In today's age of analytics, the ability to transform data into information and actionable insights is essential. Coding in R for Data provides students with an understanding of how to import, format, understand, and communicate their data findings in R, a common statistical language utilized in a diverse range of industries.
In this 4-week course, students will learn how to program in R for effective data manipulation and visualization. Students will import, transform, and manipulate datasets for various analytical purposes. Students will develop the ability to create control structures, such as loops and conditional statements to traverse, sort, merge, and evaluate data. This course is designed for those who have no experience in R or programming.
In 2001, Stern became the first business school to establish its own center for exploring new models in teaching and learning. The NYU Stern Learning Science Lab is a team of creatives, educators, designers, and technologists who work closely with faculty to create engaging and interactive courses. Leveraging the resources of Stern and NYU at large, the team applies their expertise in user experience design, learning science, and video production to build immersive digital learning environments for business school education.
Online Learning Terminology
The term "asynchronous" refers to courses or course elements that can be completed at any time within the parameters of the course schedule. Asynchronous activities are things you do independently, like watch videos and complete assignments, and interactions with others over time via email, discussion forums, collaborative documents, and other channels. The term "synchronous" refers to learning with others in real time using videoconferencing and other technologies. Our online certificate courses are asynchronous with optional synchronous elements.
This course is a non-credit, pass/fail program. To pass this course, you will need a cumulative score of at least 55%. Upon successful completion of this course, participants will receive the NYU Stern Certificate in Coding in R for Data.
R Programming BasicsLearn coding basics for working with data in the R programming language
Preparing Data in RClean, format, and manipulate data
Report & Present Data in RBuild apps, create reports, and deliver presentations of your data in R
Who Should Attend
Although there are no formal education or background requirements, this course is designed for participants who meet the criteria below. While we strongly encourage global participation, please note that all courses are taught in English. Proficiency in written and spoken English is required.
Years of ExperienceParticipants with all levels of work experience are welcome to attend
Job FunctionsIdeal for any job function
PrerequisitesIntended for individuals with no experience in R or programming
The following agenda is a sample and subject to change.
- Getting Started
- Week 1: The Basics
- Week 2: Data Exploration
- Week 3: Data Presentation
- Week 4: Data Application
- In order to access the course, you will receive login credentials via email on the start date of the course. Activation instructions for your login credentials will be provided.
Required BooksWickham, H. & Grolemund, G. (2018). R for Data Science. O'Reilly: New York. (free, available digitally)
Live Online Meetups with Faculty
- Our live online meetups provide you with the opportunity to engage face-to-face with Professor Sosulski. Please note that all online meetups are recorded and available for your viewing at a later time. Missing a meetup will not impact your grade, however we recommend attending all sessions.
- Please expect to invest about 10 to 12 hours of your time per week to course lessons, exercises, and assignments.
January 27th - February 2nd
- Introduction to R Programming
- Data Structures, Variables, and Data Types
- Live Online Meetup 1: January 30th at 8:00 PM ET
February 3rd - February 9th
- Packages, Scripts, and Rmarkdown
- Descriptive Statistics in R
- Live Online Meetup 2: February 6th at 8:00 PM ET
February 10th - February 16th
- Reporting and Visualization in Rmarkdown
- Data Cleaning and Formatting for Messy Data
- Live Online Meetup 3: February 13th at 8:00 PM ET
February 17th - February 21st
- Functions, Iteration, and Conditionals
- Interactive Applications Using Rshiny
- Live Online Meetup 4: February 20th at 8:00 PM ET
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