MATLAB for Data Science

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Introduction

With the growth of Data science in recent years, we have seen a growth in the advancement of the tools for it. R and Python have been consistent languages used by individuals worldwide. Before R and Python, there was only one essential gamer and it was MATLAB. MATLAB is still in usage in many of the academics areas and mostly all the researchers throughout the world use MATLAB.

In this blog site, we will take a look at the factors why MATLAB is a good competitor to R and Python for Data science. We will talk about different courses which use data science with MATLAB.

What is MATLAB?

MATLAB is a high-performance language for technical computing. It incorporates computation, visualization, and shows in a user friendly environment where solutions and problems are revealed in familiar mathematical notation.

It is a programs platform, particularly for engineers and researchers. The heart of MATLAB is the MATLAB language, a matrix-based language enabling the most natural expression of computational mathematics.

Typical uses consist of:

  • 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 integrated math functions enable you to rapidly explore multiple techniques to come to a solution. MATLAB lets you take your concepts from research study to production by releasing to enterprise applications and ingrained gadgets, along with integrating with Simulink ® and Model-Based Design.

Features of MATLAB

Following are the standard 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 functions available for the whole information science analytical journey. Let us take a look at how MATLAB fits in every phase of a data science problem pipeline

1. Accessing and Exploring Data

The primary step in carrying out information analytics is to access the wealth of readily available data to check out patterns and establish much deeper insights. From a single integrated environment, MATLAB helps you gain access to information from a variety of sources and formats like different databases, CSV, audio, video etc

2. Preprocessing and Data Munging

When working with data from many sources and scientists, repositories and engineers require to preprocess and prepare information before establishing predictive designs. For instance, data may have missing out on values or incorrect values, or it might use various timestamp formats. MATLAB helps you simplify what might otherwise be time-consuming tasks such as cleaning data, dealing with missing information, removing noise from the information, dimensionality decrease, function extraction and domain analysis such as videos/audios.

3. Developing Predictive Models

Prototype and build predictive designs directly from data to forecast and forecast the probabilities of future outcomes. You can compare maker finding out techniques such as logistic regression, category trees, assistance vector machines, and ensemble techniques, and utilize model improvement and reduction tools to develop an accurate model that finest records the predictive power of your information. Usage flexible tools for processing monetary, signal, image, video, and mapping information to develop analytics for a range of fields within the exact same development environment.

4. Integrating Analytics with Systems

Integrate analytics established in MATLAB into production IT environments without needing to recode or produce custom facilities. MATLAB analytics can be packaged as deployable parts suitable with a vast array of advancement 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 business applications. For low latency and scalable production applications, you can handle MATLAB analytics running as a centralized service that is callable from numerous diverse applications. When they need to create a fast model and then for doing trial and mistake for validating a fresh idea, pendtag

MATLAB tutorials

  1. Getting started with MATLAB: http://in.mathworks.com/help/matlab/getting-started-with-matlab.html
  2. MATLAB for Data science course on Udemy: http://www.udemy.com/machine-learning-for-datascience-using-matlab/
  3. Machine Learning by Andrew Ng(using MATLAB):
    http://www.coursera.org/learn/machine-learning
  4. Youtube courses :
    http://www.youtube.com/playlist?list=PL60D54836FB8893F0
  5. Running simulations in MATLAB
    http://in.mathworks.com/help/simulink/run-simulation.html

Future of MATLAB in Science

People these days utilize MATLAB just. The real execution will never ever be made with MATLAB however with python, c++ or a comparable language. In my viewpoint MATLAB and python (or python libs) serve for various purposes. Scripting is just one function out of countless features in MATLAB however it is the only function in python. Individuals use both python and MATLAB scripts where in some other faculties people count on only MATLAB toolboxes with no scripting. Hence both python and MATLAB will exist in future however most likely the use of MATLAB “outdoors” might be minimized. MATLAB will exist up until we have a much better alternative of it.

Summary

MATLAB provides a lot of inbuilt utilities which one can directly apply in data science. MATLAB today finds it’s heavy use in the field of academics and research study. Although languages like R and Python are dominating information science worldwide, they are no other way near to the simplicity level which MATLAB has to offer. MATLAB will go a long way in the field of data science in the years to come. Additionally, learning MATLAB will be an excellent benefit for those who are prepared to pursue a career in research study!

Also, follow this link, if you are wanting to find out more about information science online!

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