Data science platform and its types: How does a business thrive over a data science platform?

Data science platform and its types: How does a business thrive over a data science platform?

We stand in an era which has been popularly referred to as the era of big data analytics. There is hardly a sector or an application that does not depend upon data. Data science is the lifeline of different businesses that operate in the age of industry 4.0. As such, it is pertinent for every prosperous business to have a robust data science platform. Data science platform is a toolkit that helps in dealing with the entire lifecycle of data ranging from data collection to data analytics. Let us dig into this concept in deeper detail.

Data platform and its types 

A data science platform can be understood in terms of three important phases of a data science project. These three phases include the exploration phase, the development phase, and the deployment phase of the model. These three stages are time consuming but they determine the success of any data science project. With the help of a data science platform, it is easy to track the various stages in the life cycle of data science and strive to improve their operational efficacy. This can be done by making use of power tools that are provided by a data science platform.

Data science platforms can be broadly classified into the open type and the closed type. Open data science platforms are more flexible in nature. This is because they provide data scientists the freedom to choose a programming language of their choice. In addition to this, data scientists can also engage with a broad range of customized tools and techniques while working with this platform. On the other hand, the options related to programming language, graphical user interface, and data modeling are restricted in a closed platform. In this platform, a data scientist has to work with the platform provided by the vendor which restricts experimentation of outside tools.

How does a business thrive over a data science platform? 

A business is dependent upon a data science platform at various levels of operation. In fact, the management as well as the critical decisions of a business also require a data science platform to some extent. A data science platform not only helps in scaling up business operations but also helps in synchronizing business processes and establishing coordination between various projects.

In addition to this, a data science platform also helps in minimizing the efforts of system engineers and software developers. It helps in fabrication of different analytical models that project the growth prospects of a business in changing market dynamics. Devops, or in other words, model development, testing, and deployment are also dependent upon such a platform. The research and development that takes place in an organization and deals with various types of management tasks can be accentuated through this platform.

The value of a data science platform

  • A data science platform is of great value to a business organization. It helps in diagnosing and fixing the defects in various systems with a lot of ease.
  • It helps business professionals within an organization to understand historical work done on a project so that they don’t have to start from scratch.
  • It provides a wide range of tools, techniques, and packages to improve the functionality of different business processes.
  • In addition to this, it also helps in reproducing any type of previous work that we feel may be helpful in the future.
  • A data science platform also has a great role to play in data visualization as it helps in presenting the most complex results in a very simplified manner in the form of engaging dashboards and reports.
  • A data science platform provides a great interface for complex computations that are related to voluminous amounts of data. These computations are difficult to handle with traditional methodologies. 

Data platforms in action

There are a number of data science platforms that are in wide operation in the present times. Wolfram’s data science platform is one of the best among the lot. An engaging data science platform is also provided by gathr.one. Domino data lab science platform is also competing with the wolfram data platform that stands at the top. In addition to this, the Rapid Miner data science platform is the best open-source platform available. Finally, the Google cloud platform cannot be ignored as it is the most widely used platform by data scientists around the world.

Concluding remarks

The best data science platform is one that helps data scientists overcome the challenges of a data science project in a short span of time with a wide range of tools.

 

sharma niti

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