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市場調查報告書
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1889277

資料科學市場預測至2032年：按組件、部署類型、技術、應用、最終用戶和地區分類的全球分析

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個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球資料科學市場價值將達到 1598.9 億美元，到 2032 年將達到 11585.6 億美元，在預測期內的複合年成長率為 32.7%。

資料科學是一門跨學科領域，致力於透過統計分析、機器學習演算法和高級分析技術，從龐大而複雜的資料集中提取有意義的資訊。它融合了程式設計能力、專業知識和視覺化技術，用於發現模式、預測結果並指南明智的策略決策。數據科學正在對金融、醫療保健、零售和通訊等行業產生重大影響，幫助企業簡化流程、分析客戶行為並開發創新解決方案。隨著以數據為中心的方法對業務成長變得至關重要，對熟練數據科學家的需求持續成長，資料科學已成為現代數位環境中最具影響力和發展最快的領域之一。

根據 Anaconda 發布的《2024 年資料科學現況報告》，87% 的從業人員正在擴大人工智慧的應用範圍，包括資料清洗、任務自動化和預測建模。此外，49% 的公司正在設立人工智慧資料分析師職位，46% 的公司正在設立新的人工智慧工程師職位，這表明資料科學正在改變勞動力市場。

數據量快速成長

數位生態系統、物聯網設備和雲端基礎應用的爆炸性成長極大地加速了全球數據生成，並成為資料科學市場的主要驅動力。企業如今正從用戶行為、智慧感測器、財務活動和營運系統中累積大量資訊，並日益依賴先進的分析技術。資料科學將非結構化數據轉化為可執行的洞察，有助於提升商務策略和績效。隨著各組織競相創新並快速回應不斷變化的環境，分析大量資料集的能力變得至關重要。數據產生的持續成長正顯著推動各產業對資料科學平台和服務的採用。

熟練的資料科學專業人員短缺

資料科學市場面臨的一大限制因素是訓練有素的資料科學專業人才長期短缺，這給尋求專業分析技術的公司帶來了挑戰。資料科學職位需要精通程式設計、統計學、機器學習和技術專長，導致人才庫規模小且競爭異常激烈。這種人才缺口阻礙了高階分析技術的應用，減緩了業務流程，並推高了招募成本。儘管許多組織都在投資提昇員工技能，但數據技術的快速發展仍在加劇技能短缺。隨著企業越來越依賴數據驅動型策略，合格人才的匱乏仍是市場擴張的一大障礙。

擴展產業專用的資料科學解決方案

產業專用的資料科學應用的興起為市場擴張帶來了巨大機遇，因為各行各業都需要專門針對自身營運需求而建構的分析工具。醫療保健、金融、製造和零售等行業越來越依賴嵌入專業知識的解決方案來提高準確性、效率和合規性。客製化系統支援個人化治療、風險評分、維護預測、供應鏈規劃和價格最佳化等應用情境。跨產業加速的數位轉型正在推動對專業資料科學平台的需求，促使技術提供者提供專業化的高價值解決方案。這種產業專用的方法使供應商能夠獲得競爭優勢，並有效地滿足特定客戶的需求。

技術創新速度遠超人力資源能力。

資料科學技術的快速發展帶來了巨大的威脅，因為企業往往缺乏跟上步伐所需的專業人才。人工智慧、機器學習、巨量資料框架和自動化技術等領域的新技術層出不窮，需要企業持續學習和適應。許多公司無法及時培訓員工，導致技能過時、模型效能欠佳，計劃執行停滯不前。快速更新的工具使得舊系統更快過時，並增加了升級成本和複雜性。如果企業不持續提陞技能和加大投資，就有可能失去競爭優勢。這種日益擴大的能力差距限制了各行業資料科學實施的效率和擴充性。

新冠疫情的影響：

新冠疫情顯著加速了資料科學市場的發展，企業迅速採用數位化解決方案、遠距工作流程和進階分析技術。各組織機構高度依賴預測模型、即時儀錶板和預測系統來應對供應鏈挑戰、醫療壓力和消費行為的變化。資料科學科學使政府和企業能夠分析感染趨勢、有效分配資源並改善緊急應變計畫。由於對擴充性和靈活基礎設施的需求增加，此次危機也推動了雲端基礎的分析技術的發展。儘管一些行業推遲了主要的IT支出，但疫情最終強化了資料科學作為業務連續性和未來發展準備的關鍵工具的價值。

預計在預測期內，軟體平台細分市場將佔據最大的市場佔有率。

預計在預測期內，軟體平台細分市場將佔據最大的市場佔有率，顯著超越服務細分市場。根據多項行業研究，該細分市場貢獻了超過80%的總收入。企業正專注於建立一體化平台，以管理從資料收集、模型建置到配置和監控的所有環節，這推動了相關領域的巨額投資。這些平台解決方案對資料科學團隊至關重要，因為它們提供了可擴展的基礎架構和簡化的工作流程。因此，軟體平台的主導地位將成為推動資料科學市場成長的核心因素。

預計在預測期內，醫療保健和生命科學領域將實現最高的複合年成長率。

在預測期內，醫療保健和生命科學領域預計將實現最高成長率，這主要得益於人工智慧驅動的洞察、自動化和預測分析技術的日益普及。醫療服務提供者和研究人員越來越依賴先進的數據技術來提高診斷準確性、支持實證醫學並加速科學發現。對精準醫療、基因分析和持續病患監測的日益重視，推動了該領域對強大分析系統的需求。此外，數位醫療的快速普及、電子健康記錄的整合以及遠端醫療應用，正在產生豐富的資料集，促使醫療機構優先投資於先進且擴充性的資料科學解決方案。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率，這得益於其先進的數位化環境、較高的企業準備度以及對智慧分析解決方案的早期採用。該地區匯集了許多大型科技公司、雲端平台和人工智慧開發商，加速了創新進程，並拓展了資料科學的應用場景。各關鍵產業的企業越來越依賴分析工具來最佳化營運和進行策略決策。強大的研究實力、對新興技術的大量投入以及有利的法規環境，都鞏固了北美的市場主導地位。此外，巨量資料架構、自動化和機器學習的日益普及，也提升了企業的能力，確保北美繼續保持在資料科學成長和投資領域的領先地位。

複合年成長率最高的地區：

由於數位轉型加速、雲端生態系不斷擴展以及在分析和人工智慧領域的應用日益廣泛，亞太地區預計將在預測期內實現最高的複合年成長率。印度、中國、新加坡和韓國等國家正優先推行以數據為中心的政策，旨在提高營運效率並加速創新。網路連線的改善、行動裝置的普及以及關鍵產業數據產生量的增加，正在刺激對預測分析和智慧工具的需求。政府的支持性政策、不斷完善的數位基礎設施以及充滿活力的、專注於巨量資料和人工智慧解決方案的Start-Ups環境，進一步推動了該地區的發展勢頭，使亞太地區成為資料科學領域成長最快的中心。

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目錄

第1章執行摘要

第2章 前言

  • 摘要
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球資料科學市場（按組件分類）

  • 軟體平台
  • 工具和框架
  • 服務

第6章 全球資料科學市場依部署類型分類

  • 本地部署
  • 雲端原生
  • 混合

7. 全球資料科學市場（依技術分類）

  • 機器學習和深度學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 預測分析與規範分析
  • 數據工程和管道

第8章：全球資料科學市場（按應用分類）

  • 商業智慧和視覺化
  • 客戶分析與個人化
  • 詐欺偵測和風險管理
  • 醫學診斷與基因組學
  • 供應鏈最佳化
  • 物聯網和邊緣分析

9. 全球資料科學市場（按最終用戶分類）

  • 資訊科技/通訊
  • BFSI
  • 醫學與生命科​​學
  • 零售與電子商務
  • 製造業/汽車
  • 能源與公共產業
  • 政府/國防
  • 教育/研究

第10章：全球資料科學市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第12章：企業概況

  • 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.