Explore data reduction techniques from machine learning and how to integrate your methods in Excel, R, and Power BI.
Business Analytics: Data Reduction Techniques Using Excel and R
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
Conrad Carlberg
