiding common analytical SQL mistakes.
    • Recognize signs of incorrect joins, wrong grain, or misleading filters.
    • Value readable, explainable SQL as part of professional analytical practice.

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