• Overview of Power BI Desktop and its components
• Basic experience of data analytic processes
• Useful hints, tips and good practices in data analytics
Module | Objective | Content | Duration (mins) |
Introduction | The ‘quick-start’ guide to the workshop | What is Power BI?
Why use it? The Power BI environment Data analytics cycle |
30 |
Wrangle | Acquisition & Transformation
Acquire data from multiple varied sources and transform into format suitable for modelling and analysis |
Acquire data from common sources: Excel, csv
Transform data using Power Query and introduction to Power Query ‘M’ code |
90 |
Model | Organisation and relating
Organise data within tables and relate or link tables to support analysis Add calculated ‘helper’ columns |
Facts, dimensions and relationships and an introduction to stars and snowflakes
Create calculated columns using DAX |
30 |
Analyse | Investigation and exploration
Investigate and explore data to identify patterns and answer relevant questions |
Introduction to DAX
Basic DAX queries The CALCULATE function How to structure a DAX query The VAR & RETURN statements Computed columns Computed tables See the numbers with Table & Matrix visualisations Quick Queries, Natural Language Queries Built-in AI analysis Common DAX patterns – time series, averages, cumulatives, ranking |
90 |
Visualise | Presentation and communication
Assemble analysis results in ways that highlight insights and inform the end user |
Choosing the right chart
Layout Colour scheme Navigation, bookmarks, popup & drilldown |
120 |
Process | Data analytics framework
Provide a framework or structure within which data analytics is performed efficiently and effectively |
PIPAR vs WMAV+PT
PQ hard to review, DAX harder |
n/a |
Theory | Knowledge and technique
Knowledge, techniques and best practices that support all aspects of data analytics |
Analytic techniques
Coding best practices Field, Table, File and other naming conventions Visualisation principles Relational modelling principles |
n/a |
Wrap up | 15 |