SQL & Database Analytics
SQL is the most-requested single skill in Indian analytics and data job listings and one of the few that transfers unchanged across employers, tools and decades. This course goes from first query to window functions and query performance, worked entirely against realistic multi-table databases rather than single-table exercises.
Enrolment is through our partner colleges. You will need the code your college issued.
- Course
- DATA-004
- Topic
- Data & Analytics
- Modules
- 14
- Levels
- Foundation → Applied → Advanced
How this is shaped for your students
How far a group can go in SQL depends almost entirely on where it starts and how much time we have. Before confirming, we look at the degree and year, any prior database exposure in the syllabus, cohort size, the roles students want, what regional employers are hiring for, relocation intent, available hours and the placement calendar. That determines the level, the pace, and whether the group works towards an analyst brief or a technical interview.
Who this is for
- Strong fit
- BCA, B.Sc CS, B.Sc IT, CSE, IT, Data Science, MCA
- Also suitable
- B.Com, BBA, all engineering
- Before you start
- None
What the course covers
Foundation5 modules
- 01Relational thinkingtables, rows, columns, keys, relationships, normalisation as an idea rather than a ritual
- 02Querying one tableSELECT, WHERE, operators, NULL handling, ORDER BY, LIMIT, DISTINCT
- 03AggregationGROUP BY, aggregate functions, HAVING, the difference between WHERE and HAVING and why it is an interview question
- 04Joining tablesINNER, LEFT, RIGHT, FULL; the join that silently duplicates rows; joining on the wrong key
- 05Reading a schemaentity relationship diagrams, finding your way around an unfamiliar database, asking the right question of a data dictionary
Applied5 modules
- 01Subqueries and CTEsnested queries, WITH clauses, readability, breaking a hard problem into steps
- 02Window functionsROW_NUMBER, RANK, LAG and LEAD, running totals, partitioned aggregates; the single biggest step up in analyst SQL
- 03Data manipulationINSERT, UPDATE, DELETE, transactions, and the discipline of testing a destructive statement before running it
- 04Database designschema design for a real application, data types, constraints, indexes, when to denormalise
- 05Analytical patternscohort queries, funnel queries, retention, deduplication, time-series aggregation
Advanced4 modules
- 01Query performanceexecution plans, indexing strategy, why a query that worked on ten thousand rows dies on ten million
- 02Warehouse SQLcolumnar databases, partitioning, cloud warehouse dialects, cost-aware querying
- 03Views, procedures and pipelinesreusable logic, scheduled transformation, dbt as a concept, versioning SQL in Git
- 04AI-assisted SQL with verificationgenerating and reviewing queries, the plausible query that returns the wrong number, building a check into every generated query
Three levels, one continuous path
Foundation
You start from working vocabulary and first-hand contact with the tools. If you have never opened them before, you can start here. The work is guided, and you are assessed on knowing the material and applying it simply.
Applied
You turn knowledge into a way of working. You are given a brief with real constraints and incomplete information, you make decisions, and you explain why you made them. This is the level at which most students become useful on an internship.
Advanced
You handle the messy version: conflicting requirements, scale, cost, things going wrong, other people’s opinions, and knowing when not to do the obvious thing. The work is portfolio quality and you have to defend it.
Work you can show an employer
1Foundation
answer fifteen business questions against a supplied six-table database.
2Applied
design and populate a schema for a small application and query it analytically.
3Advanced
optimise a deliberately slow query set and document the before-and-after with execution plans.
Capstone
Analytical Database from Scratch. Design the schema, load real data, build the analytical query layer, produce a set of reusable views answering defined business questions, document the model, and defend the design choices — including the normalisation decisions — in a viva.
Tools you will use
Essential
- MySQL or PostgreSQL
- a SQL client (DBeaver or pgAdmin)
Recommended
- SQLite
- Google BigQuery sandbox
- Git
Advanced
- dbt fundamentals
- Snowflake or BigQuery
- an AI SQL assistant used under verification discipline
Jobs this points at
Roles this course is relevant to
Data Analyst · SQL Developer · Database Associate · Reporting Analyst · Junior Data Engineer · BI Developer.
What you can do when you finish
- Write multi-table queries with joins, CTEs and window functions
- design a normalised schema with appropriate constraints and indexes
- read an execution plan and improve a slow query
- build analytical queries for cohort, funnel and retention questions
- verify an AI-generated query against a known result
Length and delivery
| Option | Typically used for | What it covers |
|---|---|---|
| 30 hours | Value Added Course, or a single-semester elective | All of Foundation |
| 45 hours | Skill Enhancement Course | Foundation, plus selected Applied modules |
| 60 hours | Standard cohort | Foundation, Applied, and the Applied project |
| 90 hours | Two semesters, or intensive | All three levels, plus the Advanced project |
| 120 hours | Full pathway | All three levels, capstone, and placement preparation |
The final option is agreed with your college during programme design.
Bring this course to your college
Enrolment is through our partner colleges. You will need the code your college issued.