Skip to Main Content

Data Services Class Descriptions

Information, materials, and schedules for all currently offered Data Services classes

NYU Data Services: NYU Libraries and Information Technology logo.




For assistance, reach out by chat below or submit a request

We can be reached by email at

Join our Discord server

If you've met with us before, tell us how we're doing

Service Desk and Chat

Staffed Hours: Spring 2023
   Mondays:        12pm - 5pm
   Tuesdays:       12pm - 5pm
   Wednesdays: 12pm - 5pm
   Thursdays:     12pm - 5pm
   Fridays:          12pm - 5pm

chat loading...

JMP is a dynamic software package for statistical analysis of data. This tutorial will introduce you to the basics of JMP and will cover creating and editing data sets, computing and recoding variables and provide an overview of commonly used statistical procedures.

Computer workstations with JMP installed are available for in-person tutorials in Bobst 617. For remote tutorials, while some patrons decide to approach tutorials as a demonstration of the software, other patrons approach tutorials with a more “hands-on” approach and wish to interact with the software during the tutorial. If the latter is the case, we recommend referencing our supported software page for additional information on accessing the software prior to the tutorial.

Duration: 90 min

Room description:

As our global pandemic continues, some tutorials are held remotely and require NYU sign on to access, while others are held in person, without a remote component. Please note the correct modality and location of the tutorial when registering

Prerequisites: Basic computer literacy, understanding files and folders
Skills Taught / Learning Outcomes:
  • JMP windows and layout
  • Importing data (.csv, Excel, and .jmp)
  • Preparing data for analysis (value labels, variable types, missing values)
  • Recoding variables (creating categories)
  • Computing and transforming variables
  • Importing data from CSV, Excel, .txt, SPSS, Stata files etc...
  • Exporting data as CSV
  • Subsetting datasets
  • Descriptive statistics, frequency tables, cross tabs
  • Creating dynamic graphics (histograms, scatterplots)
  • Basic Analysis (t-test, chi-square, correlation, ANOVA, regression)
  • Exporting output
Class Materials:


Material Preview

Related Classes:

Data Visualization with Tableau

Data Cleaning Using OpenRefine

Introduction to Research Data Management

Introduction to R

Additional Training Materials:


Upcoming sessions for this tutorial