Business analysis query set
Deliverable: Twelve queries answering real business questions against a large sample database, each with a written note on the row-count check performed and why the join was structured that way.
Course
SQL and Databases
SQL taught against a database big enough that a bad query is noticeably slow, because toy datasets hide performance entirely and performance is half of what the job tests.
- Duration: 3 months
- Classroom · Online live
- Level: beginner to advanced
What you will be able to do
- Write joins, aggregations, subqueries and CTEs against a normalised schema
- Use window functions for ranking, running totals and period comparisons
- Diagnose why a query returns the wrong row count and fix the cause
- Design a normalised schema and justify each table and key
- Read an execution plan and identify the missing index
- Use transactions correctly and reason about isolation
Who this course is for
- Graduates from any stream, including commerce and arts backgrounds
- MIS executives, accounts staff and analysts working beyond Excel's limits
- Developers and testers who need to query and verify data properly
- No programming experience required
Syllabus
8 modules · 3 months
Module 1. Relational foundations
- Tables, rows, keys and what a relation actually means
- Data types and choosing them deliberately
- Normalisation to third normal form, with worked examples
- Primary keys, foreign keys and referential integrity
- NULL, and why it behaves unlike any other value
Module 2. Querying
- SELECT, WHERE and filtering precisely
- ORDER BY, LIMIT and pagination
- GROUP BY, HAVING and the difference from WHERE
- Aggregate functions and how each treats NULL
- String, date and conditional expressions
Module 3. Joins in depth
- INNER, LEFT, RIGHT and FULL joins
- Tracking row counts as the diagnostic habit
- Fan-out, and aggregating the many-side before joining
- Self joins and hierarchical data
- Anti-joins for finding orphaned records
Module 4. Subqueries and CTEs
- Scalar, row and table subqueries
- Correlated subqueries and their cost
- Common table expressions for readable multi-step logic
- Recursive CTEs for hierarchies
- EXISTS versus IN, and the NOT IN with NULL trap
Module 5. Window functions
- The window concept and how it differs from GROUP BY
- ROW_NUMBER, RANK and DENSE_RANK with ties
- Running totals and moving averages
- LAG and LEAD for period comparison
- PARTITION BY, frames and the default frame surprise
Module 6. Modifying data
- INSERT, UPDATE and DELETE safely
- Transactions, COMMIT and ROLLBACK
- Isolation levels and the anomalies each prevents
- Bulk loading and the pitfalls of large updates
Module 7. Performance
- Indexes, what they cost and when they help
- Reading an execution plan
- Why a query stops using an index
- Composite indexes and column order
- Query rewriting for performance
Module 8. Design and integration
- Schema design from a business requirement
- Views, stored procedures and when they earn their keep
- Connecting from Python and Excel
- Backups, restores and testing that a restore works
Tools and technologies you will use
- MySQL
- PostgreSQL
- MySQL Workbench and DBeaver
- EXPLAIN and query plans
- Sample databases of substantial size
Projects you will build
Cohort and retention analysis
Deliverable: A window-function analysis producing monthly cohort retention with running totals and period comparisons, plus an explanation of what the numbers do and do not establish.
Schema design from a brief
Deliverable: A normalised schema designed from a written business requirement, with an entity diagram, justification for each key, and sample queries proving it answers the required questions.
Query tuning exercise
Deliverable: Four deliberately slow queries diagnosed with execution plans, rewritten or indexed, with before-and-after timings and an explanation of what changed.
Where this course can take you
- Data Analyst
- SQL Developer
- Business Analyst
- MIS Analyst
- Database Administrator (junior)
Duration, modes and fees
- Duration
- 3 months
- Delivery modes
- Classroom · Online live
- Fees
- Share your details for the current fee
- Fees vary by batch and delivery mode. Share your details and an advisor will confirm the current fee.
Placement assistance
Every student gets placement assistance — that is what 100% placement assistance means. It is support for all, not a job for all. We do not promise a specific salary, a specific number of interviews, or placement at any named company, and you should be wary of anyone who does.
What is included
- A place in the monthly placement drive, held every third Saturday
- The readiness programme every second Saturday — mock interviews and preparation
- CV review against the specific roles you are targeting
- Portfolio review, so your project work is presented the way a reviewer will read it
- Access to the vacancy pool employers send directly to the Samyak network
- Guidance on which roles realistically fit your background and which do not
- A place in the next drive, with coaching, if you are not selected in this one
What is not included
- Any guarantee of a job, an interview, or a particular salary
- Placement at a named or partner company
- Applying to jobs on your behalf
- Support before you have completed the course and its project work
- Visa, relocation or overseas placement assistance
Why the practice database is deliberately large
Most SQL courses use a sample database with a few hundred rows. Every query returns instantly, which means an entire half of the subject — performance — never becomes visible.
Then the learner writes a query against a production table with four million rows and it runs for six minutes, and they have no framework for understanding why.
We use datasets large enough that a bad query is noticeably slow and a good one is noticeably fast. Indexes stop being a definition and become something you can feel. That feedback is what makes the performance module land rather than being abstract.
Row counts as a habit, not a topic
The most valuable thing in this course is a habit rather than a feature.
Before and after every join, check the row count. If it changed and you did not expect it to, stop and find out why before writing another line.
This single practice catches the majority of wrong SQL results in real work — the silent fan-out that inflates a total, the LEFT JOIN quietly converted to an INNER JOIN by a filter in the wrong clause. Neither raises an error. Both produce a plausible number that is wrong.
We drill it from the joins module onward, and it is what interviewers are listening for when they ask how you would check your own output.
Who this course is genuinely for
If you want to be an analyst and are starting from nothing, our data analytics course is the better fit — it covers SQL alongside Excel, Power BI and Python, which is the combination analyst job descriptions actually list.
This course is for people who need SQL deeper than that. Backend developers who own their schema. Analysts already working who keep hitting the limits of what they can express. MIS staff who have outgrown Excel and need to query the source directly.
For that group, three months here changes what you can do more than any other three-month course we run.
Questions
SQL and Databases — frequently asked questions
Is a separate SQL course worth it if data analytics already covers SQL?
It depends where you are going. Our data analytics course covers SQL well as one component alongside Excel, Power BI and Python. This course goes considerably deeper — recursive CTEs, isolation levels, execution plans and index design — which matters for SQL developer, backend and database roles rather than general analyst work.
Should I learn MySQL or PostgreSQL?
We teach both, because the standard SQL you write is largely identical and the differences appear at the edges. MySQL is more common in smaller Indian firms and legacy applications; PostgreSQL is increasingly the default in newer systems. Learning one properly means the other takes days.
How much SQL do interviews actually test?
For analyst roles, joins, aggregation and at least one window function, usually written live. For backend roles, add schema design and indexing. Query tuning appears from about the two-year mark. The final two modules exist because that is where candidates with two years of experience typically fall down.
Do I need SQL if I know Python and pandas?
Yes, in almost every case. Data usually lives in a database, and filtering and aggregating there rather than pulling everything into pandas is both faster and how teams expect it to be done. The two are complementary, and job postings for analyst roles overwhelmingly list SQL first.
Enquire about SQL and Databases
Three details is all we need. A course advisor will call you back.
Related courses
- Data Analytics
A structured path from spreadsheets to SQL, Power BI and Python, built around the four things a data analyst is actually paid to do — pull the data, clean it, analyse it and explain it.
- Power BI
Power BI taught as a data modelling tool rather than a chart menu — star schemas, relationships and DAX first, because that is what separates a report that scales from one that breaks.
- Python Programming
Python taught as a working tool rather than a syntax tour — you finish able to read unfamiliar code, automate real tasks, call APIs and write tests that catch your own mistakes.
Next step
Talk to a course advisor
Tell us what you want to learn and we will help you pick the right course, batch and mode.