Power BI Advanced
1-day course
Course Overview
This advanced course takes a deep dive into creating visualisations that incorporate AI to greatly enhance your data analysis experience. It looks at the Analytics pane to spot trends and at configuring anomaly detection, that helps you to understand whether you have quality issues in your data. It includes key influencer visuals, as the name suggests, key influencers allow you to understand the factors that drive a certain metric that you’re interested in.
Learn how to use parameters to ask ‘what if’ questions about your data and to consolidate multiple tasks. The course also looks at forecasting features that allow you to predict future behaviour patterns in your data, and explains how to use DAX to build a date tables that gives you greater control over Power BI’s time intelligence features
Prerequisite
The assumption is that you are already using Power BI on a regular basis and understand the processes of analysing data, writing DAX measures, and building dashboards. If not, then we recommend taking the 2-day Power BI Intermediate course before attending this one.
Course Content
Lesson 1. Data Model Design
1.1. Data profiling
1.2. Data model planning
1.3. Data transformations
Lesson 2. Using Parameters
2.1. Configuring parameters
2.2. Inserting parameters
Lesson 3. Dynamic Parameters
3.1. Numeric parameters
3.2. Field parameters
Lesson 4. Group, Bin & Cluster
4.1. Creating groups
4.2. Creating bins
4.3. Creating clusters
4.4. Multivariate cluster
Lesson 5. Forecast Charts
5.1. Configure forecast charts
5.2. Assigning forecast units
5.3. Confidence intervals
Lesson 6. The Analytics Pane
6.1. Finding anomalies
6.2. Detecting outliers
Lesson 7. AI Capabilities
7.1. Generate insights
7.2. The analyse feature
7.3. Power BI AI visuals
7.4. Key influencer visual
7.5. Decompression visual
7.6. The narrative visual
7.7. Explaining differences
Lesson 8. Visual Calculations
8.1. Insert a Visual Calc
8.2. Built in templates
Lesson 9. DAX Query View
9.1. Query View environment
9.2. Simplifying measures
Lesson 10. UDF Functions
10.1. What are UDFs?
10.2. Why UDFs matter
10.3. Parameter types