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Language: R. Study, Exploratory Data Analytics and Data Visualizations about stationarity in data scientists roles applying the following techniques: PCA, Factor Analysis, Clustering, KMeans and Hierarchical Clustering.

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Stationarity in Data Scientists' Roles

  • Course: Statistical Learning
  • Grade: 9.5/10

This repository contains a study on stationarity in data scientists' roles using R.

Techniques Used

  • Principal Component Analysis (PCA)
  • Factor Analysis
  • Clustering
  • KMeans
  • Hierarchical Clustering

Data

  • aug_train.csv: Dataset used for the analysis.

Files

  • unsuper_learning.html: HTML report containing the results and documentation of the study.

Usage

To replicate the analysis, you can use the provided dataset and the R scripts mentioned in the report. Make sure to have the necessary R packages installed.

Contributing

Contributions are welcome! If you have any suggestions, improvements, or new insights to add, please fork the repository and submit a pull request with your changes.

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Language: R. Study, Exploratory Data Analytics and Data Visualizations about stationarity in data scientists roles applying the following techniques: PCA, Factor Analysis, Clustering, KMeans and Hierarchical Clustering.

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