R Programming: Mastering Data Analysis and Visualization - A comprehensive introduction to R
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Set up and navigate the R programming environment using RStudio
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Import and clean data from various sources, including Excel, SPSS, and CSV files
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Perform exploratory data analysis to summarize and understand datasets
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Create effective data visualizations using ggplot2
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Apply Business Intelligence concepts to analyze and interpret data
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Implement web scraping techniques to extract data from websites
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Visualize geographic data and create interactive maps using leaflet.
Overview
Off the shelf (OTS)
This course is designed for data analysts, business intelligence professionals, and researchers who wish to leverage the R programming language for data analysis and visualization. It is particularly beneficial for those transitioning from Excel, SPSS, or SAS workflows to a more robust and scalable data analysis environment.
Participants should have a foundational understanding of data analysis concepts. Familiarity with basic programming principles is advantageous but not mandatory.
The R Data Science Training Course offers a comprehensive introduction to the R programming language, focusing on its application in data analysis and visualization. Participants will learn to import, clean, and transform data from various sources, perform exploratory data analysis, and create compelling visualizations using R's powerful libraries. The course also covers the basics of web scraping and geographic data visualization, providing a well-rounded skill set for modern data science tasks.
Key Topics Covered:
• Introduction to R and RStudio
• Data wrangling with tidyverse and importing data from various sources
• Exploratory Data Analysis (EDA) using summary statistics and visualizations
• Data visualization with ggplot2
• Introduction to Business Intelligence concepts and tools
• Web scraping techniques using rvest and parallel programming
• Geographic data visualization with leaflet
The course is delivered over three days and includes hands-on exercises, group discussions, and real-world case studies to reinforce learning.
Delivery method
Virtual
Course duration
21 hours
Competency level
Foundation

Delivery method
-
Virtual
Course duration
21 hours
Competency level
-
Foundation
