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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 Content

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

Staffing Support​
  • Resume Preparation

  • Mock Interview Preparation

  • Phone Interview Preparation

  • Face to Face Interview Preparation

  • Project/Technology Preparation

  • Internship with internal project work

  • Externship with client project work

Our Salient Features:
  • Hands-on Labs and Homework

  • Group discussion and Case Study

  • Course Project work

  • Regular Quiz / Exam

  • Regular support beyond the classroom

  • Students can re-take the class at no cost

  • Dedicated conf. rooms for group project work

  • Live streaming for the remote students

  • Video recording capability to catch up the missed class

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