Advanced Analytics with Power BI

Overview

Advanced Analytics with Power BI is a focused, practice-oriented guide that helps analysts and data teams move from static dashboards to repeatable, decision-ready analytics. Grounded in end-to-end workflows, the guide emphasizes reliable data preparation, scalable modeling, robust measure design, and practical ways to surface predictive insights inside reports. Readers learn how to convert raw sources into consistent analytical datasets, author dependable DAX measures, and present results through visual narratives that stakeholders can act on.

What you will learn

• Power Query techniques to clean, shape, and combine disparate sources, establishing a dependable analysis foundation.
• Modeling patterns and star schema principles that reduce complexity, improve query performance, and simplify DAX calculations.
• DAX best practices for measures, time-intelligence, and reusable business metrics that support consistent decision-making.
• Exploratory visual-analytics workflows to surface trends, outliers, correlations, and hypothesis-driven insights quickly.
• How to integrate predictive outputs—such as Azure Machine Learning scores—and apply forecasting or anomaly detection in reports.
• Options for extending Power BI with R scripts or custom visuals to address advanced statistical analysis and tailored presentations.
• Dashboard design and storytelling techniques to convert analytics into operational insights and executive-ready summaries.

Core focus and topic coverage

The guide centers on analytics maturity: improving data literacy across teams, establishing reproducible calculations, and designing models that scale with evolving business needs. Chapters walk through shaping and validating data in Power Query, defining schemas that support fast queries, and crafting DAX measures that encode business rules. It also highlights discovery tools for rapid insight generation and pragmatic options for integrating external models or scripts when built-in capabilities are insufficient.

Practical business applications

Realistic scenarios demonstrate measurable impact across functions. Retail and supply chain examples show how forecasting and anomaly detection can optimize inventory and promotional planning. Marketing cases illustrate campaign lift, segmentation, and attribution metrics that improve budget allocation. Operational examples present live KPI monitoring and alerting to accelerate response times. Each scenario emphasizes turning predictive or diagnostic outputs into concise dashboards that drive timely decisions.

Projects and hands-on practice

Step-by-step exercises reinforce end-to-end patterns: ingest and shape representative data, implement a star schema, author foundational DAX measures, and build narrative dashboards. Sample projects include sales performance analysis, trend reporting using automated insights, customer segmentation workflows, and a template for near-real-time monitoring. Exercises are designed so practitioners can adapt patterns to their own datasets and business questions.

Who should use this guide

Ideal for analysts, data-aware business professionals, and advanced students with basic Power BI experience who want to apply analytics to business problems. The material supports practitioners who combine visualization, calculation, and predictive techniques to deliver reports that inform strategy and operations.

How to get the most from the guide

Work alongside the examples in your Power BI environment: replicate DAX formulas, run discovery tools on representative datasets, and experiment with a simple predictive score or R script. Treat sample projects as templates to refine data literacy and embed reproducible analytics practices into reporting workflows. Where relevant, validate predictive outputs against known business outcomes before operationalizing scores.

Glossary highlights

• DAX — the formula language used to author measures and calculations in Power BI.
• Power Query — the engine for ingesting and transforming source data for analysis.
• Quick Insights — automated discovery tools that surface trends, anomalies, and correlations.
• Predictive analytics — using historical data and models to forecast outcomes and score records.

Next steps

Apply the guide’s practical patterns to build robust reports, introduce predictive scores where they add value, and design dashboards that help teams make faster, evidence-based decisions. Based on Microsoft’s guidance, the guide is best used iteratively: prototype, validate with stakeholders, and scale proven patterns across reports and teams.