While Data Science is excellent to deep dive into data which is vital for any field, including business, research or education, it is imperative to choose the right platform for the precise data study. Any Institutional or individual analytic needs to opt for a platform viable to sustain a business that can provide long-term solutions and is economical.  

What is a Data Science Platform

A Data Science platform is nothing but the hub to integrate Data Science activities. The best platform that works entirely on Data Science should support activities like data exploration, integrating resources that use the data should support coding and building models for catering to new data, managing resources in different environments and accurate reporting of results.  

With the current demands and scale of Data in businesses, the definition of the best platform that works entirely on Data Science requires the platforms to be scalable and flexible with changing requirements. Analytics is working with businesses to build platforms that are smart and efficient for best decision making. 

Apart from mentioned so far, the best platform that runs completely on data Science does tremendous support to Data scientists in interactive exploration, visualization, deployment, performance engineering data preparation and data access. Such platforms are boon to business as they act as the building block to create a solution and provide the environment for hassle-free incorporation of solutions into business processes and products.

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Data Science Platforms in the market 

Some of the most popular platforms that run on data science which are widely adopted all over the world are:

1. Microsoft’s Azure Machine-learning Studio

2. Alteryx Analytics

3. H2O.ai

4. KNIME Analytics Platform

5. RapidMiner

6. SAS

7. MathWorks’ MATLAB and Simulink

8. TIBCO Software

9. Databricks Unified Analytics Platform

10. Domino Data Science Platform

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Why MATLAB for Data Analysis?

MATLAB provides support to Data Science activities with exclusive tools for the purpose of accessing and preprocessing the data, building machine learning and predictive models, as well as creating deployment models for IT systems.  

The high-end features of MATLAB that differentiate it from other platforms:

• MATLAB supports the Accessibility of data from files, data historians. contemporary databases,  and also from cloud storage. It can also connect to sources  that are live as any hardware or real-time feeds that may carry business data of any organization.
• MATLAB has been designed with the ability of Data Management and Data cleanup. The Data types and capabilities  of preprocessing in regards to MATLAB help prepare interactive data, and its apps provide labelling service to build highly accurate training datasets.  
• Data analysis performed can be easily documented with MATLAB using graphics and Live Editor notebook features.
• MATLAB supports specific techniques for analysis with features as sensors, text, image, video, and other types of data.
• MATLAB provides support to different approaches to explore different data models with its machine learning and deep learning apps 
• MATLAB does fine-tune to machine learning and deep learning models with built-in modules like feature selection, model selection, and hyperparameter tuning algorithms.
• MATLAB models of machine learning can be deployed to live IT systems without rewriting code in any other language.

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Exploratory analysis with MATLAB

MATLAB is offering Data types that reduce the preprocessing time to a large extent for the data. For example, there is a significant drop in preprocessing time for time-series sensor data and image to text conversion when working with MATLAB.

High-level functions of MATLAB effectively synchronize unrelated time series, are capable of replacing the outliers with interpolating values and filter out the noise signals and much more. 

MATLAB helps the user to quickly visualize the data that is required to analyze trends and also highlight data quality issues in plots and the Live Editor tool

MATLAB for Machine Learning 

MATLAB caters best models for machine learning for all needs. MATLAB offers support to new users looking for help for starting off with machine learning or experts wanting  to swiftly evaluate several diverse types of models and the applications for classification , as well as regression in order to deliver swift results. 

Users are provided with a wide range  of regression  and classification algorithms that are popular,  and comparison of models can be made based on standard metrics and export of promising models for further analysis and integration. 

The users who prefer coding can may utilize hyperparameter optimization built into model training functions to find the best parameters to tune the model quickly.

Multi-Platform Deployment

MATLAB supported machine learning models can be deployed in any environment, like C/C++ code, CUDA code, corporate IT network, or the cloud network. MATLAB offers the generation of standalone C code from MATLAB code which supports high-performance requirements. The standalone code creates ready to deploy models which have high prediction speed and a small memory footprint.

MATLAB created Machine learning models can also be used in Simulink and can be deployed to MATLAB live production server to get integrated with the web, client databases, and underlying applications.

Integration of MATLAB to enterprise IT systems

The software programs written in MATLAB are ready to get deployed and this can be safely done along with integration to Organization’s IT systems, data sources and operational technologies.

IT solutions of Enterprises are programmed with coordination between Engineering and Software teams for the activities mentioned below:

•  To run the applications on Windows or Linux environments which ensures reliability, security, and also provides scalability to both in-house or on public clouds 
• Implementation of high grade security mechanism for authentication which includes providing access and data encryption. 
• Steps implemented to current networks and data, which includes current analysis platform systems like Tableau and Power BI.
• DevOps workflows are aligned along with currently implemented tools so as to set up auto deployment models, underlying algorithms, and applications to current systems with existing code.
• Helping users to get a quick start by implementing the prebuilt or industry-specific or Simulink provided tools .

Integrating Applications and Data

 Applications can be integrated with algorithms and models by implementing libraries of specific language or by getting service endpoints published using MATLAB Server. MATLAB supports the languages C/C++, Java, .NET, Python, and RESTful interfaces.

IT systems can get connected by MATLAB to allow engineering teams to set up connections to contemporary databases, Big Data, operational technologies, and streaming data sources by using prebuilt connectors. 

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Conclusion 

As the data is overflowing everywhere, the Data Science platforms are the need of the hour. The increase in adopting data analytical tools has surged the data science platform market like never before, and this competition is driving continuous innovations and enhancements to existing platforms.

Many industries have opted for MATLAB to maintain, manage, and preserve their data in recent years.  Since MATLAB offers a solution to current day requirements of Data analysis for business growth, it is most among businesses. It is widely used by industries such as information technology, healthcare and life sciences, banking, financial services, and insurance (BFSI), Research, Manufacturing, and Energy and Utilities. 

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