In the world we live in, data has become a significant tool that is used to solve problems and various industries have embraced it. Which best programming language to learn, either to gain a solid foundation of coding or to future-proof their skills, is a common concern of new data scientists. R and Python are the typical answers to this question. However, after some research, we have come to the conclusion that there may not be a single best tool to use and we would recommend the use of a toolbox approach to data science practices. And one of those tools that we will be discussing today is MATLAB.
Let’s start with getting to know more about MATLAB.
What is MATLAB?
MATLAB is a programming language similar to other well-known languages such as JAVA, python, C#, etc, which comes with its own IDE (Integrated Development) and set of libraries. MATLAB is an acronym for the word “Matrix Laboratory.” since it was originally known as the matrix programming language.
It is a programming language of the fourth generation. Cleve Moler, the department head of computer science at the University of New Mexico at the time discovered it first. He wanted to develop a different way for his students to learn and perform linear algebra and numerical computations without needing to use Fortran.
MATLAB is an advanced technical computing environment that combines computation, visualization, and development in one location With the benefit of having a wide range of mathematical functions at your disposal, such as statistics, linear algebra, and optimization techniques, among others, its interactive environment serves as a playground for developing, designing, and consuming applications.
What one can do with MATLAB?
- Analyze data
- Develop algorithms
- Create models and applications
What should you learn MATLAB for Data Science?
In the world of data science and machine learning, Python and R are more widely used in the field of data science and machine learning than MATLAB. This can be mostly due to the fact that it is not free. However, MATLAB makes data science simple, with tools to access and preprocess data, build machine learning and predictive models, and deploy models to enterprise IT systems,
Exploratory DATA analysis
The toolbox takes less time to preprocess data. From time-series sensor data to image to text, MATLAB datatypes significantly reduce the time required to preprocess data it makes it easy to align different time series, outliers may be replaced with interpolated values, noisy signals can be filtered, raw text can be turned to words, and much more. You can use plots and the Live Editor to quickly view your data to analyse patterns and spot problems with data quality.
Predictive Modeling
With the use of these toolboxes, you can implement logistic regression, classification trees, or support vector machines. You can use them for deep learning tools to implement convolutional neural networks and LSTM (Long Short-term Memory) networks on images, time series and data.
More Machine Learning with MATLAB
MATLAB offers a variety of methods to implement data science and machine learning models. You may utilise machine learning code in C/C++ code, enterprise IT systems, or the cloud with MATLAB. You can create standalone C code from your MATLAB code to create a high-performance prediction model with a small memory footprint and speedy prediction speed when performance is an issue. You may also export machine learning models to Simulink or install them on MATLAB Production Server for use with web, database, and enterprise applications.
MATLAB offers a variety of methods to implement data science and machine learning models. You may utilise machine learning code in C/C++ code, enterprise IT systems, or the cloud with MATLAB. You can create standalone C code from your MATLAB code to create a high-performance prediction model with a small memory footprint and speedy prediction speed when performance is an issue. You may also export machine learning models to Simulink or install them on MATLAB Production Server for use with web, database, and enterprise applications.
These data scientists‘ tools are some of the best available. Engineers, scientists, econometricians, and financial engineers with MATLAB training can keep taking use of the advantages that this advanced ecosystem provides.
