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Coding in R for Data

$850.00 $1,320.00 Save $470.00
Coding in R for Data

Coding in R for Data

$850.00 $1,320.00 Save $470.00

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.

Course Trailer


Online Learning Experience

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.

Software/Technical Requirements
  • Desktop or laptop computer with administrative access and ability to install software
  • A webcam and headset (preferred) or microphone for online meetups
  • Broadband/high-speed Internet (1.5 Mbps minimum/3 Mbps preferred) to ensure your ability to participate fully in online meetups
  • Operating System: Mac OS 10.14 or Windows 10 or later
  • Browser: Safari 14, Chrome 88, Firefox 84, Microsoft Edge 88, or Internet Explorer 11 or later
  • Microsoft Excel, PowerPoint (Mac users are encouraged to use KeyNote), and a basic text editor such as Notepad or TextEdit
  • Instructions and codes to install R and RStudio will be provided
  • This program does not support mobile devices, including tablets and iPads.

  • Certificates and Credits

    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:

    • 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.

    • 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


    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.


      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

      Customer Reviews

      Based on 1 review
      William Flannery
      From zero to R in 4 weeks

      Okay phew! That was intense. Excellent course. I have been coding for many years and I am self-taught. I have not coded in R before. I did the NYU Stern data visualization course in the fall and this was the natural follow on. If you haven't coded before, it will not be a problem. For me the key take away was the data wrangling element. R is great for this task. Raw data are generally 'unfriendly', poorly-structured animals that need a considerable amount of taming before they can be used for data visualization. R is the tool and this course is where you can learn to unlock its power. Once you have learned where to look and the question(s) to ask, R seems to have it all. I now use R's plotting, summary stats and modelling functions exclusively. From small acorns ...