Introduction
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.
What is 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:
- Math and computation
- Algorithm development
- Modelling, simulation, and prototyping
- Data analysis, exploration, and visualization
- Scientific and engineering graphics
- Application development, including Graphical User Interface building
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.
Features of MATLAB
Following are the basic features of MATLAB −
- It is a high-level language for numerical computation, visualization and application development
- Provides an interactive environment for iterative exploration, design and problem-solving.
- Holds a vast library of mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, numerical integration and solving ordinary differential equations.
- It provides built-in graphics for visualizing data and tools for creating custom plots.
- MATLAB’s programming interface gives development tools for improving code quality maintainability and maximizing performance.
- It provides tools for building applications with custom graphical interfaces.
- It provides functions for integrating MATLAB based algorithms with external applications and languages such as C, Java, .NET and Microsoft Excel.
Why Use MATLAB in Data Science?
- Physical-world data: MATLAB has native support for the sensor, image, video, telemetry, binary, and other real-time formats. Explore this data using MATLAB MapReduce functionality for Hadoop, and by connecting interfaces to ODBC/JDBC databases.
- Machine learning, neural networks, statistics, and beyond: MATLAB offers a full set of statistics and machine learning functionality, plus advanced methods such as nonlinear optimization, system identification, and thousands of prebuilt algorithms for image and video processing, financial modelling, control system design.
- High-speed processing of large data sets. MATLAB’s numeric routines scale directly to parallel processing on clusters and cloud.
- Online and real-time deployment: MATLAB integrates into enterprise systems, clusters, and clouds, and can be targeted to real-time embedded hardware.
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
1. Accessing and Exploring Data
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
2. Preprocessing and Data Munging
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.
3. Developing Predictive Models
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.
4. Integrating Analytics with Systems
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.
MATLAB tutorials
- Getting started with MATLAB: https://in.mathworks.com/help/matlab/getting-started-with-matlab.html
- MATLAB for Data science course on Udemy: https://www.udemy.com/machine-learning-for-datascience-using-matlab/
- Machine Learning by Andrew Ng(using MATLAB):
https://www.coursera.org/learn/machine-learning - Youtube courses :
https://www.youtube.com/playlist?list=PL60D54836FB8893F0 - Running simulations in MATLAB
https://in.mathworks.com/help/simulink/run-simulation.html
Future of MATLAB in Science
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.
Summary
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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