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Data Services Class Descriptions

Information, materials, and schedules for all currently offered Data Services classes
This session is designed for those with a basic proficiency in MATLAB and interested in writing code that is more efficient and optimized.
Software: MATLAB
Duration: 90 min

Room description:

During the Fall 2021 semester, 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