Explore data reduction techniques from machine learning and how to integrate your methods in Excel, R, and Power BI.
Machine Learning with Data Reduction in Excel, R, and Power BI
Overview
Syllabus
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
- AI and machine learning
- Numerosity
- Dimensionality
- Aggregating or grouping data
- Histograms
- Binning
- Correlation and covariance
- Challenge: Getting the data
- Solution: Getting the data
- 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
- Visualizing PCA
- Using Excel Solver to find solutions
- Solving for principal components axes
- Eigenvalues
- Eigenvectors
- PCA projection space
- Scree plot
- Challenge: PCA
- Solution: PCA
- Analyzing potential model dimensions
- Removing or replacing null values
- Setting up R in Power Query Editor
- Creating custom code with R standard visual
- Challenge: Power BI
- Solution: Power BI
- Next steps
Taught by
Helen Wall
