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Data Services Class Descriptions

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

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

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This session is designed for those with a basic proficiency in MATLAB and interested in writing code that is more efficient and optimized.

Computer workstations with MATLAB 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: At least 6 months to a year of experience of writing MATLAB code
Skills Taught / Learning Outcomes:
  • Optimizations for speed: preallocation, suppressing output, variable types
  • Linear indexing, multidimensional matrices
  • Optimizations for speed: Logical indexing and find()
  • Optimizations for speed: Vectorization vs. Loops
  • Optimizations for speed: repmat() vs. (:,ones())
  • Optimizations for speed: strfind() vs. findstr()
  • Optimizations for speed: JIT accelerator
  • Optimizations for speed: Writing your own functions
  • bsxfun()
  • Initialize multiple variables: deal()
  • Scripting vs. Functions
  • Debugging
  • Profiler - time your code
Class Materials: 

Code Preview.pdf

Related Classes:

Data Visualization with Tableau

Data Cleaning Using OpenRefine

Introduction to Research Data Management

Introduction to Python

Introduction to R

Additional Training Materials:


Upcoming sessions for this tutorial