Data Science Platform Market Know The Market Driving Factors 2032

Data Science Platform Market Overview

The data science platform market is a rapidly growing market, driven by the increasing demand for data-driven decision-making across industries. A data science platform is a software application that provides a comprehensive set of tools and features for the entire data science lifecycle, from data preparation and exploration to model development, deployment, and monitoring.

The global data science platform market is expected to reach USD 345.0 billion by 2030, growing at a CAGR of 19.20% during the forecast period. The market is segmented by component (platform and services), application (marketing & sales, logistics, finance and accounting, customer support, and others), industry vertical (BFSI, IT & telecommunication, retail & E-Commerce, healthcare, transportation, manufacturing, and others), and region (North America, Europe, Asia-Pacific, and LAMEA).

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Key Companies in the Data Science Platform Market:

Some of the key players in the data science platform industry include:

• Google
• Alteryx
• Microsoft
• IBM
• SAS
• DataRobot
• RapidMiner
• H2O.ai
• Databricks
• Cloudera
• Snowflake
• Teradata

Market Segmentation:

The data science platform market is segmented by component, application, industry vertical, and region.

Component

The component segment is further divided into platform and services. The platform segment is expected to account for the larger share of the market during the forecast period.

Application

The application segment is further divided into marketing & sales, logistics, finance and accounting, customer support, and others. The marketing & sales segment is expected to account for the largest share of the market during the forecast period.

Industry Vertical

The industry vertical segment is further divided into BFSI, IT & telecommunication, retail & E-Commerce, healthcare, transportation, manufacturing, and others. The BFSI segment is expected to account for the largest share of the market during the forecast period.

Region

The region segment is further divided into North America, Europe, Asia-Pacific, and LAMEA. North America is expected to account for the largest share of the market during the forecast period.

Regional Insights:

North America is the largest market for data science platforms, followed by Europe and Asia-Pacific. The growth of the market in North America is attributed to the early adoption of data science technologies and the presence of a large number of data science vendors. The growth of the market in Europe and Asia-Pacific is attributed to the increasing demand for data-driven decision-making from businesses in these regions.

Industry Latest News:

Here are some of the latest news and trends in the data science platform market:

• Google Cloud Platform launches new data science tools: Google Cloud Platform has launched a number of new data science tools, including Vertex AI Workbench, a unified platform for data science and machine learning development, and Vertex AI Pipelines, a managed service for building and running data pipelines.
• Microsoft Azure launches new data science services: Microsoft Azure has launched a number of new data science services, including Azure Databricks, a managed service for Apache Spark, and Azure Machine Learning Studio, a low-code/no-code platform for building and deploying machine learning models.
• AWS launches new data science services: Amazon Web Services (AWS) has launched a number of new data science services, including Amazon SageMaker, a managed service for building, training, and deploying machine learning models, and Amazon SageMaker Ground Truth, a managed service for human-in-the-loop machine learning.
• Data science platforms are becoming more accessible: Data science platforms are becoming more accessible to businesses of all sizes, thanks to the rise of cloud-based platforms and open source software.
• Data science platforms are becoming more integrated: Data science platforms are becoming more integrated with other enterprise applications, such as CRM and ERP systems. This is making it easier for businesses to use data science insights to improve their operations.

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