Hands-on courses that teach you how to get real results from AI. You’ll complete projects that translate directly to your job and leave with prompts, workflows, and agents you can bring to work with you.
Go from asking AI questions to automating real work.

Build your AI fluency and get more done, faster.
Choose a track of courses tailored to your role.
Get AI to draft, fix, and harden real queries, and catch the query that runs clean but answers the wrong question.
Shape cohorts and funnels, diagnose anomalies without shipping a false cause, and write findings execs act on, keeping AI honest throughout.
Use AI to turn raw analyses into stakeholder-ready writeups, charts, dashboards, and exec narratives.
Learn the SQL every analyst uses every day, hands-on against a real in-browser database you query live.
The sequel to Introduction to SQL: conditional logic, every join type, set operations, subqueries, and CTEs, against the same live in-browser database.
Go beyond SELECT: type conversion, window functions, views, writes, transactions, table design, constraints, indexes, and triggers, against the same live database.
Practical Python for analytics and AI work, from your first variable to pandas and an LLM API call.
Modules, classes, async, testing, and the patterns that show up in real AI engineering code.
Performance, concurrency, packaging, and production-grade Python that holds up under real load.
Search by job, level, or time commitment — or whatever you need to do at work this week.
Understand how AI really works, where it fails, and how to use it with judgment instead of guesswork.
Turn vague, hit-or-miss prompts into clear briefs that get you exactly what you need.
Give AI persistent context for related work without rebuilding the setup in every chat.
Turn one bounded job into an AI tool a teammate can use without you beside them.
Build an AI agent for repeatable work, then give it clear actions, limits, and approval points.
Redesign the work that fills your week so AI does the heavy lifting and you keep the judgment.
Get AI to draft, fix, and harden real queries, and catch the query that runs clean but answers the wrong question.
Shape cohorts and funnels, diagnose anomalies without shipping a false cause, and write findings execs act on, keeping AI honest throughout.
Use AI to turn raw analyses into stakeholder-ready writeups, charts, dashboards, and exec narratives.
Learn the SQL every analyst uses every day, hands-on against a real in-browser database you query live.
The sequel to Introduction to SQL: conditional logic, every join type, set operations, subqueries, and CTEs, against the same live in-browser database.
Go beyond SELECT: type conversion, window functions, views, writes, transactions, table design, constraints, indexes, and triggers, against the same live database.
Practical Python for analytics and AI work, from your first variable to pandas and an LLM API call.
Modules, classes, async, testing, and the patterns that show up in real AI engineering code.
Performance, concurrency, packaging, and production-grade Python that holds up under real load.
Your first AI Fundamentals lesson is free. Check it out.
Hands-on courses that teach you how to get real results from AI. You’ll complete projects that translate directly to your job and leave with prompts, workflows, and agents you can bring to work with you.
Go from asking AI questions to automating real work.

Build your AI fluency and get more done, faster.
Choose a track of courses tailored to your role.
Get AI to draft, fix, and harden real queries, and catch the query that runs clean but answers the wrong question.
Shape cohorts and funnels, diagnose anomalies without shipping a false cause, and write findings execs act on, keeping AI honest throughout.
Use AI to turn raw analyses into stakeholder-ready writeups, charts, dashboards, and exec narratives.
Learn the SQL every analyst uses every day, hands-on against a real in-browser database you query live.
The sequel to Introduction to SQL: conditional logic, every join type, set operations, subqueries, and CTEs, against the same live in-browser database.
Go beyond SELECT: type conversion, window functions, views, writes, transactions, table design, constraints, indexes, and triggers, against the same live database.
Practical Python for analytics and AI work, from your first variable to pandas and an LLM API call.
Modules, classes, async, testing, and the patterns that show up in real AI engineering code.
Performance, concurrency, packaging, and production-grade Python that holds up under real load.
Search by job, level, or time commitment — or whatever you need to do at work this week.
Lead high-stakes strategic initiatives for a professional sports franchise.
Learn sports business analytics through hands-on assignments with the Seattle Cascade NBA team
This course teaches advanced database techniques focused on basketball analytics, covering complex joins, subqueries, and CTEs using real player and team data. Students learn to build sophisticated queries for ranking players, analyzing team performance, tracking player development, and calculating advanced basketball metrics like clutch performance and efficiency comparisons.
This course provides foundational skills in Structured Query Language (SQL). Students learn database design, querying, and manipulation, gaining hands-on experience in data retrieval and analysis. Ideal for beginners, this course prepares learners for roles in data analysis, database management, and software development.
This is a course focused exclusively on basketball data analysis using sophisticated database techniques including window functions for player rankings, views for performance dashboards, triggers for automated stat tracking, and advanced table design with proper constraints and indexes. Students master data type conversion for handling diverse basketball metrics, transaction management for reliable data updates, and creating robust basketball databases that support real-time analytics.
This is a course focused exclusively on baseball data analysis using sophisticated database techniques including window functions for player rankings, views for performance dashboards, triggers for automated stat tracking, and advanced table design with proper constraints and indexes. Students master data type conversion for handling diverse baseball metrics, transaction management for reliable data updates, and creating robust baseball databases that support real-time analytics.
This course provides foundational skills in Structured Query Language (SQL). Students learn database design, querying, and manipulation, gaining hands-on experience in data retrieval and analysis. Ideal for beginners, this course prepares learners for roles in data analysis, database management, and software development.
This course provides foundational skills in Structured Query Language (SQL). Students learn database design, querying, and manipulation, gaining hands-on experience in data retrieval and analysis. Ideal for beginners, this course prepares learners for roles in data analysis, database management, and software development.
This course builds upon foundational SQL skills to master advanced querying techniques specifically for soccer data analysis. Students learn complex joins, window functions, advanced aggregations, and performance optimization while working with real soccer datasets including player statistics, team performance, and match analytics. This course covers advanced filtering, subqueries, CTEs, and statistical analysis techniques essential for soccer analytics professionals. Perfect for analysts who have basic SQL knowledge and want to advance their soccer data analysis capabilities.
This course builds upon foundational SQL skills to master advanced querying techniques specifically for football data analysis. Students learn complex joins, window functions, advanced aggregations, and performance optimization while working with real NFL datasets including player statistics, team performance, and game analytics. This course covers advanced filtering, subqueries, CTEs, and statistical analysis techniques essential for football analytics professionals. Perfect for analysts who have basic SQL knowledge and want to advance their football data analysis capabilities.
This is a course focused exclusively on soccer data analysis using sophisticated database techniques including window functions for player rankings, views for performance dashboards, triggers for automated stat tracking, and advanced table design with proper constraints and indexes. Students master data type conversion for handling diverse soccer metrics, transaction management for reliable data updates, and creating robust soccer databases that support real-time analytics.
This course builds on intermediate concepts to master sophisticated data analysis techniques. Students learn window functions, performance optimization, real-time analytics, and advanced reporting for complex sports business scenarios. Perfect for analysts ready to tackle enterprise-level sports data challenges and drive strategic business decisions.
This course provides foundational skills in Structured Query Language (SQL). Students learn database design, querying, and manipulation, gaining hands-on experience in data retrieval and analysis. Ideal for beginners, this course prepares learners for roles in data analysis, database management, and software development.
This course advances your SQL skills through sabermetrics and player evaluation. Master complex queries for batting, pitching, and team analysis while calculating advanced baseball metrics used by analysts for competitive advantage.
This course teaches advanced querying techniques using sports business scenarios, covering data types, joins, subqueries, and Common Table Expressions. Students learn to analyze complex datasets like fan segments, venue performance, and revenue patterns while building sophisticated, readable queries for real-world business problems.
This is a course focused exclusively on football data analysis using sophisticated database techniques including window functions for player rankings, views for performance dashboards, triggers for automated stat tracking, and advanced table design with proper constraints and indexes. Students master data type conversion for handling diverse football metrics, transaction management for reliable data updates, and creating robust football databases that support real-time analytics.
This course provides foundational skills in Structured Query Language (SQL). Students learn database design, querying, and manipulation, gaining hands-on experience in data retrieval and analysis. Ideal for beginners, this course prepares learners for roles in data analysis, database management, and software development.
Understand how AI really works, where it fails, and how to use it with judgment instead of guesswork.
Turn vague, hit-or-miss prompts into clear briefs that get you exactly what you need.
Give AI persistent context for related work without rebuilding the setup in every chat.
Turn one bounded job into an AI tool a teammate can use without you beside them.
Build an AI agent for repeatable work, then give it clear actions, limits, and approval points.
Redesign the work that fills your week so AI does the heavy lifting and you keep the judgment.
Get AI to draft, fix, and harden real queries, and catch the query that runs clean but answers the wrong question.
Shape cohorts and funnels, diagnose anomalies without shipping a false cause, and write findings execs act on, keeping AI honest throughout.
Use AI to turn raw analyses into stakeholder-ready writeups, charts, dashboards, and exec narratives.
Learn the SQL every analyst uses every day, hands-on against a real in-browser database you query live.
The sequel to Introduction to SQL: conditional logic, every join type, set operations, subqueries, and CTEs, against the same live in-browser database.
Go beyond SELECT: type conversion, window functions, views, writes, transactions, table design, constraints, indexes, and triggers, against the same live database.
Practical Python for analytics and AI work, from your first variable to pandas and an LLM API call.
Modules, classes, async, testing, and the patterns that show up in real AI engineering code.
Performance, concurrency, packaging, and production-grade Python that holds up under real load.
Your first AI Fundamentals lesson is free. Check it out.