Deep dives across data, automation & AI

Understanding Power Apps Licensing: Choosing the Right Plan for Your App and Users

Power Apps licensing trips up almost every new developer — not because it's impossible to understand, but because no one explains it clearly from first principles. This lesson walks you through exactly how the licensing model works, what drives the cost, and how to make confident decisions for any app you build.

How to Track Power BI Report Adoption and Identify Unused Assets Using Activity Logs and Usage Metrics

Most Power BI teams are flying blind about which reports people actually use. This lesson teaches you how to extract tenant-wide Activity Log data, combine it with your report inventory, and build an adoption dashboard that exposes unused assets before they drain your capacity and development budget.

DAX Error Handling in Practice: Using IFERROR, ISBLANK, and DIVIDE to Build Robust Measures

Real-world DAX measures fail silently all the time — blanks become zeros, denominators hit zero, and your report tells your manager a lie. This lesson teaches you how to use IFERROR, ISBLANK, and DIVIDE to write measures that handle messy data gracefully and communicate clearly when something is missing.

Importing and Transforming Your First Dataset in Power Query: A Step-by-Step Beginner Walkthrough

Raw data is almost never analysis-ready — dollar signs in number columns, inconsistent capitalization, missing values, and columns that need splitting are the norm. This hands-on lesson walks you through your first complete Power Query workflow, from connecting to a CSV file all the way to loading clean, properly typed data into Power BI, with a realistic sales dataset and every step explained from first principles.

Conditional Logic and Dynamic Column Selection in Power Query M: Mastering if-then-else, each, and Predicate Functions

Most Power Query users hardcode column names and write static transformations — then their queries break the moment the source data changes. This lesson teaches you to use M's if-then-else, the each keyword, and predicate functions to write queries that think dynamically, adapt to schema changes, and classify rows with multi-condition logic.

Understanding Query Dependencies and Evaluation Order in Power Query: How the M Engine Executes Your Steps

Power Query doesn't run your steps in the order you wrote them — it uses a dependency-driven evaluation model that surprises almost every new user. Learn how the M engine actually decides what to compute, when, and why, so you can write faster and more reliable queries.

Exiting a Freelance Data Engagement Professionally: Handover Documentation, Knowledge Transfer, and Turning Project Endings into Referral Opportunities*

Mastering the Data Science Case Interview: Frameworks, Live Problem-Solving, and How to Think Out Loud Under Pressure

Most data scientists fail case interviews not because they lack technical knowledge, but because they don't know how to make their reasoning visible in real time. This lesson gives you the SCOPE framework, full worked examples, and the specific verbal techniques that turn anxious guessing into confident, structured thinking.

Automating dbt Environment Promotion with CI/CD Pipelines, Slim CI, and State-Aware Deployments

Learn how to build a production-grade dbt promotion pipeline that uses Slim CI to run only modified models, deferred state to resolve dependencies against production, and webhook-based promotion gates to prevent bad code from ever reaching your business-critical data. This is the CI/CD architecture lesson that dbt's documentation glosses over.

Cost Attribution and Pipeline-Level Resource Optimization: Profiling and Reducing Cloud Spend in Production Data Workflows

Cloud bills are easy to generate and hard to explain. This expert-level lesson teaches you how to instrument production data pipelines for granular cost attribution, profile Spark and SQL workloads to find what's actually expensive, and implement durable optimization strategies across compute, query, and storage layers — without degrading SLAs.

Multi-Agent Orchestration: Building RAG Systems Where Specialized Agents Collaborate Across Data Sources

Single-agent RAG systems break when your data lives across SQL databases, document archives, and live APIs — because one retriever can't serve all those sources well. This lesson teaches you to build multi-agent RAG architectures with specialized agents, a working orchestrator, and production-grade failure handling.

Building a Multi-Tenant LLM Platform: Isolating Contexts, Enforcing Usage Quotas, and Managing Costs Per Customer

Multi-tenant LLM platforms fail in ways traditional SaaS doesn't — context bleeds between tenants, quota enforcement has race conditions, and cost attribution is invisible until the bill arrives. This lesson builds the complete system: atomic quota enforcement, structural context isolation, and billing-grade cost attribution, with production-ready Python code throughout.