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Beginner · Self-paced2026 Edition

SQL for Analysts

From SELECT to window functions. The complete grammar an analyst needs to query, shape, and trust real datasets.

12

Modules

~9h 20m

Reading time

Beginner

Level

Self-paced

Format

Hands-on practice environment

Real SQLite. 50 graded exercises. In your browser.

A live SQLite database seeded with a realistic Kenyan SACCO dataset (1,500 rows across 5 tables). Work through fifty exercises from SELECT to window functions and CTEs — every query you write executes against the same data, instantly.

Open the practice environment →View the schema
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Syllabus

  1. 01

    Why SQL, and how a database thinks

    The mental model: relations, rows, and why declarative beats imperative for data.

    ~30 minModule 01
    →
  2. 02

    SELECT, FROM, WHERE

    Pull rows out of a table with filters that match your question, not the other way around.

    ~40 minModule 02
    →
  3. 03

    ORDER BY, LIMIT, DISTINCT

    Slice, sort, and de-duplicate result sets — the everyday verbs.

    ~30 minModule 03
    →
  4. 04

    JOINs: INNER, LEFT, RIGHT, FULL

    Combine tables. The single concept that separates beginners from analysts.

    ~60 minModule 04
    →
  5. 05

    GROUP BY and aggregate functions

    Roll up to summaries — counts, sums, averages, min/max — with HAVING for post-aggregate filters.

    ~50 minModule 05
    →
  6. 06

    Subqueries and CTEs

    Compose queries from smaller queries. WITH clauses make complex analysis readable.

    ~50 minModule 06
    →
  7. 07

    Window functions

    Running totals, ranks, lag/lead, percentiles. Where SQL becomes truly analytical.

    ~60 minModule 07
    →
  8. 08

    CASE expressions, strings, and dates

    Conditional logic, text wrangling, and time arithmetic — the daily tools of cleaning real data.

    ~45 minModule 08
    →
  9. 09

    Tables, keys, and a tiny bit of normalization

    How to read a schema, why foreign keys matter, and when denormalization is a feature not a bug.

    ~35 minModule 09
    →
  10. 10

    Performance: indexes, EXPLAIN, and query smell

    Why a query is slow, how to read an execution plan, and the three patterns that fix 80% of issues.

    ~50 minModule 10
    →
  11. 11

    Mini-projects on a real dataset

    Three guided analyses on a public dataset — cohort retention, revenue trends, and an outlier hunt.

    ~90 minModule 11
    →
  12. 12

    Where to go next

    Postgres extensions, dbt, BI tools, and the SQL dialects you'll meet at work.

    ~20 minModule 12
    →

How to use this course

Start with module 01 if the material is new; skip ahead if you have prior exposure. Each module is self-contained but the arc is sequential — the projects in the final module assume the toolkit from modules 1-11. Every module ends with key takeaways and a curated further-reading list with primary sources.