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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:

Introduction to R

  • What is R?

  • Installing R and R Studio

  • Basic R syntax

  • Variables and data types

  • Simple calculations and operators​

Working with Data in R

  • Data structures in R: vectors, matrices, and data frames

  • Data input and output

  • Subsetting and indexing data

  • Basic data manipulation

Control Structures and Functions

  • Conditional statements (if-else)

  • Loops (for, while)

  • Writing and using functions

  • Built-in functions in R

Data Visualization with ggplot2

  • Introduction to ggplot2

  • Creating scatter plots, bar charts, and line graphs

  • Customizing plot aesthetics

  • Combining and faceting plots​

Data Analysis with dplyr

  • Introduction to dplyr

  • Filtering and selecting data

  • Grouping and summarizing data

  • Joining datasets

Introduction to Statistics with R

  • Descriptive statistics

  • Inferential statistics

  • Hypothesis testing

  • Linear regression

  • AI/ML library with Association Rule, Clustering, Classification, Forecasting

R Projects and Reproducible Research

  • Organizing your R projects

  • Version control with Git and GitHub

  • Creating dynamic reports with R Markdown

  • Sharing and collaborating on R projects

Data Visualization Beyond ggplot2

  • Customizing ggplot2 themes

  • Interactive visualizations with Shiny

  • Visualizing geographic data with leaflet

  • Other R visualization libraries

Final Projects and Course Wrap-up

  • Students work on final projects applying what they have learned

  • Project presentations and peer review

  • Course review and next steps in R programming

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