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. They will import, transform, and manipulate datasets for various analytical purposes. Program participation will also 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-House Quality
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.
Program Takeaways
During this course, participants will:
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R Programming Basics
Learn coding basics for working with data in the R programming language. -
Preparing Data in R
Clean, format, and manipulate data. -
Report & Present Data in R
Build 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.
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Years of Experience
Participants with all levels of work experience are welcome to attend -
Job Functions
Ideal for any job function -
Prerequisites
Intended for individuals with no experience in R or programming
Agenda
The following agenda is a sample and subject to change.
Course Access
- 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 Books
Wickham, H. & Grolemund, G. (2018). R for Data Science. O'Reilly: New York. (free, available digitally); Sosulski, K. (2019.) R Fundamentals. Bookdown: 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.
Workload
- Please expect to invest about 10 to 12 hours of your time per week to course lessons, exercises, and assignments.
Week 1 (June 13th): Coding Basics
- Introduction to R Programming
- Data Structures, Variables, and Data Types
- Live Online Meetup 1: June 16th at 8:00 PM ET
Week 2 (June 20th): Data Exploration
- Packages, Scripts, and Rmarkdown
- Descriptive Statistics in R
Week 3 (June 27th): Data Presentation
- Reporting and Visualization in Rmarkdown
- Data Cleaning and Formatting for Messy Data
- Live Online Meetup 2: June 30th at 8:00 PM ET
Week 4 (July 4th): Data Application
- Functions, Iterations, and Conditionals
- Interactive Applications Using Rshiny
Participant Reviews
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