What Is A Data Science Platform

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As a software hub, a data science platform is relatively new. But its market is rapidly growing; a study shows that the market for the platform will reach USD$101.4 billion by the end of 2021. But what exactly is it?

Data Science Platform Definition

As mentioned, a data science platform is essentially a software hub. It’s where all the data science action takes place, and typically involves collecting and analyzing data from different sources. The platform also codes and builds models to turn data into something useful, implement those models into production, and deliver results.

Having a single location for data science is essential since data science jobs usually include different tools for each step of the development. For data science teams who have to work without a centralized platform hub, tool sprawl can be a problem.

With a data science platform, the whole data modeling process is the sole province of the data science teams. This way, they can concentrate on getting insights from data and conveying them to your company’s main stakeholders.

The platform has all the required tools for implementing a data science project’s lifecycle through different stages, like:

• Data exploration, integration, and ideation
• Model development

Data scientists also get invaluable help from the platform, which helps them through the different stages prior to the implementation of analytical models. All these tasks typically take a lot of engineering effort to create and maintain. The data science platform, however, gives the team a boost to accelerate analysis.

A company, however, may not always have its own data science platform, so some opt to buy a data science platform instead of building one. This content discusses thoroughly this topic.

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Data Science Platform Types

There are different types of data science platforms. They are:

• Closed Data Science Platform – In this type of platform, the data scientists only get to use the vendor’s programming language, modeling package, and GUI tools. This limits the tools that the scientists can use on the platform.
• Open Data Science Platform – This type of platform gives scientists a degree of flexibility to select any programming languages and packages they want to use, depending on what’s needed.

Reasons Why Companies Need Data Science Platform

Teams in a business almost always use some kind of software platform as support for their tasks. The engineering team and sales team, for example, have their own software platform. Data science should have its data science platform, too, so it can perform more efficiently. It shouldn’t depend on disorganized tools and disconnected engineering efforts to do its job.

A data science platform can bring together things that the team needs in a single place. This way, the data scientists can share resources and collaborate easily, accelerating the models’ implementation.

In addition, there are more reasons why companies need a data science platform:

• Simplify Collaboration Among Data Scientists

Providing a centralized platform hub for data scientists would prevent them from working needlessly on the same task. The platform would make sure that the team is working and collaborating efficiently. Having a flexible centralized hub, with the necessary tools data scientists require, would ensure efficiency and productivity.

Data visualizations, code libraries, and data models would be in one common accessible place. For data scientists, this would simplify project discussions. They could also reuse code and can share best practices. Fewer resources would be used and make the data repeatable and easily scalable.

• Reduce Engineering Effort

The platform could aid data scientists to deploy analytical models into production with no further effort from DevOps or engineering. The models wouldn’t have to be tested, refined, and integrated by the engineers. The data science platform would make sure the data models are accessible via an Application Programming Interface (API) so that data scientists wouldn’t have to depend on engineering efforts.

• Enable Faster Experimentation And Research

The data scientists wouldn’t have to contend with extra data management tasks if they know what others are working on and how they work. New hires could also integrate quickly with the data science team because it’s easier to preserve and keep track of people’s work via a centralized platform.

Conclusion

As a software hub, a data science platform would make the work of data scientists more efficient and could overcome the challenges that an unfocused team faces. The platform centralizes work and promotes collaboration, making project integration and implementation go smoothly.