Data Science & AI Databases

Advanced SQL: Window Functions, CTEs, and Optimization

Write analytical SQL that is clearer, faster, and ready for real production data work.

Advanced SQL: Window Functions, CTEs, and Optimization logo
Quick Course Facts
19
Self-paced, Online, Lessons
19
Videos and/or Narrated Presentations
6.9
Approximate Hours of Course Media
About the Advanced SQL: Window Functions, CTEs, and Optimization Course

Advanced SQL: Window Functions, CTEs, and Optimization is a practical course for analysts, data professionals, and developers who want to move beyond basic queries and work confidently with complex Data problems. You will learn how to write analytical SQL that is clearer, faster, and ready for real production data work.

Build Faster Analytical SQL For Real Data Work

  • Apply window functions to ranking, rolling metrics, period-over-period analysis, cohorts, funnels, and retention reporting.
  • Use CTEs to break complex business logic into readable, maintainable query layers.
  • Read execution plans, choose indexing strategies, and optimize joins, filters, and aggregations with less guesswork.
  • Complete an applied capstone project where you build and tune an analytical reporting query from start to finish.

Learn advanced SQL techniques for analyzing Data with window functions, CTEs, recursive queries, and performance optimization.

This course starts by strengthening your advanced querying mindset, including how SQL processes queries and how to design reliable analytical results. From there, you will work deeply with window functions, learning how PARTITION BY, ORDER BY, frames, ranking functions, LAG, LEAD, running totals, moving averages, and rolling metrics help answer real Data questions without messy workarounds.

You will also learn advanced analytical patterns used in production reporting, including gaps and islands, sessionization, cohort analysis, retention analysis, and funnel analysis. These lessons help you translate business questions into SQL that is easier to explain, test, and maintain.

The course then focuses on Common Table Expressions, showing how CTEs can improve clarity, decompose complex logic, support recursive hierarchy and path queries, and sometimes affect performance. You will learn when CTEs make SQL easier to manage and when they may create optimization tradeoffs.

In the optimization section, you will practice reading execution plans, improving indexing strategies, and refactoring slow queries into maintainable SQL. By the end of Advanced SQL: Window Functions, CTEs, and Optimization, you will be able to approach complex Data work with more structure, produce clearer analytical SQL, and tune queries for practical production use.

Course Lessons

Full lesson breakdown

Lessons are organized by topic area and each includes descriptive copy for search visibility and student clarity.

Foundations for Advanced Querying

2 lessons

This lesson establishes the mindset needed for advanced SQL: thinking in terms of relational transformations, logical query processing, and readable intent rather than line-by-line execution. Before u…
Reliable analytical SQL is not only about getting an answer. It is about getting the right answer consistently as data changes, requirements evolve, and queries move from notebooks into production wor…

Window Functions in Practice

6 lessons

In this lesson, Professor Peter Lambert introduces the practical foundation of SQL window functions: how they calculate values across related rows while preserving row-level detail. You will learn the…
This lesson teaches how PARTITION BY and ORDER BY define the analytical context for SQL window functions. Learners will see how partitions create independent groups, how ordering makes row position me…
Ranking functions turn ordered rows into analytical positions without collapsing the result set. In this lesson, you will use ROW_NUMBER , RANK , and DENSE_RANK to solve common production problems suc…
In this lesson, students learn how to build running totals, moving averages, and rolling metrics with SQL window functions. The focus is on practical analytical patterns that appear in revenue reporti…
This lesson teaches how to use LAG and LEAD to compare each row with a previous or next row inside a window. Students learn how to define the right partition and order, calculate period-over-period di…
This lesson explains how window frames control which rows are visible to a window aggregate for each current row. Learners will distinguish ROWS from RANGE , choose appropriate boundaries such as UNBO…

Advanced Analytical Patterns

2 lessons

In this lesson, students learn how to solve three common advanced analytical SQL patterns: gaps, islands, and sessionization. The focus is on identifying breaks in ordered event data, assigning stable…
This lesson shows how to build practical cohort, retention, and funnel analysis in SQL using CTEs, date logic, conditional aggregation, and window functions. You will learn how to define cohorts, meas…

Common Table Expressions

4 lessons

This lesson teaches how to use common table expressions, or CTEs, to make advanced SQL easier to read, test, and maintain. Students learn to break a complex query into named logical steps instead of f…
This lesson teaches how to use layered Common Table Expressions to express complex business logic in clear, testable stages. Students learn how to break a dense analytical query into named steps such …
Recursive common table expressions let SQL walk data that points back to itself: org charts, category trees, bill-of-materials structures, referral chains, folder paths, and graph-like relationships. …
This lesson explains when common table expressions make SQL faster, when they make it slower, and why the answer depends on the database engine and query shape. Learners will examine CTE inlining, mat…

Optimization and Performance

4 lessons

Execution plans are the database optimizer's explanation of how it intends to answer a query. In this lesson, Professor Peter Lambert teaches a practical way to read plans without guessing: start from…
Indexing is one of the main reasons an advanced SQL query either feels production-ready or falls apart under real data volume. In this lesson, Professor Peter Lambert explains how indexes support join…
This lesson focuses on practical query optimization for the three places SQL workloads often spend the most time: joins, filters, and aggregations. Students learn how to reason from execution plans, r…
Slow SQL is often hard to fix because performance problems are mixed together with readability problems. This lesson teaches a practical refactoring workflow: preserve the result, isolate expensive wo…

Applied SQL Project

1 lesson

In this capstone lesson, learners build a production-style analytical reporting query from raw transactional tables into a clear, tuned, stakeholder-ready result set. The project combines CTEs, joins,…

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About Your Instructor
Professor Peter Lambert

Professor Peter Lambert

Professor Peter Lambert guides this AI-built Virversity course with a clear, practical teaching style.