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Data Services Class Descriptions

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

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 Bobst Library, 5th floor

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

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

Computer workstations with SAS 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/downloading the software prior to the tutorial.

Duration: 90 min

Room description:

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:
  • SAS Interface (Editor, Libraries, Log, Results/Explorer)
  • Syntax (proc and data steps, semicolon, run;)
  • Importing data (manual, proc import, data step, import wizard)
  • Labels and formats vs. informats
  • Recoding variables with if/then statements and arrays
  • Creating new variables, transforming variables, dropping variables
  • Descriptive statistics, frequency tables, cross tabulations
  • Creating boxplot, scatterplot etc...
  • Basic analysis (t-test, correlation, ANOVA, regression)
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