Introduction to R Libraries for Data Science | AIML End-to-End Session 54

Ready to dive deep into the world of
Artificial Intelligence
Machine Learning (AIML)?

Welcome to Session 54 of our End-to-End AIML series! In this session, we introduce the most popular R libraries used in Data Science. R is a powerful language for statistical analysis and data visualization, and its libraries make it a go-to choice for data scientists worldwide.

What You'll Learn:

Overview of R Libraries for Data Science: Get a comprehensive introduction to essential R libraries for data manipulation, visualization, machine learning, and statistical analysis.
dplyr: Learn how to perform fast, efficient data manipulation using dplyr’s intuitive functions for filtering, grouping, and summarizing data.
ggplot2: Master data visualization with ggplot2, the most popular R package for creating stunning plots and graphs.
tidyr: Explore tidyr’s tools for tidying and reshaping messy data, making it easier to work with.
caret: Discover how caret simplifies machine learning workflows in R, providing tools for model training, tuning, and evaluation.
shiny: Create interactive web applications and dashboards using Shiny, a powerful tool for sharing your data insights.
plotly: Add interactivity to your visualizations with Plotly, enhancing static plots with dynamic, web-friendly elements.
readr & readxl: Learn to easily import data from CSVs, Excel files, and other formats.
Hands-On Coding Example: Follow along as we demonstrate how to use these R libraries together to perform real-world data science tasks.
Best Practices: Learn how to integrate multiple libraries effectively to build robust data pipelines and enhance data analysis workflows.
By the end of this session, you'll be well-equipped with the knowledge of key R libraries and how to use them for effective data science projects.

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