Coding and Visual Analytics provides students with the foundational skills needed to become a data literate manager. Learn the basics of programming for data preparation, understanding, and communication. Build core skills in the R programming language for data importing, formatting, and analysis. Use Tableau, a leading business intelligence platform, to create robust visualizations and dashboards that communicate your findings.
For the first four weeks of this program, students will learn how to program in R for effective data manipulation and visualization. They will also develop the ability to create control structures, such as loops and conditional statements, to traverse, sort, merge, and evaluate data. The following eight weeks of the program will focus on techniques for data preparation — how to choose, create, and edit graphics, as well as best practices for presenting your visualizations.
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 is a non-credit, pass/fail program. To pass each course, you will need a cumulative score of at least 55%. Upon successful completion of both courses, participants will receive the NYU Stern Certificate in Coding + Visual Analytics.
Statistical ProgrammingGo beyond working with data in Excel. Learn a powerful statistical programming package to format, manipulate, analyze, and visualize data.
Data PresentationPresent your data so it is compelling and easy to understand.
Data Analysis and ReportingExplore, analyze, and share your data findings. Learn to build dynamic reports and interactive apps.
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 basic knowledge of Microsoft Excel and no prior experience in R or programming
The following agenda is a sample and subject to change.
- 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 (Available Digitally)Data Visualization Made Simple: Insights into Becoming Visual by Kristen Sosulski (Routledge)
Wickham, H. & Grolemund, G. (2018). R for Data Science. O'Reilly: New York. (free)
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.
Coding in R for Data
Week 1 (January 27th - February 2nd): Coding Basics
- Introduction to R Programming
- Data Structures, Variables, and Data Types
- Live Online Meetup 1: January 30th at 8:00 PM ET
Week 2 (February 3rd - February 9th): Data Exploration
- Packages, Scripts, and Rmarkdown
- Descriptive Statistics in R
- Live Online Meetup 2: February 6th at 8:00 PM ET
Coding in R for Data
Week 3 (February 10th - February 16th): Data Presentation
- Reporting and Visualization in Rmarkdown
- Data Cleaning and Formatting for Messy Data
- Live Online Meetup 3: February 13th at 8:00 PM ET
Week 4 (February 17th - February 21st): Data Application
- Functions, Iteration, and Conditionals
- Interactive Applications Using Rshiny
- Live Online Meetup 4: February 20th at 8:00 PM ET
- BREAK: February 24th - March 1st
Week 5 & 6 (March 2nd - March 15th): Essentials of Visualizing Data
- Live Online Meetup 5: March 5th at 8:00 PM ET
- Graphics + Data
- Live Online Meetup 6: March 12th at 8:00 PM ET
- Design + Audience
Weeks 7 & 8 (March 16th - March 29th): Visualizing Comparisons
- Categorical Data Graphics
- Categorical Data Types
- Live Online Meetup 7: March 19th at 8:00 PM ET
- Design Principles for Categorical Data Graphics
- Live Online Meetup 8: March 26th at 8:00 PM ET
- Pitch: The Report
Weeks 9 & 10 (March 30th - April 12th): Visualizing Locations
- Geospatial Data Graphics
- Geospatial Data Types
- Live Online Meetup 9: April 2nd at 8:00 PM ET
- Design Principles for Geospatial Data Graphics
- Live Online Meetup 10: April 9th at 8:00 PM ET
- Pitch: Web Dashboard
Weeks 11 & 12 (April 13th - April 26th): Visualizing Time
- Temporal Data Graphics
- Temporal Data Types
- Live Online Meetup 11: April 16th at 8:00 PM ET
- Design Principles for Temporal Data Graphics
- Live Online Meetup 12: April 23rd at 8:00 PM ET
- Pitch: The Presentation
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