Exploring Data Analysis Strategies in MATLAB

MATLAB is an exceptionally versatile software for data analysis. A numerical computing framework and fourth-generation language clubbed into one, MATLAB is an interpretive language & framework that boasts powerful & sophisticated data analysis. Along with R and Python, MATLAB is one of the most popular languages & frameworks used for data analysis, finding immense applications in data science, analytics, and AI. 

If you are working on a data analysis assignment in MATLAB, give this write-up a thorough read. It offers some crack insights from professional assignment help experts regarding the most prominent exploratory data analysis strategies used in data science & analytics. 

Dive right in. 

Exploratory Data Analysis using MATLAB

• Linear Methods of Dimensionality Reduction

This is all about finding an appropriate low-dimensional space to represent some data accurately. Dimensionality reduction is especially effective for exploring & analyzing high-dimensional data with the intent to identify patterns & trends within and carrying out advanced statistical analysis. It is a tough concept to master, and if you are struggling with it in your assignments, drop a “Do my MATLAB assignment” at MyAssignmentHelp.com, USA’s largest academic service provider.

Principal Component Analysis (PCA) 🡪  PCA is among a family of techniques that uses dependencies among intrinsic variables to represent higher-dimensional data into easily manipulatable lower-dimensional forms. Say we start with data comprising p-dimensional vectors, and need to summarize them by projecting them onto a q-dimensional subspace (p>q). The q-dimensional vectors, thus, formed in the q-dimensional sub-space, are the principal components of the original data.

If you wish to conduct principal component analysis of raw data in MATLAB, the command to conduct the analysis is pca(). For example, if you have a n x p matrix named A, then PCA onto A will generate the principal component coefficients in a p x p coefficient matrix. 

Each matrix column contains the coefficients of a single principal component and is arranged in descending order of the component variance. 

• Non-Linear Dimensionality Reduction Methods

A common non-linear dimensionality reduction technique is multidimensional scaling. It comprises a set of methods that enable intuitive and accurate visualizing of higher dimensional information. The methods involve using similarity or dissimilarity measures/metrics to represent data in lower dimensions. 

One of the biggest advantages of multidimensional scaling is that it does not require raw data. You can obtain dimensionality reduction of a data set using just a matrix of distances or dissimilarities among data. 

In MATLAB, the command midscale carries our multidimensional scaling. Say we have a n x n dissimilarity matrix D. Then, the command Y = mdscale (D, p) generates a n x p dimensional matrix Y, whose points or elements are approximate transformations of the corresponding dissimilarities in matrix D. 

• Finding Similar Data Clusters

Cluster analysis is a vital aspect of data analysis and mining. It involves organizing/segregating datasets into groups in such a manner so that the intra-cluster dissimilarities are lower than inter-cluster dissimilarities. Naturally, the manner of data representation and the selection of distance metrics play critical roles in the overall accuracy of the clustering process.

MATLAB supports different clustering techniques across various domains and is considered an unsupervised learning technique in machine learning. One of the most commonly used techniques is k-means clustering. So, if you want to partition the elements in a n x p data matrix A into k clusters, you need to write X = kmeans (A, k). X, thus generated, is a n-dimensional vector that contains the indices of all the clusters of the original observation. 

By default, MATLAB’s k-means clustering groups data using the squared Euclidean distance metric. 

There are numerous other analysis algorithms that you can implement in MATLAB. Unfortunately, we do not have the time or space to dig into them all. Here’s an awesome book on Exploratory Data Analysis accredited by MathWorks themselves. 

We wrap things up with quick tips to make your EDA codes run faster & smoother in MATLAB. 

Tips to Improve the Performance of your MATLAB Codes

• Note the background processes that drain heavy resources. MATLAB also utilizes large amounts of computational resources when carrying out EDA or any sophisticated statistical analysis. Running them in tandem will slow things down drastically. 
• Use functions more than scripts. Use scripts as less as possible since they are much slower than functions. 
• Employ local functions instead of nested functions. 
• Engage in modular programming to avoid creating large files. 
• Create new variables when changing a data type.
• Avoid using global variables as much as possible. 
• DO NOT use data as code.
• It is best to use parallel or GPU computing when working with vast volumes of data.

Well, that’s all the space we have for today. Hope this was an interesting read for everyone. Put in some solid effort, as data analysis is difficult to master. Connect with experts at MyAssignmentHelp.com in case you need any help.

All the best!