R Programming course for beginnersR Programming for Data Analysis

Learn R, a popular language for data analysis and statistics. Manipulate data, create visualizations and perform basic statistical analysis, then use AI/ML algorithms to make data driven decisions.

Book an appointment Call 847-350-9034

Beginner friendlyggplot2 and dplyrAI/ML libraryFinal project

Nine parts of the course

From R basics to final projects. 1

Introduction to R

Installing R and R Studio, syntax, variables, data types, operators.2

Working with data

Vectors, matrices, data frames, input and output, subsetting.3

Control and functions

If-else, for and while loops, writing and using functions.4

Visualization with ggplot2

Scatter plots, bar charts, line graphs, aesthetics and faceting.5

Analysis with dplyr

Filtering, selecting, grouping, summarizing and joining datasets.6

Statistics with R

Descriptive and inferential statistics, hypothesis testing, regression.7

Projects and reporting

Git and GitHub, R Markdown reports, collaboration.8

Visualization beyond ggplot2

Themes, interactive Shiny apps, leaflet maps, other libraries.9

Final projects

Final projects, presentations, peer review and next steps.

Detailed course content

Topics covered in each part.

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

✓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✓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: association rule, clustering, classification, forecasting

R projects and reproducible research

✓Organizing your R projects✓Version control with Git and GitHub✓Dynamic reports with R Markdown✓Sharing and collaborating on R projects

Visualization beyond ggplot2

✓Customizing ggplot2 themes✓Interactive visualizations with Shiny✓Visualizing geographic data with leaflet✓Other R visualization libraries

Support and salient features

Career support and classroom features that come with the course.

Staffing support

✓Resume preparation✓Mock interview preparation✓Phone interview preparation✓Face to face interview preparation✓Project and 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 and exam✓Regular support beyond the classroom✓Students can re-take the class at no cost✓Dedicated conference rooms for group project work✓Live streaming for remote students✓Video recording to catch up on a missed class

Explore more training

See other programs, funding options and the class schedule. All training programs Education grant program Upcoming classes

Ready to start analyze data with R?

Call 847-350-9034 or book an appointment to talk about this course and funding options.Book an appointment Call 847-350-9034 Education grant program

Nine parts of the course

From R basics to final projects.1

Introduction to R

Installing R and R Studio, syntax, variables, data types, operators.2

Working with data

Vectors, matrices, data frames, input and output, subsetting.3

Control and functions

If-else, for and while loops, writing and using functions.4

Visualization with ggplot2

Scatter plots, bar charts, line graphs, aesthetics and faceting.5

Analysis with dplyr

Filtering, selecting, grouping, summarizing and joining datasets.6

Statistics with R

Descriptive and inferential statistics, hypothesis testing, regression.7

Projects and reporting

Git and GitHub, R Markdown reports, collaboration.8

Visualization beyond ggplot2

Themes, interactive Shiny apps, leaflet maps, other libraries.9

Final projects

Final projects, presentations, peer review and next steps.

Detailed course content

Topics covered in each part.1

Introduction to R

✓What is R?✓Installing R and R Studio✓Basic R syntax✓Variables and data types✓Simple calculations and operators2

Working with data in R

✓Vectors, matrices and data frames✓Data input and output✓Subsetting and indexing data✓Basic data manipulation3

Control structures and functions

✓Conditional statements (if-else)✓Loops (for, while)✓Writing and using functions✓Built-in functions in R4

Data visualization with ggplot2

✓Introduction to ggplot2✓Scatter plots, bar charts and line graphs✓Customizing plot aesthetics✓Combining and faceting plots5

Data analysis with dplyr

✓Introduction to dplyr✓Filtering and selecting data✓Grouping and summarizing data✓Joining datasets6

Introduction to statistics with R

✓Descriptive statistics✓Inferential statistics✓Hypothesis testing✓Linear regression✓AI/ML library: association rule, clustering, classification, forecasting7

R projects and reproducible research

✓Organizing your R projects✓Version control with Git and GitHub✓Dynamic reports with R Markdown✓Sharing and collaborating on R projects8

Visualization beyond ggplot2

✓Customizing ggplot2 themes✓Interactive visualizations with Shiny✓Visualizing geographic data with leaflet✓Other R visualization libraries

Support and salient features

Career support and classroom features that come with the course.1

Staffing support

Our salient features

Explore more training

See other programs, funding options and the class schedule.All training programs Education grant program Upcoming classes

Ready to start analyze data with R?

Call 847-350-9034 or book an appointment to talk about this course and funding options.Book an appointment Call 847-350-9034 Education grant program