1. Prototyping vs. Production
1.1. Power BI in Pipelines
1.1.1. Microsoft: Big Data Architectures
1.1.2. Microsoft: Enterprise Data Warehouse
1.1.3. Learnen: Excel Online To Power BI
1.1.4. Microsoft: Enterprise Business Intelligence
2. Starting Points
2.1. Recap Course Objectives
2.2. Access this Mindmap
2.2.1. Copy this Mindmap
2.3. Download Course Datasets
2.3.1. Download Course Materials
2.4. Download Power BI Desktop
2.4.1. Create a Power BI Online Account
3. Cheat Sheets
3.1. https://docs.microsoft.com/en-us/power-bi/
3.2. https://www.techrepublic.com/article/microsoft-power-bi-a-smart-persons-guide/
3.3. https://pbicheatsheet.blob.core.windows.net/pbicheatsheet/Macaw%20Power%20BI%20cheat%20sheet%20EN.pdf
3.4. https://docs.microsoft.com/en-gb/dax/
3.5. https://docs.microsoft.com/en-gb/powerquery-m/index
3.6. https://oxcrx34285.i.lithium.com/t5/image/serverpage/image-id/10656i1E4A1F0FCD1C4429/image-size/large?v=1.0&px=999
3.7. https://docs.microsoft.com/en-us/power-bi/media/service-azure-and-power-bi/azure_4_complex.png
3.8. https://azure.microsoft.com/en-us/solutions/architecture/modern-data-warehouse/
3.9. https://databricks.com/blog/2019/02/07/high-performance-modern-data-warehousing-with-azure-databricks-and-azure-sql-dw.html
3.10. https://docs.microsoft.com/en-us/power-bi/whitepapers
4. Final Exercise
4.1. A "What If" Dashboard
4.1.1. EnterpriseDNA: Scenario Analysis in PBI
4.1.2. Microsoft: Data Refresh in Power BI
5. Four Deep Dive Scenarios
5.1. 1. Visual Analytics
5.1.1. Visual Analytics
5.1.1.1. Tibco: What is Data Science?
5.1.1.2. McKinsey: Analytics Disciplines
5.1.1.3. Info is Beautiful: Information Design
5.1.1.4. UX Planet: Better Dashboard Design
5.1.1.5. Tufte: Graphical Excellence
5.1.1.6. Few: Why Most Dashboards Fail
5.1.1.7. PLOS: 10 Simple Rules for Better Charts
5.1.2. Visual Analytics Scenario
5.1.2.1. YouTube: Hans Rosling
5.1.2.2. Gapminder: The Gapminder Visual
5.2. 2. Dashboard Mechanics
5.2.1. Dashboard Mechanics
5.2.1.1. Juice: A Guide to Creating Dashboards People Love to Use
5.2.1.2. Few: Why Most Dashboards Fail
5.2.1.3. Weekdone: OKRs
5.2.2. Dashboard Mechanics Scenario
5.2.2.1. Microsoft: Cards
5.2.2.2. Microsoft: Slicers
5.2.2.3. Microsoft: Bookmarks
5.3. 3. Data Wrangling
5.3.1. Data Wrangling
5.3.1.1. LearnSQL: Power BI Capabilities
5.3.1.2. Microsoft: Database Design Basics
5.3.1.3. Microsoft: Understand Star Schema
5.3.2. Data Wranging Scenario (pt.1)
5.3.2.1. Microsoft: M Query Language
5.3.2.2. Microsoft: Power Query Editor
5.3.2.3. ExceleratorBI: Data Design in Power BI
5.3.2.4. Global Innovation Index
5.3.3. Data Wrangling Scenario (pt.2)
5.3.3.1. Microsoft: Models in Power BI Desktop
5.3.3.2. Microsoft: Row Level Security
5.4. 4. Calculated Measures
5.4.1. Calculated Measures
5.4.1.1. Microsoft: DAX Guide
5.4.1.2. DAX Patterns: Pattern Book
5.4.1.3. Energy Central: Analytics Curve
5.4.1.4. Microsoft: DAX Quickstart
5.4.2. Calculated Measures Scenario
5.4.2.1. An Advanced DAX Pattern
5.4.2.1.1. XXLBI: Regression in DAX
5.4.2.2. SQLBI: Row and Filter Context
6. Fundamentals
6.1. Purpose of Power BI
6.1.1. Weekdone: OKR Examples
6.2. Anatomy of Power BI
6.2.1. Microsoft: PBI Desktop and PBI Service
6.2.2. SQL Dusty: Power BI Architecture
6.3. Introduction to Power BI Desktop
6.3.1. Microsoft: PBI Desktop
6.3.2. TDS: Forecasting in Power BI
6.4. Introduction to Power BI Online
6.4.1. Microsoft: PBI Service
6.4.2. Microsoft: Share in Power BI
6.4.3. Microsoft: Data Refresh in Power BI
6.5. Process of Power BI
6.5.1. Roman Pilcher: Product Management
6.5.2. Map and Fire: Jobs to be Done