Most of the time, users of R and Python will rely on packages and libraries as far as possible, in order to avoid “reinventing the wheel”. Packages that are established are also often superior and preferred, due to lower chance of errors and bugs.

We list down the most popular and useful packages in R and Python for data science, statistics, and machine learning.

## Packages in R

- arules
- arulesViz
- car
- caret
- cluster
- corrplot
- ggplot2
- lattice
- perturb
- psych
- readr
- recommenderlab
- reshape2
- ROCR
- rpart
- rpart.plot
- tidyverse

## Python Packages

- factor_analyzer
- math
- matplotlib
- numpy
- pandas
- scipy
- seaborn
- sklearn
- statsmodels

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