Efficient Data Management & Analysis with MATLAB

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Course Highlights

This 2-day course is an intermediate-level course focusing on the details of data management, visualization technique and analysis techniques using Matlab, Statistics Toolbox and Curve Fitting Toolbox. This course is dealing with reading various formats of data files to producing customized publication-quality graphics. Emphasis is given to creating scripts that extend the basic features provided by the MATLAB language. It is also focusing on data analysis using Statistic Toolbox and Curve Fitting Toolbox. Hands-on examples explore features for efficiently organizing, presenting and analyzing data.
 
Topics include:

  • Importing Data
  • Organizing Data
  • Visualizing Data
  • Exporting Data
  • Data and Statistics
  • Probability and Distributions
  • Regression Analysis
  • Hypothesis Test

Course Objectives

 The aim of this course is to provide knowledge on data management, visualization and analysis using Matlab, Statistic Toolbox and Curve Fitting Toolbox. Themes of probability, distributions and regression analysis are explored throughout the course.

Who Must Attend

This hands-on course is designed for engineers, researchers, scientist, statisticians, who wish to implement efficient data management and analysis using Statistic Toolbox and Curve Fitting Toolbox in Matlab environment.

Course Benefits

Upon the completion of the course, the participants will be able to process, visualize and analyze data using Matlab, Statistic Toolbox and Curve Fitting Toolbox.

Prerequisites

"Comprehensive MATLAB" or equivalent experience using MATLAB and basic statistics.

Course Outline

Importing Data
Objective: Data files come in many formats, from spreadsheets to plain text. This first section of the course dissects the many facets of reading files of various types and formats. Emphasis is given to irregular text files, which contain a mixture of data types, delimiters, and headers.

  • File types and formats
  • Interactive import methods
  • Programmatic import methods
  • Low-level import methods
  • Importing a mixture of data types from text files using textscan
  • Large data sets and irregular formats
  • Batch import tasks

Organizing Data
Objective: There are many ways to store data in the MATLAB environment. This section explores the tradeoffs involved in choosing an appropriate data type, highlighting the built in data types of cell arrays and structures. Additionally, this section demonstrates some of the common techniques for organizing and processing data.

  • Cell arrays
  • Set operations
  • Function handles
  • Applying functions to an array
  • Structure arrays
  • Extracting and aggregating data
  • Finding and counting

Visualizing Data
Objective: A good visualization can effectively communicate the results of an analysis. Using the plotting capabilities of MATLAB, this section aims to investigate many different techniques for presenting data. Highlighted are techniques for annotating and modifying standard plots into publication-quality graphics.

  • Choosing a plot type
  • Using color
  • Customizing plots
  • Handle Graphics®
  • Common plot applications
  • Creating animations
  • Writing user-defined plot functions

Exporting Data
Objective: A final stage of a successful data analysis includes the publication of results. Whether publishing a presentation, a report, or a simple text file for further analysis, this section will help in outputting results in various formats. Additional guidance in publishing to various graphical formats is also a feature of this section.

  • Writing numeric data to text files
  • Writing a mixture of data types to text files
  • Saving data to other file formats
  • Exporting and printing graphics
  • Saving animations
  • Publishing

Data and Statistics
Objective: Learn to compute basic descriptive statistics and visualize data in a variety of ways.

  • What is Statistics?
    • Statistical sampling and modeling
    • Statistical questions
    • Data analysis
  • Working with data
    • Incommensurate data
    • Missing data
  • Descriptive Statistics
    • Measures of center, spread and shape
  • Statistical plotting
    • Histograms, scatter plots, and box plots
    • Grouped data

Probability and Distributions
Objective: Review the basics of probability and random variables and explore the variety of probability distributions available in the Statistics Toolbox

  • Probability Concepts
    • Probability measures
    • Random variables
    • Probability distributions
  • Distribution Concepts
    • Discrete distributions
    • Continuous distributions
    • Distributions in the Statistic Toolbox
    • Distribution parameters
    • Computing probabilities
  • Data and Distributions
    • Sampling distributions
    • Choosing a distributions
    • Parameter estimation
    • Nonparametric density functions
    • Bootstrapping and simulation
    • Distribution testing

Regression Analysis
Objective: Explore regression analysis for bivariate data

  • Regression Concepts
    • Predictors and responses
    • Linear and nonlinear models
    • Correlation and covariance
  • Linear Methods
    • Quantiles and quantile plots
    • Solving systems of linear equations with the backslash operator
    • Polynomial fitting
    • Graphical user interface tools for linear regression
    • Curve fitting toolbox
    • Generalized linear models
  • Nonlinear Methods
    • Nonlinear fitting
    • Graphical user interface tools for nonlinear regression
    • Using the Curve fitting toolbox for nonlinear regression

Hypothesis Test
Objective: Explore the various hypothesis tests within the Statistic Toolbox

  • Terminology
  • Assumptions
  • Tests in the Statistic Toolbox

Exercises

  • Hands-on exercises
  • Mini Project
  • Case Studies