MATLAB for Data Processing and Visualization

Course Highlights

This one-day course focuses on importing and preparing data for data analytics applications. The course is intended for data analysts and data scientists who need to automate the processing, analysis, and visualization of data from multiple sources. Topics include:

  • Importing data
  • Processing data
  • Customizing visualizations
  • Working with irregular data

Course Objectives

The aim of this course is to equip those who need to process mixture data types from arbitrarily formatted text data and irregular data and to customized visualization of the data.

Who Should Attend

This course is intended for engineer, researchers, data scientists, and managers, who are involved in the process of preprocessing of the mixture data types measurement data and irregular data and to create customized

Prerequisites

Attended Comprehensive MATLAB course or equivalent experience in using MATLAB

Course Outline

Day 1 of 1

Importing Data

Objective: Read text files that contain a mixture of data types, delimiters, and headers.

  • Import a mixture of data types from arbitrarily formatted text files
  • Import only required columns of data from a text file
  • Import and merge data from multiple files

Processing Data

Objective: Process raw imported data by extracting, manipulating, aggregating, and counting portions of data.

  • Process data with missing elements
  • Create and modify categorical arrays
  • Aggregate, bin, and count groups of data

Customizing Visualizations

Objective: Annotate and modify standard plots to produce informative customized graphics.

  • Determine properties of graphics objects and their associated values
  • Locate and manipulate graphics objects
  • Customize plots by modifying properties of graphics objects

Working with Irregular Data

 Objective: Import and visualize scattered data from text files with irregular formatting.

  • Parse text files to determine formatting
  • Import data from separate sections of a text file
  • Extract data from container variables
  • Interpolate irregularly spaced three-dimensional data
  • Visualize three-dimensional data in two and three dimensions

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