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市场调查报告书
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1889277

资料科学市场预测至2032年：按组件、部署类型、技术、应用、最终用户和地区分類的全球分析

Data Science Market Forecasts to 2032 - Global Analysis By Component (Software Platforms, Tools & Frameworks and Services), Deployment, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球资料科学市场价值将达到 1598.9 亿美元，到 2032 年将达到 11585.6 亿美元，在预测期内的复合年增长率为 32.7%。

资料科学是一门跨学科领域，致力于透过统计分析、机器学习演算法和高级分析技术，从庞大而复杂的数据集中提取有意义的资讯。它融合了程式设计能力、专业知识和视觉化技术，用于发现模式、预测结果并指南明智的策略决策。数据科学正在对金融、医疗保健、零售和通讯等行业产生重大影响，帮助企业简化流程、分析客户行为并开发创新解决方案。随着以数据为中心的方法对业务成长变得至关重要，对熟练数据科学家的需求持续增长，资料科学已成为现代数位环境中最具影响力和发展最快的领域之一。

根据 Anaconda 发布的《2024 年资料科学现况报告》，87% 的从业人员正在扩大人工智慧的应用范围，包括资料清洗、任务自动化和预测建模。此外，49% 的公司正在设立人工智慧资料分析师职位，46% 的公司正在设立新的人工智慧工程师职位，这表明资料科学正在改变劳动力市场。

数据量快速成长

数位生态系统、物联网设备和云端基础应用的爆炸性成长极大地加速了全球数据生成，并成为资料科学市场的主要驱动力。企业如今正从用户行为、智慧感测器、财务活动和营运系统中累积大量信息，并日益依赖先进的分析技术。资料科学将非结构化数据转化为可执行的洞察，有助于提升商务策略和绩效。随着各组织竞相创新并快速回应不断变化的环境，分析大量资料集的能力变得至关重要。数据产生的持续成长正显着推动各产业对资料科学平台和服务的采用。

熟练的资料科学专业人员短缺

资料科学市场面临的一大限制因素是训练有素的资料科学专业人才长期短缺，这给寻求专业分析技术的公司带来了挑战。资料科学职位需要精通程式设计、统计学、机器学习和技术专长，导致人才库规模小且竞争异常激烈。这种人才缺口阻碍了高阶分析技术的应用，减缓了业务流程，并推高了招募成本。儘管许多组织都在投资提昇员工技能，但数据技术的快速发展仍在加剧技能短缺。随着企业越来越依赖数据驱动型策略，合格人才的匮乏仍是市场扩张的一大障碍。

扩展产业专用的资料科学解决方案

产业专用的资料科学应用的兴起为市场扩张带来了巨大机会，因为各行各业都需要专门针对自身营运需求而建构的分析工具。医疗保健、金融、製造和零售等行业越来越依赖嵌入专业知识的解决方案来提高准确性、效率和合规性。客製化系统支援个人化治疗、风险评分、维护预测、供应链规划和价格优化等应用情境。跨产业加速的数位转型正在推动对专业资料科学平台的需求，促使技术提供者提供专业化的高价值解决方案。这种产业专用的方法使供应商能够获得竞争优势，并有效地满足特定客户的需求。

技术创新速度远超人力资源能力。

资料科学技术的快速发展带来了巨大的威胁，因为企业往往缺乏跟上步伐所需的专业人才。人工智慧、机器学习、巨量资料框架和自动化技术等领域的新技术层出不穷，需要企业持续学习和适应。许多公司无法及时培训员工，导致技能过时、模型效能欠佳，计划执行停滞不前。快速更新的工具使得旧系统更快过时，并增加了升级成本和复杂性。如果企业不持续提升技能和加大投资，就有可能失去竞争优势。这种日益扩大的能力差距限制了各行业资料科学实施的效率和扩充性。

新冠疫情的影响：

新冠疫情显着加速了资料科学市场的发展，企业迅速采用数位化解决方案、远距工作流程和进阶分析技术。各组织机构高度依赖预测模型、即时仪錶板和预测系统来应对供应链挑战、医疗压力和消费行为的变化。资料科学科学使政府和企业能够分析感染趋势、有效分配资源并改善紧急应变计画。由于对扩充性和灵活基础设施的需求增加，此次危机也推动了云端基础的分析技术的发展。儘管一些行业推迟了主要的IT支出，但疫情最终强化了资料科学作为业务连续性和未来发展准备的关键工具的价值。

预计在预测期内，软体平台细分市场将占据最大的市场份额。

预计在预测期内，软体平台细分市场将占据最大的市场份额，显着超越服务细分市场。根据多项行业研究，该细分市场贡献了超过80%的总收入。企业正专注于建立一体化平台，以管理从资料收集、模型建置到配置和监控的所有环节，这推动了相关领域的巨额投资。这些平台解决方案对资料科学团队至关重要，因为它们提供了可扩展的基础架构和简化的工作流程。因此，软体平台的主导地位将成为推动资料科学市场成长的核心因素。

预计在预测期内，医疗保健和生命科学领域将实现最高的复合年增长率。

在预测期内，医疗保健和生命科学领域预计将实现最高成长率，这主要得益于人工智慧驱动的洞察、自动化和预测分析技术的日益普及。医疗服务提供者和研究人员越来越依赖先进的数据技术来提高诊断准确性、支持实证医学并加速科学发现。对精准医疗、基因分析和持续病患监测的日益重视，推动了该领域对强大分析系统的需求。此外，数位医疗的快速普及、电子健康记录的整合以及远端医疗应用，正在产生丰富的数据集，促使医疗机构优先投资于先进且扩充性的资料科学解决方案。

占比最大的地区：

预计北美将在预测期内占据最大的市场份额，这得益于其先进的数位化环境、较高的企业准备度以及对智慧分析解决方案的早期采用。该地区汇集了许多大型科技公司、云端平台和人工智慧开发商，加速了创新进程，并拓展了资料科学的应用场景。各关键产业的企业越来越依赖分析工具来优化营运和进行策略决策。强大的研究实力、对新兴技术的大量投入以及有利的法规环境，都巩固了北美的市场主导地位。此外，巨量资料架构、自动化和机器学习的日益普及，也提升了企业的能力，确保北美继续保持在资料科学成长和投资领域的领先地位。

复合年增长率最高的地区：

由于数位转型加速、云端生态系不断扩展以及在分析和人工智慧领域的应用日益广泛，亚太地区预计将在预测期内实现最高的复合年增长率。印度、中国、新加坡和韩国等国家正优先推行以数据为中心的政策，旨在提高营运效率并加速创新。网路连线的改善、行动装置的普及以及关键产业数据产生量的增加，正在刺激对预测分析和智慧工具的需求。政府的支持性政策、不断完善的数位基础设施以及充满活力的、专注于巨量资料和人工智慧解决方案的Start-Ups环境，进一步推动了该地区的发展势头，使亚太地区成为资料科学领域增长最快的中心。

免费客製化服务：

购买此报告的客户可以选择以下免费自订选项之一：

  • 公司概况
    • 对其他市场公司（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户要求，对主要国家的市场规模进行估算和预测，併计算复合年增长率（註：可行性需确认）。
  • 竞争基准化分析
    • 根据主要企业的产品系列、地理覆盖范围和策略联盟基准化分析

目录

第一章执行摘要

第二章 前言

  • 摘要
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球资料科学市场（按组件划分）

  • 软体平台
  • 工具和框架
  • 服务

第六章 全球资料科学市场依部署类型划分

  • 本地部署
  • 云端原生
  • 杂交种

7. 全球资料科学市场（依技术划分）

  • 机器学习和深度学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 预测分析与规范分析
  • 数据工程和管道

第八章：全球资料科学市场（按应用划分）

  • 商业智慧和视觉化
  • 客户分析与个人化
  • 诈欺侦测和风险管理
  • 医学诊断与基因组学
  • 供应链优化
  • 物联网和边缘分析

9. 全球资料科学市场（按最终用户划分）

  • 资讯科技/通讯
  • BFSI
  • 医学与生命科​​学
  • 零售与电子商务
  • 製造业/汽车
  • 能源与公共产业
  • 政府/国防
  • 教育/研究

第十章：全球资料科学市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：企业概况

  • Google
  • Microsoft
  • Amazon
  • IBM
  • Fractal Analytics
  • Mu Sigma
  • Accenture
  • Cloudera
  • Nvidia
  • Databricks
  • Tiger Analytics
  • LatentView Analytics
  • Teradata
  • Deloitte
  • Tata Consultancy Services（TCS）
Product Code: SMRC32737

According to Stratistics MRC, the Global Data Science Market is accounted for $159.89 billion in 2025 and is expected to reach $1158.56 billion by 2032 growing at a CAGR of 32.7% during the forecast period. Data Science is an interdisciplinary domain dedicated to uncovering meaningful information from vast and intricate datasets through statistical analysis, machine learning algorithms, and advanced analytical practices. It combines programming abilities, subject expertise, and visualization techniques to detect patterns, predict outcomes, and guide well-informed strategic decisions. The field significantly influences sectors like finance, healthcare, retail, and telecommunications by helping organizations streamline processes, analyze customer behavior, and develop innovative solutions. As data-centric approaches become essential for business growth, the demand for skilled data scientists continues to rise, positioning data science as one of the most influential and fast-evolving fields in the modern digital landscape.

According to the Anaconda State of Data Science 2024 Report, data shows that 87% of practitioners are increasing AI adoption, with applications in data cleaning, task automation, and predictive modeling. Additionally, 49% of companies are adding AI Data Analysts and 46% are creating new AI Engineering roles, demonstrating workforce transformation driven by data science.

Market Dynamics:

Driver:

Growing volume of data

The explosive growth of digital ecosystems, IoT devices, and cloud-based applications has dramatically accelerated global data creation, making it a primary catalyst for the Data Science Market. Companies now accumulate extensive information from user behavior, smart sensors, financial activities, and operational systems, leading to greater reliance on sophisticated analytical methods. Data science helps convert unstructured data into actionable intelligence, strengthening business strategies and performance. As organizations compete to innovate and react quickly to changing conditions, the capability to analyze massive datasets becomes essential. This continuous rise in data production significantly drives the implementation of data science platforms and services across multiple sectors.

Restraint:

Shortage of skilled data science professionals

A significant limitation in the Data Science Market is the persistent shortage of trained data science professionals, leading to difficulties for companies seeking specialized analytical expertise. Data science roles require proficiency in programming, statistics, machine learning, and domain knowledge, resulting in a small and highly competitive talent pool. This gap hinders the adoption of advanced analytics, slows operational workflows, and drives up recruitment expenses. Although many organizations invest in upskilling, the fast-paced advancement of data technologies keeps widening the skills deficit. As firms increasingly depend on data-driven strategies, the lack of qualified professionals remains a major barrier to broader market expansion.

Opportunity:

Expansion of industry-specific data science solutions

The rise of industry-tailored data science applications offers strong opportunities for market expansion, as various sectors seek analytics tools built specifically for their operational needs. Healthcare, finance, manufacturing, and retail increasingly rely on solutions that incorporate domain expertise to improve accuracy, efficiency, and regulatory compliance. Customized systems support use cases such as personalized treatments, risk scoring, maintenance forecasting, supply chain planning, and pricing optimization. As digital transformation accelerates across industries, the demand for specialized data science platforms grows, encouraging technology providers to deliver focused, high-value solutions. This industry-specific approach enables vendors to strengthen competitiveness and address niche customer requirements effectively.

Threat:

Rapid technological changes outpacing workforce capabilities

The rapid evolution of data science technologies creates a significant threat, as organizations often lack the skilled workforce required to keep up. New advancements in AI, machine learning, big data frameworks, and automation appear frequently, demanding continuous learning and adaptation. Many companies struggle to train employees fast enough, resulting in outdated skills, suboptimal model performance, and stalled project execution. Fast-changing tools also make older systems irrelevant more quickly, adding upgrade costs and complexity. Without sustained upskilling and investment, businesses risk losing competitive advantage. This widening capability gap limits the efficiency and scalability of data science deployments across industries.

Covid-19 Impact:

The COVID-19 pandemic played a major role in accelerating the Data Science Market as businesses quickly adopted digital solutions, remote workflows, and advanced analytics. Organizations relied heavily on predictive models, real-time dashboards, and forecasting systems to navigate supply chain challenges, healthcare pressures, and shifts in consumer behavior. Data science enabled governments and enterprises to analyze infection trends, allocate resources efficiently, and improve emergency response planning. The crisis also boosted cloud-based analytics due to higher demand for scalable, flexible infrastructure. Although some industries postponed major IT spending, the pandemic ultimately reinforced the value of data science as an essential tool for operational continuity and future preparedness.

The software platforms segment is expected to be the largest during the forecast period

The software platforms segment is expected to account for the largest market share during the forecast period, significantly outpacing the services segment. According to various industry studies, this component contributes more than eighty percent of the total revenue. Businesses are gravitating toward all-in-one platforms that manage everything from data collection and model building to deployment and monitoring, driving heavy investment. These platform solutions provide scalable infrastructures and streamlined workflows, making them essential for data science teams. Therefore, the dominance of software platforms is a central anchor of the data science market's growth.

The healthcare & life sciences segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare & life sciences segment is predicted to witness the highest growth rate due to its expanding use of AI-enabled insights, automation, and predictive analytics. Healthcare providers and researchers increasingly depend on advanced data techniques to enhance diagnostic accuracy, support evidence-based care, and accelerate scientific discovery. The rising emphasis on precision medicine, genetic profiling, and continuous patient monitoring fuels the sector's need for robust analytics systems. Moreover, the surge in digital health adoption, integration of electronic medical records, and widespread telehealth usage generates rich datasets, prompting healthcare institutions to prioritize investment in advanced, scalable data science solutions.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by its advanced digital landscape, strong enterprise readiness, and early adoption of intelligent analytics solutions. The region hosts leading technology firms, cloud platforms, and AI developers that accelerate innovation and expand data science use cases. Companies across key industries increasingly depend on analytics tools for operational optimization and strategic decision-making. Robust research initiatives, substantial funding toward emerging technologies, and a favorable regulatory environment strengthen its market advantage. Moreover, the growing use of big data architectures, automation, and machine learning enhances organizational capabilities, ensuring North America remains the primary hub for data science growth and investment.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to its swift pace of digital transformation, expanding cloud ecosystem, and rising commitments toward analytics and AI adoption. Nations including India, China, Singapore, and South Korea are prioritizing data-centric policies to improve operational efficiency and accelerate innovation. Increasing internet connectivity, widespread mobile adoption, and growing data generation across key verticals stimulate the need for predictive insights and intelligent tools. Supportive government initiatives, strengthening digital infrastructure and a thriving startup environment focused on big data and AI solutions further elevate the region's momentum, positioning Asia-Pacific as the fastest-growing hub for data science expansion.

Key players in the market

Some of the key players in Data Science Market include Google, Microsoft, Amazon, IBM, Fractal Analytics, Mu Sigma, Accenture, Cloudera, Nvidia, Databricks, Tiger Analytics, LatentView Analytics, Teradata, Deloitte and Tata Consultancy Services (TCS).

Key Developments:

In November 2025, IBM and Atruvia AG have sealed a long-term collaboration that paves the way for sustainable and state-of-the-art IT platforms for the banking of tomorrow. Atruvia will use IBM z17, which was announced earlier this year, as a cornerstone supports its mission critical operations including the core banking system.

In September 2025, Microsoft and OpenAI have reached a non-binding agreement with Microsoft to restructure its for-profit arm into a Public Benefit Corporation (PBC), a move that could pave the way for the AI startup to raise new funding and eventually go public. In a blog post, OpenAI Board Chairman Bret Taylor explained that under the new arrangement, OpenAI's nonprofit parent will continue to exist and maintain control over the company's operations.

In August 2025, Accenture has agreed to acquire CyberCX, a leading privately-owned cybersecurity services provider serving both private and public sector organizations across Australia, New Zealand and internationally. The move represents Accenture's largest cybersecurity acquisition to date and will significantly bolster Accenture's cybersecurity services in Asia Pacific.

Components Covered:

  • Software Platforms
  • Tools & Frameworks
  • Services

Deployments Covered:

  • On-Premises
  • Cloud-Native
  • Hybrid

Technologies Covered:

  • Machine Learning & Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Predictive & Prescriptive Analytics
  • Data Engineering & Pipelines

Applications Covered:

  • Business Intelligence & Visualization
  • Customer Analytics & Personalization
  • Fraud Detection & Risk Management
  • Healthcare Diagnostics & Genomics
  • Supply Chain Optimization
  • IoT & Edge Analytics

End Users Covered:

  • IT & Telecom
  • BFSI
  • Healthcare & Life Sciences
  • Retail & E-Commerce
  • Manufacturing & Automotive
  • Energy & Utilities
  • Government & Defense
  • Education & Research

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Data Science Market, By Component

  • 5.1 Introduction
  • 5.2 Software Platforms
  • 5.3 Tools & Frameworks
  • 5.4 Services

6 Global Data Science Market, By Deployment

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud-Native
  • 6.4 Hybrid

7 Global Data Science Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning & Deep Learning
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Computer Vision
  • 7.5 Predictive & Prescriptive Analytics
  • 7.6 Data Engineering & Pipelines

8 Global Data Science Market, By Application

  • 8.1 Introduction
  • 8.2 Business Intelligence & Visualization
  • 8.3 Customer Analytics & Personalization
  • 8.4 Fraud Detection & Risk Management
  • 8.5 Healthcare Diagnostics & Genomics
  • 8.6 Supply Chain Optimization
  • 8.7 IoT & Edge Analytics

9 Global Data Science Market, By End User

  • 9.1 Introduction
  • 9.2 IT & Telecom
  • 9.3 BFSI
  • 9.4 Healthcare & Life Sciences
  • 9.5 Retail & E-Commerce
  • 9.6 Manufacturing & Automotive
  • 9.7 Energy & Utilities
  • 9.8 Government & Defense
  • 9.9 Education & Research

10 Global Data Science Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Google
  • 12.2 Microsoft
  • 12.3 Amazon
  • 12.4 IBM
  • 12.5 Fractal Analytics
  • 12.6 Mu Sigma
  • 12.7 Accenture
  • 12.8 Cloudera
  • 12.9 Nvidia
  • 12.10 Databricks
  • 12.11 Tiger Analytics
  • 12.12 LatentView Analytics
  • 12.13 Teradata
  • 12.14 Deloitte
  • 12.15 Tata Consultancy Services (TCS)

List of Tables

  • Table 1 Global Data Science Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Data Science Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Data Science Market Outlook, By Software Platforms (2024-2032) ($MN)
  • Table 4 Global Data Science Market Outlook, By Tools & Frameworks (2024-2032) ($MN)
  • Table 5 Global Data Science Market Outlook, By Services (2024-2032) ($MN)
  • Table 6 Global Data Science Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 7 Global Data Science Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 8 Global Data Science Market Outlook, By Cloud-Native (2024-2032) ($MN)
  • Table 9 Global Data Science Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 10 Global Data Science Market Outlook, By Technology (2024-2032) ($MN)
  • Table 11 Global Data Science Market Outlook, By Machine Learning & Deep Learning (2024-2032) ($MN)
  • Table 12 Global Data Science Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 13 Global Data Science Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 14 Global Data Science Market Outlook, By Predictive & Prescriptive Analytics (2024-2032) ($MN)
  • Table 15 Global Data Science Market Outlook, By Data Engineering & Pipelines (2024-2032) ($MN)
  • Table 16 Global Data Science Market Outlook, By Application (2024-2032) ($MN)
  • Table 17 Global Data Science Market Outlook, By Business Intelligence & Visualization (2024-2032) ($MN)
  • Table 18 Global Data Science Market Outlook, By Customer Analytics & Personalization (2024-2032) ($MN)
  • Table 19 Global Data Science Market Outlook, By Fraud Detection & Risk Management (2024-2032) ($MN)
  • Table 20 Global Data Science Market Outlook, By Healthcare Diagnostics & Genomics (2024-2032) ($MN)
  • Table 21 Global Data Science Market Outlook, By Supply Chain Optimization (2024-2032) ($MN)
  • Table 22 Global Data Science Market Outlook, By IoT & Edge Analytics (2024-2032) ($MN)
  • Table 23 Global Data Science Market Outlook, By End User (2024-2032) ($MN)
  • Table 24 Global Data Science Market Outlook, By IT & Telecom (2024-2032) ($MN)
  • Table 25 Global Data Science Market Outlook, By BFSI (2024-2032) ($MN)
  • Table 26 Global Data Science Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 27 Global Data Science Market Outlook, By Retail & E-Commerce (2024-2032) ($MN)
  • Table 28 Global Data Science Market Outlook, By Manufacturing & Automotive (2024-2032) ($MN)
  • Table 29 Global Data Science Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 30 Global Data Science Market Outlook, By Government & Defense (2024-2032) ($MN)
  • Table 31 Global Data Science Market Outlook, By Education & Research (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.