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

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

3h 33mIntermediate2022-02-09

Authors

Helen Wall

Helen Wall

Data analytics and business analysis expert

Course details

Analytics is a big part of how the world does data science. But did you know that you can use applications like Excel, R, and Power BI for high-dimensional data reduction with machine learning models and algorithms? In this course, instructor Helen Wall gives you an overview of machine learning and data reduction techniques that enable you to analyze large datasets and determine trends with a variety of different classifications.

Learn about machine learning models like clusters and anomaly detection algorithms. Find out more about distance, dimensionality, and granularity, as you explore dimensional and numerical data reduction techniques, analytic models, and visualization tools in Excel, R, and Power BI. Along the way, get tips on how to integrate your methods so you can scale them for sharing with a wider audience.

Skills covered

RStudioRPower BIStatisticsBusiness AnalyticsMachine LearningBusiness IntelligenceSpreadsheetsData EngineeringMicrosoft ExcelData AnalysisArtificial Intelligence (AI)Programming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceMicrosoftSoftware DevelopmentDeep Dive (X:Y)

Concepts

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

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

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

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

Selecting Dimensions

  • Analyzing potential model dimensions
  • Removing or replacing null values

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
80,000 Toman