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Business Analytics: Data Reduction Techniques Using Excel and R

via LinkedIn Learning

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Overview

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

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

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

Taught by

Conrad Carlberg

Reviews

4.7 rating at LinkedIn Learning based on 85 ratings

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