The theory and the practice together. ER modelling and normalisation for your university paper, and real SQL against real schemas for the job.
What you will be able to do
At a glance
Full syllabus
10 modules · 50 topics · 185 concepts
Why databases exist
The relational model
Keys
Constraints
Types of databases
You can explain the relational model and read an existing schema.
ER modelling
Functional dependency
Normalisation
Denormalisation
Data types
You can model a real domain and normalise it to third normal form.
SELECT fundamentals
Filtering
Sorting and limiting
Logical query order
Built-in functions
You can write a multi-clause query and predict its result set.
Join types
Join mechanics
Self joins
Set operations
Multi-table queries
You can join several tables correctly and diagnose a wrong row count.
Aggregate functions
GROUP BY
HAVING
Pivoting
Grain
You can aggregate at the right grain and know when to use HAVING.
Scalar and row subqueries
IN, EXISTS and ANY
Derived tables
Common table expressions
Recursive CTEs
You can break a complex question into readable steps with CTEs.
The OVER clause
Ranking
Offset functions
Aggregate windows
Practical patterns
You can rank, compare to a previous row, and compute a running total.
ACID
Transaction control
Concurrency problems
Isolation levels
Locking
You can reason about isolation levels and write a safe multi-step update.
How indexes work
Creating indexes
Reading a plan
Query anti-patterns
Practical tuning
You can read an execution plan and add an index that actually helps.
Views
Stored procedures and functions
Triggers
Users and permissions
Backup and recovery
You can create views and stored routines, and manage access safely.
5 builds you keep
Model a ticketing system where two people can try for the last seat at once. Normalise it, then make the hold-and-confirm flow correct with a transaction and the right isolation level.
You are given a slow reporting query and a million rows. Read the plan, find the full table scan, add the right composite index, and prove the improvement with timings before and after.
Group users by signup month and track who came back in each following month, using window functions. The report every subscription business runs, and a genuine test of grain.
Record who changed what and when, without the application having to remember to do it. Includes the awkward question of what to store when a row is deleted.
Given a database and no documentation, work out what it models, find the keys, draw the ER diagram, and answer five business questions from it. Exactly what the first week of a data job looks like.
Hands on with
Before you enrol
Both, and on purpose. ER modelling, normalisation, functional dependency and transactions are the university paper. Joins, window functions, indexing and query plans are what the job asks for. They are taught together against the same database.
We teach standard SQL and use both, pointing out where they differ. Most Mumbai employers use MySQL; PostgreSQL turns up more in newer product companies. The transferable part is the same.
No. SQL is a declarative language and does not assume any prior programming. It is often the first technical skill non-engineering graduates pick up successfully.
Joins, aggregation and window functions cover most of the working day, and those get a module each here. Indexing and execution plans are what separate an analyst who can be trusted with a production database from one who cannot.
Yes. Most projects need a schema that survives review, and the design module is the part students most often get marked down on. You will leave with a normalised model and the ER diagram to defend it.
Where this leads
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