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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:	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:	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.zip 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:	guides.nyu.edu/matlab
Feedback:	bit.ly/feedbackds

Upcoming sessions for this tutorial