According to a new report by Allied Market Research, titled, Data Science Platform Market by Type and End User: Global Opportunity and Forecast, 2017-2023, the data science platform market was valued at $19,621 million in 2016, and is projected to reach at $183,688 million by 2023, growing at a CAGR of 39.6% from 2017 to 2023.

Portland, OR -- (SBWire) -- 02/01/2018 --The phrase "Data Science Platforms" is the most talked about topic in data science conferences, meet-ups and top publications these days. However, the concept of data science platforms is not new in the big data space but still many do not know what is a data science platform, why a company needs a data science platform, what are the best data science platforms out there in the market. Data science platforms are the buzzword of 2017. This blog walks you through answers to the following questions –what is a data science platform, what are the features of a good data science platform, why a company needs a data science platform and list of some of the best data science platforms available today in the market.

The easiest way of defining a data science platform – "A data science platform is a framework of the entire life cycle of a data science project.

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Data science platform contains all the tools required for executing the lifecycle of the data science project spanning across different phases –

Data ideation, integration and exploration

Model Development
Model Deployment

A data science platform helps data scientists enhance their analysis by helping them run, track, reproduce, share and deploy analytical models faster. Usually, all these tasks require lot of engineering effort and hassle to build and maintain analytical models but a data science platform gives you the extra "power tools" to speed up analysis. Data science platforms give the data science teams a leg up in the competitive race to leverage analytics effectively.

Types of Data Science Platforms
Data science platforms are categorized as –

i) Open Data Science Platform
Open data science platforms are the one that provide data scientists with the flexibility to choose the programming languages and packages they want to use as per their requirements. An open data science platform allows data scientists to use the right tools for the right job based on the situation and also lets them experiment with different languages and tools.

ii) Closed Data Science Platform

Closed data science platforms are the one wherein data scientists have to use the vendor's platform specific programming language, GUI tools and modelling packages. This restricts data scientists on the tools that can be used on top of the platform.

Why your company needs a data science platform?

Every team in an organization uses some kind of a software platform to support their workflow – just like the engineering team of a company uses source code control systems, the sales team of a company uses CRM systems and the customer support team uses ticketing system. Similarly, to perform data science at scale, organizations need to rely on data science platforms. It's time for companies to bid adieu to data science processes that depend on disjointed tools and widespread engineering effort to perform data science. Data science platforms bring everything that a data science team needs at a centralized place so that data scientist can pool resources and team up effectively speeding up the process of deploying models instantly.

Challenges Data Scientists Encounter in the Lifecycle of a Data Science Project

1. Data science process begins with exploring the data to understand what is on the plate for analysis.

2. Ideation and exploration can be a time consuming process if you do not know what other team members have already accomplished as you might be redoing the same thing.

3.Data scientists run experiments to test different ideas, review the output and make changes. This phase of the data science workflow is likely to slow down in the absence of a data science platform if the experiments performed are computationally intensive.

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It is necessary to operationalize data science work to gain value from the outcomes of analysis. This requires engineer resources incurring additional costs and increases the time to market.