Analytical SQL · Hands-on database labs
SQL Training in Bangalore — Window Functions, CTEs & AI Code Assist
Learn to turn a business question into a correct query, explain why it works and test the result before it reaches a report or dashboard.
- Duration
- 20 live hours
- Core skills
- Queries, joins, CTEs and windows
- Practice
- Business questions on a relational database
- Platforms
- Dialect agreed before delivery
What is the SQL Training course?
SQL Training is a 20-hour instructor-led course for analysts, reporting professionals and technical teams who need to retrieve and analyse relational data. Participants progress from SELECT, filtering and grouping to joins, subqueries, CTEs and window functions, then apply performance and validation checks. AI code assistants may be used to draft or explain SQL, but every generated query is read and tested by the participant.
Is this the right course for you or your team?
Who it is for
- Aspiring and working data analysts
- MIS, finance and operations professionals moving beyond exports
- BI developers who need stronger query skills
- Application and support teams who read operational databases
Situations it fits
- Every data question becomes a request to another team
- JOINs create duplicated totals
- Long queries are hard to explain or maintain
- Analysts use AI-generated SQL without testing it
Prerequisites
- No programming experience required for the foundation route
- Comfort with rows, columns and basic business reporting
- A laptop and agreed database client
- Advanced corporate routes may require prior SELECT and JOIN familiarity
The business problems this course addresses
SQL quality is not measured by whether a query runs—it is measured by whether it answers the right question with the right rows.
| Task | The usual problem | What participants practise |
|---|---|---|
| Pulling a report | Filters and date boundaries do not match the business definition. | Translate the question into explicit population, period and measure rules. |
| Joining tables | One-to-many relationships multiply revenue or headcount. | State the grain of each table and reconcile counts before and after the join. |
| Comparing periods | Repeated self-joins make the query hard to maintain. | Use window functions for ranking, prior-period values and running calculations. |
| Using AI code assist | A polished query can target the wrong dialect or invent a column. | Provide schema context, inspect every clause and test against a known case. |
What participants will be able to do
Practise the skills below through course exercises, then check the results and explain how you would apply them to your work.
Translate questions into queries
Define the population, grain, filters and measures before writing SQL.
Combine tables safely
Use appropriate joins and reconciliation checks without silently multiplying rows.
Summarise business data
Apply grouping, conditional aggregation and date logic to reporting questions.
Structure complex logic
Use subqueries and CTEs to make multi-step analysis readable and testable.
Use window functions
Build rankings, running totals, previous-period comparisons and within-group analysis.
Review assisted SQL
Use an AI assistant for drafting or explanation while verifying schema, dialect, logic and output.
Applied to each team’s real work
Business and data analysts
Customer, product, revenue, retention and cohort questions.
Finance and MIS
Period comparisons, reconciliations, ageing, targets and management reporting.
Operations and supply chain
Service levels, throughput, inventory, delays and exception analysis.
BI and application teams
Dataset preparation, dashboard queries, debugging and query review.
Learn the SQL concepts, then apply the right dialect
The core analytical patterns transfer across databases. Syntax and performance details are aligned to the selected platform.
PostgreSQL
by PostgreSQL Global Development GroupDefault standards-oriented environment for public practice when specified for the cohort.
- Query fundamentals
- CTEs and window functions
- Explain-plan discussion
- Used in
- Guided labs on a relational business dataset.
- What to verify
- Functions and date syntax can differ from the database used at work.
MySQL
by OracleSelected when it matches the delivery environment
Alternative practice platform or client-specific corporate dialect.
- Relational querying
- Aggregations and joins
- Platform comparison
- Used in
- Used when the cohort or client environment is based on MySQL.
- What to verify
- Confirm version and SQL mode before copying patterns between systems.
AI code assistant
by Client-approved providerOptional — provider agreed before delivery
Optional assistance for drafting, explaining and debugging a query.
- Turning a stated question into a first draft
- Explaining unfamiliar syntax
- Generating test cases
- Used in
- The reviewed AI-assisted SQL lab.
- What to verify
- Do not paste confidential schema or data into an unapproved service; generated SQL must be tested.
20 hours · 20 live hours
Detailed curriculum
4 modules, each pairing short instruction with guided practice. Sequence and examples can be adjusted after the training-needs discussion; the core outcomes stay the same.
Session 14 hours Query foundations
- Tables, primary and foreign keys; SELECT, aliases and expressions
- AND, OR, IN, BETWEEN, LIKE and IS NULL
- ORDER BY, DISTINCT and database-specific LIMIT, TOP or FETCH syntax
- Dates, text and conditional logic
Practical activityAnswer a set of progressively refined reporting questions.
Session 25 hours Aggregations and joins
- COUNT, SUM, AVG, MIN and MAX; GROUP BY and HAVING
- INNER, LEFT, RIGHT and FULL OUTER joins where supported
- Grain, keys and reconciliation across three or more tables
- UNION versus UNION ALL and duplicate handling
Practical activityBuild a multi-table sales report without duplicating totals.
Session 36 hours Subqueries, CTEs and window functions
- Scalar and correlated subqueries
- Readable multi-step CTEs
- ROW_NUMBER, RANK and DENSE_RANK; choosing a tie-handling rule
- LAG and LEAD for period comparisons
- SUM OVER and AVG OVER; partitions, ordering and window frames
Practical activitySolve ranking, month-on-month and cohort-style analytical questions.
Session 45 hours Quality, performance and AI assistance
- Indexes and explain-plan fundamentals
- Testing edge cases and reconciling results
- Reviewing SELECT * and implicit conversions in their query context
- Views, stored procedures, permissions and schema governance concepts
- Prompting for SQL without exposing sensitive data
Practical activityReview, repair and explain an AI-generated analytical query.
Need a learning plan around your goals?
Share your experience, stack and goals. The proposal sets out the suitable modules.
SQL Use Cases by Industry
These industry examples connect SQL skills to practical reporting questions. Select exercises that match your team’s database and starting skills. Use synthetic or approved, de-identified data for training; regulated reports and clinical decisions need specialist validation.
🏦BFSI & Fintech
- Transaction analysis and fraud pattern detection
- Customer portfolio and lifetime value queries
- Loan book performance and ageing bucket reports
- Regulatory reporting from core banking data
E-commerce & Retail
- Order, revenue and returns analysis by SKU and category
- Customer cohort and RFM segmentation queries
- Inventory levels and stockout frequency reporting
- Marketing attribution and channel performance SQL
IT Services & SaaS
- Product usage analytics from application event logs
- ARR, churn and expansion revenue reporting
- Support ticket volume and resolution time analysis
- Sales pipeline SQL on CRM data
Manufacturing
- Production output vs target by line and shift
- Quality defect rate queries by batch and supplier
- Inventory turns and reorder point calculations
- Downtime and OEE reporting from SCADA data
Healthcare & Pharma
- Patient journey and readmission cohort analysis
- Prescription analytics by territory and molecule
- Clinical trial data querying and adverse event reporting
- Hospital operations: OPD volumes and wait times
Operations & Logistics
- Delivery performance and SLA compliance queries
- Carrier cost and on-time rate analysis
- Warehouse utilisation and throughput reporting
- Demand vs supply gap analysis by SKU and location
SQL or Python: which should a data analyst learn first?
If your immediate task is to retrieve, join and aggregate data in a relational database, SQL is a practical starting point. If the task centres on statistical modelling, custom automation or machine learning, Python may be the earlier priority. Many workflows use both: SQL prepares a query result and Python performs subsequent analysis.
SQL does not require prior Python knowledge, but it still requires practice with table grain, joins, missing values and validation. Python involves programming concepts and an execution environment. Neither language is automatically faster: performance depends on the engine, query plan, data movement and implementation.
For a broader pathway, compare AI Data Analytics training and AI Programming with Python against your immediate work. Choose SQL first when database reporting is the gap, rather than treating the sequence as a rule for everyone.
How SQL skills map to analytical roles
The original role pathways include Data Analyst, Business Intelligence Analyst, MIS Executive or Manager, SQL Developer, Data Engineer, Reporting Analyst and Analytics Consultant. SQL is one part of those roles, not a placement guarantee or a substitute for their other requirements.
For reporting work, demonstrate a reconciled multi-table report. For advanced analysis, explain a CTE and a window function, including how ties, missing periods and duplicate rows affect the result. Developer and engineering roles also require broader database, application or pipeline skills; show how you investigate an execution plan rather than claiming every query needs an index.
Exercises and practical outputs
Each lab starts with a business question, not a finished query.
How every module runs
Define
State grain, population, period and expected output.
Query
Build the result in small, inspectable steps.
Reconcile
Check row counts and totals against known cases.
Explain
Describe the query so another analyst can review it.
Exercises use representative, non-sensitive examples. Outputs are produced during the workshop for practice and review; anything participants keep is their own working material.
A correct-looking report can still be wrong
Every course treats data quality, calculation review and access control as part of the analytical workflow—not as an optional final check.
Check the current product documentation and your organisation’s data-handling rules before applying these workflows.
Use approved data
Use public, synthetic, sanitised or organisation-approved datasets. Remove personal and confidential fields before using external AI services.
Trace every number
Reconcile row counts, totals, filters, joins and time periods against the source before a report supports a decision.
Review generated logic
Read formulas, DAX, SQL, calculated fields and Python generated with AI. Test boundary cases instead of trusting fluent output.
Publish with controls
Confirm permissions, refresh ownership, data classification and reviewer responsibility before sharing a workbook or dashboard.
How the course is tailored and delivered
Private programmes are adapted around the reports, datasets, tool versions and decisions participants handle at work. Public cohorts use neutral practice data so people from different organisations can work safely.
From first conversation to evaluation
Map the work
Identify participant roles, current skill level, recurring reports and the decisions the training should support.
Confirm the environment
Check software versions, licences, database access, security rules and whether client data may be used in exercises.
Shape the labs
Choose representative datasets and outputs, then set the depth of formulas, modelling, visualisation or code.
Practise and review
Participants build during the session, explain their choices and use a review checklist before presenting the result.
Delivery formats
Public live-online cohort
Best for: Individuals and small teams following a published cohort schedule
Instructor-led sessions run on Zoom with notes, assignments, lab instructions and recordings in the Technovids LMS. Recording access lasts three months after the cohort.
Private corporate programme
Best for: Teams that want their own datasets, reporting standards and delivery calendar
Delivered live online or onsite. Scope, participant count, exercises, materials, support and commercials are confirmed in the written proposal.
Delivery modes
Live online
Interactive instructor-led sessions with screen sharing, guided practice and review. Public cohorts are hosted through Zoom and the Technovids LMS.
Onsite for corporate teams
Delivered at the client location with the same practical curriculum, using approved or sanitised organisational data where appropriate.
Practical training, with people doing the work
A selection of Technovids instructor-led sessions. The photographs show delivery environments, not results attributed to a particular course or client.
Photo 1 of 10: Hands-on session 
Photo 2 of 10: Group photo 
Photo 3 of 10: Hands-on session 
Photo 4 of 10: Group photo 
Photo 5 of 10: Classroom session 
Photo 6 of 10: Group photo 
Photo 7 of 10: Computer-lab session 
Photo 8 of 10: Group photo 
Photo 9 of 10: Classroom session 
Photo 10 of 10: Group photo
Course details at a glance
The published course parameters on one page. Final scope, dates and fees are confirmed in the written proposal.
Discuss your training goals| Course | SQL Training |
|---|---|
| Duration | 20 live hours, with depth adjusted to starting level and database platform |
| Audience | Analysts, MIS, finance, operations, BI and application teams |
| Prerequisite | No programming required for the foundation route |
| Platforms | PostgreSQL, MySQL, SQL Server, Snowflake or BigQuery as agreed |
| Delivery | Public live online; private live online or onsite |
| Assessment | Query labs, reconciliation tasks and final analytical challenge |
The trainer assigned to your programme
Technovids assigns a trainer whose technical depth and delivery experience fit the agreed curriculum and audience.
Pankaj Rana
Co-founder & Lead Instructor, Technovids Consulting
Pankaj is a co-founder and the lead instructor at Technovids Consulting. His programme work spans practical AI adoption, AI engineering, evaluation, responsible production AI, data analytics and corporate learning design, with an emphasis on hands-on exercises connected to workplace tasks.
Expertise:
- Practical AI adoption
- Prompting and evaluation
- Responsible AI
- Corporate learning design
Junaid Khateeb
AI & Machine Learning Trainer, Strategist and Consultant
Junaid is an AI and machine-learning trainer, strategist and consultant with more than 22 years of training experience. His work combines enterprise AI education, business-centred adoption, responsible AI practices and applied solution design, helping professional audiences connect AI concepts to real organisational needs.
M.E. in Computer Engineering, specialising in Artificial Intelligence · author of programming books on Python, Java and C++
Expertise:
- Enterprise AI and ML training
- AI strategy and adoption
- Responsible AI
- Applied AI solution design
Pradeep Varadarajan
Technical Trainer & Curriculum Architect
Pradeep is a technical trainer and curriculum architect with more than 20 years of experience across enterprise technology, cloud infrastructure and advanced AI systems. He designs practitioner-focused programmes, labs and assessments covering AI, LLM systems, Python and machine learning, translating complex technical subjects into structured learning experiences.
Bachelor of Engineering in Electrical Engineering · instructor-led, virtual and blended delivery
Expertise:
- AI and LLM systems
- Curriculum architecture
- Python and machine learning
- Hands-on lab design
The final trainer assignment is confirmed with the cohort schedule or corporate proposal.
Updated
Organisations we have trained, and participant perspectives
Client relationships and permissioned participant feedback provide context on Technovids delivery. They are not promises of a specific learning or business outcome.
Organisations we have delivered training for
Practical training programmes delivered for learners, professionals and teams across AI, data, cloud, productivity and professional skills.
Participant experiences across workplace roles
What professionals found useful, practical and relevant to their day-to-day work.
- Gauraw DeshmukhData Engineer · Capgemini
Technovids AI training was very practical and well structured. The sessions helped me understand how AI concepts can be applied in real data engineering and business workflows, instead of only learning theory. The trainer explained the topics clearly with relevant examples, which made the learning experience valuable and easy to follow.
- PriyanshaSoftware Developer
The AI training from Technovids helped me gain a better understanding of Generative AI, prompt engineering, and practical AI use cases for software development. The course was clear, interactive, and focused on real-world applications. It gave me confidence to explore AI tools more effectively in my work.
- Sidda RajuData Engineer
Technovids delivered the AI training in a very practical and industry-relevant way. As a data engineer, I found the examples and hands-on discussions useful for understanding how AI can improve data workflows, automation, and decision-making. The sessions were well planned and easy to understand.
- Akshat KabraData Engineer
The AI training program by Technovids was insightful and professionally delivered. It covered important AI concepts in a simple and practical manner, with examples that connected well with data and analytics use cases. The sessions helped me build a stronger foundation in applying AI in real business scenarios.
- MangeshSoftware Engineer · Siemens
Technovids conducted the AI training in a very effective and engaging manner. The sessions were practical, easy to follow, and focused on how AI can be used in real software engineering and business workflows. It was a valuable program for understanding the current direction of AI adoption in the industry.
- ReshuSoftware Developer
Technovids AI training was a very useful learning experience. The course explained AI concepts in a practical way and showed how developers can use AI tools to improve productivity, problem-solving, and application development. The trainer's approach was simple, clear, and focused on industry use cases.
Common questions before planning this course
Answers about the published course. The written proposal records any agreed customisation, delivery details and commercials.
Are public cohorts available for SQL Training?
Public cohorts are offered live online when dates are published. Submit an enquiry for the next available schedule. A date is not reserved until Technovids confirms the cohort in writing.
What is included for a public-cohort learner?
The Technovids LMS includes session notes, assignments, lab instructions and recordings for three months after the cohort. Completion requirements and the certificate process are explained at the start.
Is a completion certificate provided?
Yes. Learners who meet the stated completion requirements receive a Technovids completion certificate with a unique certificate ID that can be verified on the Technovids website. It is not a vendor certification.
Who provides the laptop, software and licences?
Participants use their own suitable laptop. The required software version, accounts and licences are confirmed before delivery. For private programmes, a sandbox or virtual machine can be scoped separately when needed.
Can the course be delivered for a corporate team?
Yes. The private version can use your approved datasets, reporting standards and business scenarios. Delivery may be live online or onsite, with scope and commercials confirmed in the proposal.
Which SQL database is used?
The platform is stated with the cohort schedule or corporate proposal. Core query concepts transfer, but syntax and performance details are taught against the selected dialect.
Do I need programming experience?
No for the foundation route. You should be comfortable thinking about tables and business data. The advanced route assumes familiarity with SELECT, filtering and joins.
Will AI write the SQL for me?
AI can help create a first draft or explain a query, but it cannot know your schema, business definitions or access rules unless they are supplied. The course teaches participants to inspect and test generated SQL rather than copy it blindly.
Plan your next step with this course
Tell us whether you are enquiring for yourself or a team, which development tasks matter and what access you have. We will confirm suitable delivery options and commercial details in writing.