With the growth of Data science in recent years, we have seen a growth in the development of the tools for it. R and Python have been steady languages used by people worldwide. But before R and Python, there was only one key player and it was MATLAB. MATLAB is still in usage in most of the academics areas and mostly all the researchers throughout the world use MATLAB.
In this blog, we will look at the reasons why MATLAB is a good contender to R and Python for Data science. Furthermore, we will discuss different courses which offer data science with MATLAB.
MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.
It is a programming platform, specifically for engineers and scientists. The heart of MATLAB is the MATLAB language, a matrix-based language allowing the most natural expression of computational mathematics.
Typical uses include:
The language, apps, and built-in math functions enable you to quickly explore multiple approaches to arrive at a solution. MATLAB lets you take your ideas from research to production by deploying to enterprise applications and embedded devices, as well as integrating with Simulink® and Model-Based Design.
Following are the basic features of MATLAB −
Also, MATLAB finds its features available for the entire data science problem-solving journey. Let us have a look at how MATLAB fits in every stage of a data science problem pipeline
The first step in performing data analytics is to access the wealth of available data to explore patterns and develop deeper insights. From a single integrated environment, MATLAB helps you access data from a wide variety of sources and formats like different databases, CSV, audio, video etc
When working with data from numerous sources and repositories, engineers and scientists need to preprocess and prepare data before developing predictive models. For example, data might have missing values or erroneous values, or it might use different timestamp formats. MATLAB helps you simplify what might otherwise be time-consuming tasks such as cleaning data, handling missing data, removing noise from the data, dimensionality reduction, feature extraction and domain analysis such as videos/audios.
Prototype and build predictive models directly from data to forecast and predict the probabilities of future outcomes. You can compare machine learning approaches such as logistic regression, classification trees, support vector machines, and ensemble methods, and use model refinement and reduction tools to create an accurate model that best captures the predictive power of your data. Use flexible tools for processing financial, signal, image, video, and mapping data to create analytics for a variety of fields within the same development environment.
Integrate analytics developed in MATLAB into production IT environments without having to recode or create custom infrastructure. MATLAB analytics can be packaged as deployable components compatible with a wide range of development environments such as Java, Microsoft .NET, Excel, Python, and C/C++. You can share standalone MATLAB applications or run MATLAB analytics as a part of the web, database, desktop, and enterprise applications. For low latency and scalable production applications, you can manage MATLAB analytics running as a centralized service that is callable from many diverse applications.
People these days use MATLAB only when they need to create a quick prototype and then for doing trial and error for validating a fresh concept. The real implementation will never be made with MATLAB but with python, c++ or a similar language. In my opinion MATLAB and python (or python libs) serve for different purposes. Scripting is just one feature out of thousands of features in MATLAB but it is the only feature in python. People use both python and MATLAB scripts where in some other faculties people rely on only MATLAB toolboxes with zero scripting. Hence both python and MATLAB will exist in future but most probably the usage of MATLAB “outside” may be reduced. MATLAB will exist until we have a better alternative of it.
MATLAB provides a lot of inbuilt utilities which one can directly apply in data science. Furthermore, MATLAB today finds it’s heavy usage in the field of academics and research. Although languages like R and Python are dominating data science worldwide, they are no way near to the simplicity level which MATLAB has to offer. Also, MATLAB will go a long way in the field of data science in the years to come. Additionally, learning MATLAB will be a great bonus for those who are willing to pursue a career in research!
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