Summary:
This course is designed for beginners who want to learn R, a popular programming language for data analysis and statistics. Students will acquire fundamental programming skills in R and learn to manipulate data, create visualizations, and perform basic statistical analysis to leverage AI/ML algorithm to make data driven decisions.
Course Specifics:
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Introduction to R
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What is R?
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Installing R and R Studio
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Basic R syntax
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Variables and data types
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Simple calculations and operators​
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Working with Data in R
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Data structures in R: vectors, matrices, and data frames
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Data input and output
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Subsetting and indexing data
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Basic data manipulation
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​Control Structures and Functions
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Conditional statements (if-else)
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Loops (for, while)
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Writing and using functions
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Built-in functions in R
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Data Visualization with ggplot2
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Introduction to ggplot2
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Creating scatter plots, bar charts, and line graphs
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Customizing plot aesthetics
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Combining and faceting plots​
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Data Analysis with dplyr
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Introduction to dplyr
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Filtering and selecting data
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Grouping and summarizing data
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Joining datasets
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Introduction to Statistics with R
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Descriptive statistics
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Inferential statistics
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Hypothesis testing
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Linear regression
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AI/ML library with Association Rule, Clustering, Classification, Forecasting
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R Projects and Reproducible Research
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Organizing your R projects
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Version control with Git and GitHub
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Creating dynamic reports with R Markdown
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Sharing and collaborating on R projects
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Data Visualization Beyond ggplot2
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Customizing ggplot2 themes
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Interactive visualizations with Shiny
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Visualizing geographic data with leaflet
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Other R visualization libraries
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Final Projects and Course Wrap-up
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Students work on final projects applying what they have learned
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Project presentations and peer review
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Course review and next steps in R programming
