The Library · Insights
Deep dives across data, automation & AI
1261 in-depth articles — from Excel foundations to production machine learning. Filter by topic or difficulty to find exactly what you need.
PythonFoundation
Sampling, Sorting, and Inspecting DataFrames in pandas: head, tail, sample, sort_values, and nlargest for Quick Data Exploration
Before you analyze anything, you need to orient yourself in your data. This lesson teaches you the five pandas methods every analyst uses in the first two minutes with a new dataset — and how to combine them into a fast, reliable exploration workflow.
15 min read
PythonExpert
Forecasting Seasonal Trends in pandas: Decomposing Time Series into Trend, Seasonality, and Residuals for Business Reporting
Learn how to decompose business time series into trend, seasonal, and residual components using pandas and statsmodels. Go from raw monthly data to formatted seasonal forecasts and stakeholder-ready Excel reports.
25 min read
Microsoft FabricPractitioner
Building a Star Schema in a Fabric Lakehouse Gold Layer: Creating Dimension and Fact Delta Tables with PySpark for Direct Lake Reporting
Learn how to design and build a production-quality star schema in your Fabric Lakehouse Gold layer using PySpark — covering surrogate key strategy, dimension and fact table construction, orphan validation, and post-write optimization for Direct Lake reporting in Power BI.
22 min read
Power AppsPractitioner
Configuring Dataverse Search: Enabling Relevance Search, Configuring Searchable Tables and Columns, and Tuning Quick Find for Model-Driven Apps
Most Dataverse Search problems aren't data problems — they're configuration problems. This lesson teaches you exactly how to enable Dataverse Search, control which tables and columns are indexed, and tune Quick Find views so users actually find what they're looking for.
24 min read
Power AppsFoundation
Configuring Model-Driven App Quick Forms and Card Forms: Displaying Related Record Summaries in Lookups and Subgrids
Learn how to build Quick Forms and Card Forms in Power Apps model-driven apps so users see rich, contextual summaries of related records directly inside lookup flyouts and subgrids — no navigation required. This hands-on lesson covers creating, configuring, and troubleshooting both form types from scratch.
17 min read
PythonPractitioner
Combining String, Date, and Numeric Transformations in a pandas Data Cleaning Pipeline: Standardizing Real-World Columns Before Analysis
Real exports are never clean — dates in four formats, currency fields as strings, category columns with a dozen spellings of the same value. This lesson builds a complete, production-grade cleaning pipeline that handles all three mess types together, with validation built in.
18 min read
PythonPractitioner
Cohort Analysis in pandas: Calculating Retention, Churn, and Lifetime Value from Transaction Data
Learn how to turn a raw transaction log into a complete cohort analysis in pandas — including a retention matrix, period-over-period churn rates, and cumulative customer lifetime value broken down by acquisition cohort. Covers everything from cohort assignment to a reusable production function.
20 min read
Power AppsExpert
Configuring Dataverse Plug-in Steps and Business Event Handlers: Triggering Server-Side Logic on Table Operations
Learn how the Dataverse event pipeline really works — stages, modes, images, and filtering — and how to configure plug-in steps and business event handlers without writing code. Build systems with genuine transactional integrity and event-driven automation.
31 min read
PythonFoundation
Calculating Running Totals, Subtotals, and Grand Totals in pandas for Boardroom-Ready Summary Tables
Learn how to build executive-quality summary tables in pandas — complete with running totals, group subtotals, and grand totals. This step-by-step lesson teaches you the exact patterns that turn raw data into boardroom-ready reports, using a realistic retail sales dataset you can run immediately.
14 min read
Power AppsExpert
Configuring Dataverse Auditing and Field-Level Change History in Model-Driven Apps
Learn how to configure Dataverse audit logging at the organization, table, and column level — then surface that change history directly on model-driven app forms. This expert-level lesson covers storage architecture, security role configuration, API querying, and compliance patterns.
29 min read
PythonExpert
Building a Dynamic pandas Report Pipeline with Parameters, Filters, and Excel Output
Stop rewriting your analysis every time someone asks for a different region or date range. This expert-level lesson walks you through building a fully parameterized pandas report pipeline that filters, aggregates, and exports formatted Excel workbooks from a single, reusable codebase — with batch generation across any parameter grid.
27 min read
Microsoft FabricExpert
Implementing Delta Lake Time Travel in a Fabric Lakehouse: Querying Historical Snapshots, Rolling Back Bad Writes, and Auditing Table Changes with PySpark and T-SQL
Delta Lake time travel turns catastrophic data overwrites into two-minute recoveries. Learn how to query historical snapshots by version and timestamp, execute safe rollbacks with RESTORE TABLE, build a persistent audit trail, and manage retention windows so time travel is available when you actually need it.
31 min read