Power BI: Integrating AI and Machine Learning

Course details

Power BI is a powerful data analytics and visualization tool that allows business users to monitor data, analyze trends, and make decisions. Every month, Microsoft pushes Power BI updates out to the end users, and the growth of Power BI is a key part of Microsoft’s current strategy. This course showcases existing AI and machine learning capabilities available directly in accessible Power BI functionalities. Data analytics and business analysis expert Helen Wall gives you a useful overview of Power BI, then dives into the steps to configure Power Query and your data model. Helen steps through analyzing single variables and shows you the tools and techniques to measure relationships between variables. She shows you visuals that you can use to pose and answer questions in Power BI, explains useful techniques to enhance your analysis of time series data, and walks you through some best practices for sharing your analysis.

Concepts

0. Introduction

01 - The power of Power BI
02 - What you should know
03 - Overviewing AI and machine learning types
04 - Defining dimensionality
05 - Utilizing the Power BI ecosystem and Azure
06 - Configuring R in Power BI Desktop
07 - Introducing the course project

1. Configuring Power Query and the Data Model

08 - Utilizing AI in the ETL framework
09 - Configuring parameters
10 - Analyzing dataset statistics and distributions
11 - Configuring separate error logs for existing datasets
12 - Running Vision algorithms
13 - Utilizing Text Analytics algorithms
14 - Leveraging AI and the star schema
15 - Adjusting DateTime fields for lags

2. Analyzing a Single Variable

16 - Configuring aggregations and dimensionality
17 - Filtering options
18 - Calculating DAX measures
19 - Challenge - Single variable
20 - Calculating rolling averages
21 - Utilizing binning to create histograms
22 - Summarizing statistics
23 - Splitting a category with small multiples
24 - Leveraging violin plots
25 - Solution - Single variable

3. Measuring Relationships between Variables

26 - Visualizing relationships with scatter plots
27 - Accessing the Analytics pane
28 - Calculating correlations
29 - Visualizing correlations
30 - Adding clustering to existing visuals
31 - Calculating best fit line
32 - Utilizing the outlier detection visual
33 - Calculating outliers
34 - Contextualizing outliers
35 - Challenge - Multiple variables
36 - Solution - Multiple variables

4. Utilizing AI Visuals to Ask What-If Questions

37 - Determining key drivers with decomposition tree visual
38 - Leveraging the Q&A visual
39 - Discovering key insights with the Key Influencer visual
40 - Utilizing parameters to model what-if scenarios
41 - Challenge - AI visuals
42 - Solution - AI visuals

5. Analyzing Time Series Data

43 - Organizing time series analysis
44 - Adding forecasting from the Analytics pane
45 - Leveraging anomaly detection
46 - Utilizing ARIMA forecasting
47 - Incorporating seasonality through TBATS forecasting
48 - Analyzing predictions vs. actuals
49 - Challenge - Time series analysis
50 - Solution - Time series analysis

6. Creating and Sharing Analysis

51 - Designing a consolidated view for sharing
52 - Uploading and sharing in the Power BI service
53 - Configuring quick insights
54 - Challenge - Shared view
55 - Solution - Shared view

Conclusion

56 - How to learn ML and AI in Power BI