Machine Learning with Data Reduction in Excel, R, and Power BI

Explore data reduction techniques from machine learning and how to integrate your methods in Excel, R, and Power BI.

Introduction

• Use data reduction for valuable insights
• What you should know
• Introducing the course project
• Configuring Excel Solver Add-in
• Working with R
• Configuring R in Power BI

1. Working with Large Datasets

• AI and machine learning
• Numerosity
• Dimensionality
• Aggregating or grouping data
• Histograms
• Binning
• Correlation and covariance
• Challenge: Getting the data
• Solution: Getting the data

2. Clustering

• Calculating distances
• Hierarchical clustering
• Heatmaps and dendrograms
• K-means clustering in one dimension
• K-means clustering in two dimensions
• Determining k
• Challenge: Clustering
• Solution: Clustering

3. PCA

• Visualizing PCA
• Using Excel Solver to find solutions
• Solving for principal components axes
• Eigenvalues
• Eigenvectors
• PCA projection space
• Scree plot
• Challenge: PCA
• Solution: PCA

4. Selecting Dimensions

• Analyzing potential model dimensions
• Removing or replacing null values

5. Power BI and R

• Setting up R in Power Query Editor
• Creating custom code with R standard visual
• Challenge: Power BI
• Solution: Power BI

Conclusion

• Next steps