R Programming
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Overview:
R is a programming language and environment widely used for statistical computing, data analysis, and graphics.
Key Features:
Open-source, extensive statistical libraries, and data visualization capabilities.
Skills Developed:
Getting started with programming in R for data analysis.
Introduction to R and RStudio
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Introduction:
RStudio is an integrated development environment (IDE) for R. This module covers the basics of both R and RStudio.
Key Concepts:
R syntax, data types, variables, and RStudio interface.
Skills Developed:
Navigating RStudio and executing basic R commands.
Data Manipulation with R
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Overview:
Data Manipulation with R focuses on preparing and transforming data for analysis.
Key Concepts:
Data frames, subsetting, merging, and reshaping data.
Skills Developed:
Cleaning and restructuring data for analysis in R.
Data Visualization with ggplot2
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Introduction:
ggplot2 is a popular data visualization package in R. This module covers creating compelling visualizations.
Key Concepts:
Grammar of graphics, layers, and aesthetics.
Skills Developed:
Designing and customizing visualizations using ggplot2.
Statistical Analysis with R
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Overview:
Statistical Analysis with R covers a range of statistical techniques for data exploration and hypothesis testing.
Key Techniques:
Descriptive statistics, hypothesis testing, and regression analysis.
Skills Developed:
Conducting statistical analysis using R.
Machine Learning in R
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Introduction:
Machine Learning in R explores using R for building and evaluating machine learning models.
Key Algorithms:
Decision trees, clustering, and regression.
Skills Developed:
Implementing machine learning algorithms in R.
R Package Development
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Overview:
R Package Development covers creating, documenting, and sharing custom R packages.
Key Elements:
Package structure, documentation, and version control.
Skills Developed:
Developing and publishing R packages for extended functionality.
