Advanced SQL Analytics
Develop the skills to produce a reproducible analytical query portfolio through a learning path centred on Advanced SQL Analytics. Explore concepts, guided practice and an applied project.
Build practical Advanced SQL Analytics skills.
Skills and knowledge developed in this course.
- Analytical query patterns
- Window functions
- Time and cohort analysis
- Query correctness
- Applied project: Produce a reproducible analytical query portfolio
- Advanced SQL Analytics — validation and review
About this course
This course develops practical capability in Advanced SQL Analytics. Its course-specific content covers Analytical query patterns, Window functions, Time and cohort analysis, Query correctness, followed by an applied project focused on produce a reproducible analytical query portfolio. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with Advanced SQL Analytics or plan to take on responsibilities in this area.
Before you start
Working knowledge of data engineering & analytics and practical familiarity with the named platform or subject. Review the course topics before enrolment to confirm the lab starting point.
Produce a reproducible analytical query portfolio
Deliver produce a reproducible analytical query portfolio. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Analytical query patterns
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- Common table expressions and conditional aggregation
- Subqueries and set operations
Practical exerciseApply analytical query patterns to the produce a reproducible analytical query portfolio and capture the validation evidence.
02Window functions
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- Partitioning, ordering and frames
- Ranking, running totals and lag-lead comparisons
Practical exerciseApply window functions to the produce a reproducible analytical query portfolio and capture the validation evidence.
03Time and cohort analysis
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- Date scaffolds and retention cohorts
- Sessionisation, gaps and rolling metrics
Practical exerciseApply time and cohort analysis to the produce a reproducible analytical query portfolio and capture the validation evidence.
04Query correctness
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- Join multiplicity and NULL semantics
- Execution plans and result reconciliation
Practical exerciseApply query correctness to the produce a reproducible analytical query portfolio and capture the validation evidence.
05Applied project: Produce a reproducible analytical query portfolio
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- Define inputs, assumptions and acceptance criteria for: Produce a reproducible analytical query portfolio.
- Implement the project using analytical query patterns, window functions, time and cohort analysis, query correctness.
Practical exerciseProduce a reproducible analytical query portfolio.
06Advanced SQL Analytics — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Analytical query patterns · Window functions
Establish the Advanced SQL Analytics scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Time and cohort analysis · Query correctness
Apply the core Advanced SQL Analytics techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for produce a reproducible analytical query portfolio. - 04Learning milestone 4 In progress
Deliver the applied project
Complete produce a reproducible analytical query portfolio, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| Data Engineer | ₹8–15 LPA | ₹17–28 LPA | ₹29–50 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗