Overview
This course is intended as a continuation of our introductory-level courses in Power BI (Data analysis and visualization with  with Power BI ). It covers common intermediate-level tasks and some of Power BI’s most desirable new features.

Prerequisites
Before attending this course, students should have: – General knowledge equivalent to what is covered in Data analysis and visualization with  with Power BI  .

At Course Completion
After completing this course, students will be able to:
• Import data from PDFs, regions of web pages, and collections of files
• Characterize data with data profiling
• Merge mismatched data sets with fuzzy matching
• Generate custom columns in Power Query
• Perform advanced data modeling
• Use Power BI time intelligence
• Work with custom scripts in R and Python
• Create KPIs and scorecards
• Use advanced report design techniques
• Use advanced dashboard design techniques
• Perform basic statistical analysis in Power BI

Duration

3days

Course Outline

Intermediate Power Query
Importing from PDFs
Finding data in web pages
Getting tabular data
Getting data by providing an example
Importing the contents of a folder
Using fuzzy matching to combine disparate data sets
Creating custom columns in Power Query
Common math & string operations
M script
Columns by example

Intermediate Data Modeling
Adding What-If Parameters
Grouping and Binning
Using Time Intelligence
Generating DAX with Quick Measures

Script Visuals
Creating an R script visual
Installing an R environment
Creating a Python visual
Installing a Python environment

Advanced Report Design
Using report themes
Creating your own theme
Conditional formatting in tables and matrices
Using drill through in your reports
Adding data-driven images

Advanced Dashboard Design
Using dashboard themes
Using the KPI visual
Using the Multi KPI visual
Adding KPIs and trend analysis with DAX
Strategies for adding KPIs to tables & matrices
Importing from Excel data model
Conditional formatting
The DAX UNICHAR function
Image embedding

Analytics
Characterizing your data
Revisiting the data profiler
Getting help from custom visuals
Moving averages
ARIMA
Linear regression in R
Time series forecasting in Python