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

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 AnalyticsBusiness IntelligenceMachine LearningSpreadsheetsData EngineeringMicrosoft ExcelData AnalysisArtificial Intelligence (AI)Programming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceMicrosoftSoftware DevelopmentDeep Dive (X:Y)

Concepts

0. Introduction

01 - Use data reduction for valuable insights
02 - What you should know
03 - Introducing the course project
04 - Configuring Excel Solver Add-in
05 - Working with R
06 - Configuring R in Power BI

1. Working with Large Datasets

07 - AI and machine learning
08 - Numerosity
09 - Dimensionality
10 - Aggregating or grouping data
11 - Histograms
12 - Binning
13 - Correlation and covariance
14 - Challenge - Getting the data
15 - Solution - Getting the data

2. Clustering

16 - Calculating distances
17 - Hierarchical clustering
18 - Heatmaps and dendrograms
19 - K-means clustering in one dimension
20 - K-means clustering in two dimensions
21 - Determining k
22 - Challenge - Clustering
23 - Solution - Clustering

3. PCA

24 - Visualizing PCA
25 - Using Excel Solver to find solutions
26 - Solving for principal components axes
27 - Eigenvalues
28 - Eigenvectors
29 - PCA projection space
30 - Scree plot
31 - Challenge - PCA
32 - Solution - PCA

4. Selecting Dimensions

33 - Analyzing potential model dimensions
34 - Removing or replacing null values

5. Power BI and R

35 - Setting up R in Power Query Editor
36 - Creating custom code with R standard visual
37 - Challenge - Power BI
38 - Solution - Power BI

Conclusion

39 - Next steps