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封面
市场调查报告书
商品编码
1920855

全球资料科学平台市场（至 2035 年）：按组件类型、部署类型、应用程式类型、产业类型、地区、产业趋势与预测

Data Science Platform Market, Till 2035: Distribution by Type of Component, Type of Deployment, Type of Application, Type of Vertical, and Geographical Regions: Industry Trends and Global Forecasts
出版日期: | 出版商: Roots Analysis | 英文 174 Pages | 商品交期: 7-10个工作天内

价格
简介目录

资料科学平台市场概述

全球资料科学平台市场预计将从目前的 1,380 亿美元成长到 2035 年的 1,6,780 亿美元，预测期内复合年增长率 (CAGR) 为 25.47%。

资料科学平台市场-IMG1

资料科学平台市场：成长与趋势

随着数位转型加速和智慧型装置日益普及，数据科学平台市场正呈现显着成长。资料科学平台是指为资料科学家、分析师和工程师提供工具、框架和基础设施的综合性软体和解决方案，用于建置、部署和管理资料驱动型解决方案。这些平台使资料科学家能够执行从资料探索和特征工程到资料视觉化等广泛的活动。随着企业努力利用数据分析和商业智慧的潜力，对高阶数据科学平台的需求正在增长。各种影响因素，例如需要改善决策、提高营运效率以及更深入地了解客户行为，正在拓宽市场的视野。

这些因素，以及资料视觉化平台日益增长的趋势（这些平台可以将复杂的资料集转化为易于理解的洞察），有助于组织更快、更有效地做出决策。此外，机器学习平台也越来越受欢迎，因为它们能够实现流程自动化并发现资料集中隐藏的模式。因此，数据科学平台在商业中的应用广泛而多样，涵盖了从用于行销策略的预测分析到利用先进的预测技术来改进供应链分析等方方面面。对这些平台和服务日益增长的需求正在推动各行业组织的市场成长。

本报告分析了全球数据科学平台市场，并提供了市场规模估算、机会分析、竞争格局和公司概况。

目录

第一部分：报告概述

第一章：引言

第二章：研究方法

第三章：市场动态

第四章：宏观经济指标

第二部分：质性研究结果

第五章：摘要整理

第六章：引言

第七章：监理环境

第三部分：市场概览

第八章：关键指标综合资料库

第九章：竞争格局

第十章：市场空白分析

第十一章：竞争分析

第十二章：资料科学平台市场的创业生态系

第四部分：公司简介

第十三章：公司简介

  • 章节概述
  • Altair
  • Alteryx
  • Amvik系统
  • 阿里克托
  • AWS
  • Cloudera
  • 资料带
  • 资料块
  • 大泰库
  • 资料机器人
  • Google
  • H2O.ai
  • IBM
  • MathWorks
  • 微软
  • 快速矿工
  • SAP
  • SAS
  • 拼字
  • Teradata
  • TIBCO

第 5 节市场趋势

第 14 章大趋势分析

第15章未满足的分析需求

第十六章：专利分析

第十七章：最新进展

第六节：市场机会分析

第十八章：全球资料科学平台市场

第十九章：依组件类型划分的市场机会

第二十章：按部署类型划分的市场机会

第二十一章：按应用类型划分的市场机会

第二十二章：依产业类型划分的市场机会

第二十三章：北美资料科学平台市场机会

第二十四章：欧洲资料科学平台市场机会

第25章：亚洲资料科学平台市场机会

第26章：中东与北非（MENA）资料科学平台市场机会

第27章：拉丁美洲资料科学平台市场机会

第28章：其他地区资料科学平台市场机会

第29章：邻近市场分析

第7节：策略工具

第30章：关键成功策略

第31章：波特五力分析

第32章：SWOT分析分析

第33章：价值链分析

第34章：Roots的策略建议

第8节：其他独家见解

第35章：主要研究成果

第36章：报告结论

第9节：附录

简介目录
Product Code: RAICT300276

Data Science Platform Market Overview

As per Roots Analysis, the global data science platform market size is estimated to grow from USD 138 billion in the current year USD 1,678 billion by 2035, at a CAGR of 25.47% during the forecast period, till 2035.

Data Science Platform Market - IMG1

The opportunity for data science platform market has been distributed across the following segments:

Type of Component

  • Platform
  • Service

Type of Deployment

  • Cloud
  • On-Premises

Type of Application

  • Business Operation
  • Customer Support
  • Finance & Accounting
  • Logistics
  • Marketing
  • Others

Type of Vertical

  • BFSI
  • Energy Utilities
  • Government
  • Healthcare
  • IT & Telecom
  • Manufacturing
  • Retail
  • Others

Geographical Regions

  • North America
  • US
  • Canada
  • Mexico
  • Other North American countries
  • Europe
  • Austria
  • Belgium
  • Denmark
  • France
  • Germany
  • Ireland
  • Italy
  • Netherlands
  • Norway
  • Russia
  • Spain
  • Sweden
  • Switzerland
  • UK
  • Other European countries
  • Asia
  • China
  • India
  • Japan
  • Singapore
  • South Korea
  • Other Asian countries
  • Latin America
  • Brazil
  • Chile
  • Colombia
  • Venezuela
  • Other Latin American countries
  • Middle East and North Africa
  • Egypt
  • Iran
  • Iraq
  • Israel
  • Kuwait
  • Saudi Arabia
  • UAE
  • Other MENA countries
  • Rest of the World
  • Australia
  • New Zealand
  • Other countries

Data Science Platform Market: Growth and Trends

As digital transformation accelerates and smart devices become increasingly ubiquitous, the market for data science platforms is experiencing remarkable growth. Data science platforms are defined as all-encompassing software and solutions that provide data scientists, analysts, and engineers with tools, frameworks, and infrastructure to create, deploy, and manage solutions driven by data. These platforms enable data scientists to conduct a wide range of activities from data exploration and feature engineering to data visualization. With businesses striving to leverage the potential of data analytics and business intelligence, the requirement for advanced data science platforms is on the rise. Various influencing factors, such as the necessity for better decision-making, enhanced operational efficiency, and a more profound comprehension of customer behaviors, are broadening the market's perspective.

In addition to these factors, there is a notable trend toward data visualization platforms that convert intricate datasets into easily understandable insights. These tools facilitate swift and effective decision-making for organizations. Furthermore, machine learning platforms are becoming increasingly popular as they allow businesses to automate processes and discover hidden patterns within their datasets. Consequently, the applications of data science platforms in the business realm are extensive and diverse, encompassing everything from predictive analytics for marketing strategies to improving supply chain analytics through advanced forecasting methods. As a result, the demand for these platforms and services is prompting organizations across various sectors to boost market growth.

Data Science Platform Market: Key Segments

Market Share by Type of Component

Based on type of component, the global data science platform market is segmented into platform and service. According to our estimates, currently, the platform segment captures the majority of the market share, due to its comprehensive tools and features. These platforms integrate tools for data preparation and deployment of machine learning models within a single or collaborative environment, helping organizations optimize their workflows.

Conversely, the service segment is expected to grow at a higher CAGR during the forecast period. This increase can be attributed to the rising trend of outsourcing services, which enables companies to take advantage of the knowledge of industry experts and technical support, ensuring efficient platform operation, minimizing downtime, and addressing challenges effectively.

Market Share by Type of Deployment

Based on type of deployment, the global data science platform market is segmented into cloud and on-premises. According to our estimates, currently, the cloud deployment segment captures the majority of the market share. This can be attributed to the significant increase in the use of cloud-based data science platforms. However, the on-premises segment is expected to grow at a higher CAGR during the forecast period. This is due to the fact that on-premises deployment model is predominantly favored by large companies due to its robust security features, granting organizations complete control over their data.

Market Share by Type of Application

Based on type of application, the global data science platform market is segmented into business operation, customer support, finance & accounting, logistics, marketing, and others. According to our estimates, currently, the marketing application captures the majority of the market share. This can be attributed to the growing demand for solutions that provide personalization, customer targeting, and behavior analysis across various organizations. Data science platforms and tools enable customized customer experiences through recommendation engines and predictive targeting.

However, the logistics segment is expected to grow at a higher CAGR during the forecast period. This growth can be attributed to the expansion of the logistics industry driven by the rapid rise of the e-commerce sector, which has increased the demand for logistics solutions to improve efficiency, optimize routing, and manage inventory.

Market Share by Type of Vertical

Based on type of vertical, the global data science platform market is segmented into BFSI, energy utilities, government, healthcare, it & telecom, manufacturing, retail, and others. According to our estimates, currently, the BFSI industry captures the majority of the market share. This can be attributed to the strong demand for tools for fraud detection and risk management, which are driven by the significant amount of sensitive data related to transactions and customer information. As a result of these advantages, banks and financial institutions are increasingly utilizing big data analytics platforms to assess data, enhance decision-making, and improve customer experiences, thereby boosting operational efficiency. Additionally, the stringent regulatory requirements in this sector make enterprise data management solutions indispensable.

Market Share by Geographical Regions

Based on geographical regions, the data science platform market is segmented into North America, Europe, Asia, Latin America, Middle East and North Africa, and the rest of the world. According to our estimates, currently North America captures the majority share of the market. Additionally, Asia is anticipated to experience remarkable growth with a higher CAGR during the forecast period. This can be attributed to the rapid progress in digital transformation and economic development in this region. The increasing prevalence of smartphones, IoT devices, enhanced internet services, and the creation of smart cities are producing significant amounts of data that require sophisticated data science software and tools, thereby leading to remarkable growth in market development.

Example Players in Data Science Platform Market

  • Altair
  • Alteryx
  • Anaconda
  • Arrikto
  • AWS
  • Cloudera
  • Databand
  • Databricks
  • Dataiku
  • DataRobot
  • Google
  • H2O.ai
  • IBM
  • MathWorks
  • Microsoft
  • RapidMiner
  • SAP
  • SAS
  • Snowflake
  • Spell
  • Teradata
  • TIBCO

Data Science Platform Market: Research Coverage

The report on the data science platform market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the data science platform market, focusing on key market segments, including [A] type of component, [B] type of deployment, [C] type of application, [D] type of vertical, and [E] geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the data science platform market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
  • Company Profiles: Elaborate profiles of prominent players engaged in the data science platform market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] portfolio, [J] moat analysis, [K] recent developments, and an informed future outlook.
  • Megatrends: An evaluation of ongoing megatrends in the data science platform industry.
  • Patent Analysis: An insightful analysis of patents filed / granted in the data science platform domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
  • Recent Developments: An overview of the recent developments made in the data science platform market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
  • Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the data science platform market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
  • SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.
  • Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the data science platform market.

Key Questions Answered in this Report

  • How many companies are currently engaged in data science platform market?
  • Which are the leading companies in this market?
  • What factors are likely to influence the evolution of this market?
  • What is the current and future market size?
  • What is the CAGR of this market?
  • How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

  • The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
  • The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

Additional Benefits

  • Complimentary Excel Data Packs for all Analytical Modules in the Report
  • 15% Free Content Customization
  • Detailed Report Walkthrough Session with Research Team
  • Free Updated report if the report is 6-12 months old or older

TABLE OF CONTENTS

SECTION I: REPORT OVERVIEW

1. PREFACE

  • 1.1. Introduction
  • 1.2. Market Share Insights
  • 1.3. Key Market Insights
  • 1.4. Report Coverage
  • 1.5. Key Questions Answered
  • 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
  • 2.3. Database Building
    • 2.3.1. Data Collection
    • 2.3.2. Data Validation
    • 2.3.3. Data Analysis
  • 2.4. Project Methodology
    • 2.4.1. Secondary Research
      • 2.4.1.1. Annual Reports
      • 2.4.1.2. Academic Research Papers
      • 2.4.1.3. Company Websites
      • 2.4.1.4. Investor Presentations
      • 2.4.1.5. Regulatory Filings
      • 2.4.1.6. White Papers
      • 2.4.1.7. Industry Publications
      • 2.4.1.8. Conferences and Seminars
      • 2.4.1.9. Government Portals
      • 2.4.1.10. Media and Press Releases
      • 2.4.1.11. Newsletters
      • 2.4.1.12. Industry Databases
      • 2.4.1.13. Roots Proprietary Databases
      • 2.4.1.14. Paid Databases and Sources
      • 2.4.1.15. Social Media Portals
      • 2.4.1.16. Other Secondary Sources
    • 2.4.2. Primary Research
      • 2.4.2.1. Introduction
      • 2.4.2.2. Types
        • 2.4.2.2.1. Qualitative
        • 2.4.2.2.2. Quantitative
      • 2.4.2.3. Advantages
      • 2.4.2.4. Techniques
        • 2.4.2.4.1. Interviews
        • 2.4.2.4.2. Surveys
        • 2.4.2.4.3. Focus Groups
        • 2.4.2.4.4. Observational Research
        • 2.4.2.4.5. Social Media Interactions
      • 2.4.2.5. Stakeholders
        • 2.4.2.5.1. Company Executives (CXOs)
        • 2.4.2.5.2. Board of Directors
        • 2.4.2.5.3. Company Presidents and Vice Presidents
        • 2.4.2.5.4. Key Opinion Leaders
        • 2.4.2.5.5. Research and Development Heads
        • 2.4.2.5.6. Technical Experts
        • 2.4.2.5.7. Subject Matter Experts
        • 2.4.2.5.8. Scientists
        • 2.4.2.5.9. Doctors and Other Healthcare Providers
      • 2.4.2.6. Ethics and Integrity
        • 2.4.2.6.1. Research Ethics
        • 2.4.2.6.2. Data Integrity
    • 2.4.3. Analytical Tools and Databases

3. MARKET DYNAMICS

  • 3.1. Forecast Methodology
    • 3.1.1. Top-Down Approach
    • 3.1.2. Bottom-Up Approach
    • 3.1.3. Hybrid Approach
  • 3.2. Market Assessment Framework
    • 3.2.1. Total Addressable Market (TAM)
    • 3.2.2. Serviceable Addressable Market (SAM)
    • 3.2.3. Serviceable Obtainable Market (SOM)
    • 3.2.4. Currently Acquired Market (CAM)
  • 3.3. Forecasting Tools and Techniques
    • 3.3.1. Qualitative Forecasting
    • 3.3.2. Correlation
    • 3.3.3. Regression
    • 3.3.4. Time Series Analysis
    • 3.3.5. Extrapolation
    • 3.3.6. Convergence
    • 3.3.7. Forecast Error Analysis
    • 3.3.8. Data Visualization
    • 3.3.9. Scenario Planning
    • 3.3.10. Sensitivity Analysis
  • 3.4. Key Considerations
    • 3.4.1. Demographics
    • 3.4.2. Market Access
    • 3.4.3. Reimbursement Scenarios
    • 3.4.4. Industry Consolidation
  • 3.5. Robust Quality Control
  • 3.6. Key Market Segmentations
  • 3.7. Limitations

4. MACRO-ECONOMIC INDICATORS

  • 4.1. Chapter Overview
  • 4.2. Market Dynamics
    • 4.2.1. Time Period
      • 4.2.1.1. Historical Trends
      • 4.2.1.2. Current and Forecasted Estimates
    • 4.2.2. Currency Coverage
      • 4.2.2.1. Overview of Major Currencies Affecting the Market
      • 4.2.2.2. Impact of Currency Fluctuations on the Industry
    • 4.2.3. Foreign Exchange Impact
      • 4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
      • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 4.2.4. Recession
      • 4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
      • 4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 4.2.5. Inflation
      • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
    • 4.2.6. Interest Rates
      • 4.2.6.1. Overview of Interest Rates and Their Impact on the Market
      • 4.2.6.2. Strategies for Managing Interest Rate Risk
    • 4.2.7. Commodity Flow Analysis
      • 4.2.7.1. Type of Commodity
      • 4.2.7.2. Origins and Destinations
      • 4.2.7.3. Values and Weights
      • 4.2.7.4. Modes of Transportation
    • 4.2.8. Global Trade Dynamics
      • 4.2.8.1. Import Scenario
      • 4.2.8.2. Export Scenario
    • 4.2.9. War Impact Analysis
      • 4.2.9.1. Russian-Ukraine War
      • 4.2.9.2. Israel-Hamas War
    • 4.2.10. COVID Impact / Related Factors
      • 4.2.10.1. Global Economic Impact
      • 4.2.10.2. Industry-specific Impact
      • 4.2.10.3. Government Response and Stimulus Measures
      • 4.2.10.4. Future Outlook and Adaptation Strategies
    • 4.2.11. Other Indicators
      • 4.2.11.1. Fiscal Policy
      • 4.2.11.2. Consumer Spending
      • 4.2.11.3. Gross Domestic Product (GDP)
      • 4.2.11.4. Employment
      • 4.2.11.5. Taxes
      • 4.2.11.6. R&D Innovation
      • 4.2.11.7. Stock Market Performance
      • 4.2.11.8. Supply Chain
      • 4.2.11.9. Cross-Border Dynamics

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION

  • 6.1. Chapter Overview
  • 6.2. Overview of Data Science Platform Market
    • 6.2.1. Type of Component
    • 6.2.2. Type of Deployment
    • 6.2.3. Type of Application
    • 6.2.4. Type of Vertical
  • 6.3. Future Perspective

7. REGULATORY SCENARIO

SECTION III: MARKET OVERVIEW

8. COMPREHENSIVE DATABASE OF LEADING PLAYERS

9. COMPETITIVE LANDSCAPE

  • 9.1. Chapter Overview
  • 9.2. Data Science Platform: Overall Market Landscape
    • 9.2.1. Analysis by Year of Establishment
    • 9.2.2. Analysis by Company Size
    • 9.2.3. Analysis by Location of Headquarters
    • 9.2.4. Analysis by Ownership Structure

10. WHITE SPACE ANALYSIS

11. COMPANY COMPETITIVENESS ANALYSIS

12. STARTUP ECOSYSTEM IN THE DATA SCIENCE PLATFORM MARKET

  • 12.1. Data Science Platform: Market Landscape of Startups
    • 12.1.1. Analysis by Year of Establishment
    • 12.1.2. Analysis by Company Size
    • 12.1.3. Analysis by Company Size and Year of Establishment
    • 12.1.4. Analysis by Location of Headquarters
    • 12.1.5. Analysis by Company Size and Location of Headquarters
    • 12.1.6. Analysis by Ownership Structure
  • 12.2. Key Findings

SECTION IV: COMPANY PROFILES

13. COMPANY PROFILES

  • 13.1. Chapter Overview
  • 13.2. Altair *
    • 13.2.1. Company Overview
    • 13.2.2. Company Mission
    • 13.2.3. Company Footprint
    • 13.2.4. Management Team
    • 13.2.5. Contact Details
    • 13.2.6. Financial Performance
    • 13.2.7. Operating Business Segments
    • 13.2.8. Service / Product Portfolio (project specific)
    • 13.2.9. MOAT Analysis
    • 13.2.10. Recent Developments and Future Outlook
  • 13.3. Alteryx
  • 13.4. Amvik Systems
  • 13.5. Arrikto
  • 13.6. AWS
  • 13.7. Cloudera
  • 13.8. Databand
  • 13.9. Databricks
  • 13.10. Dataiku
  • 13.11. DataRobot
  • 13.12. Google
  • 13.13. H2O.ai
  • 13.14. IBM
  • 13.15. MathWorks
  • 13.16. Microsoft
  • 13.17. RapidMiner
  • 13.18. SAP
  • 13.19. SAS
  • 13.20. Spell
  • 13.21. Teradata
  • 13.22. TIBCO

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS

15. UNMEET NEED ANALYSIS

16. PATENT ANALYSIS

17. RECENT DEVELOPMENTS

  • 17.1. Chapter Overview
  • 17.2. Recent Funding
  • 17.3. Recent Partnerships
  • 17.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

18. GLOBAL DATA SCIENCE PLATFORM MARKET

  • 18.1. Chapter Overview
  • 18.2. Key Assumptions and Methodology
  • 18.3. Trends Disruption Impacting Market
  • 18.4. Demand Side Trends
  • 18.5. Supply Side Trends
  • 18.6. Global Data Science Platform Market, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 18.7. Multivariate Scenario Analysis
    • 18.7.1. Conservative Scenario
    • 18.7.2. Optimistic Scenario
  • 18.8. Investment Feasibility Index
  • 18.9. Key Market Segmentations

19. MARKET OPPORTUNITIES BASED ON TYPE OF COMPONENT

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Revenue Shift Analysis
  • 19.4. Market Movement Analysis
  • 19.5. Penetration-Growth (P-G) Matrix
  • 19.6. Data Science Platform Market for Platform: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.7. Data Science Platform Market for Service: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.8. Data Triangulation and Validation
    • 19.8.1. Secondary Sources
    • 19.8.2. Primary Sources
    • 19.8.3. Statistical Modeling

20. MARKET OPPORTUNITIES BASED ON TYPE OF DEPLOYMENT

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. Revenue Shift Analysis
  • 20.4. Market Movement Analysis
  • 20.5. Penetration-Growth (P-G) Matrix
  • 20.6. Data Science Platform Market for Cloud: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 20.7. Data Science Platform Market for On-Premises: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 20.8. Data Triangulation and Validation
    • 20.8.1. Secondary Sources
    • 20.8.2. Primary Sources
    • 20.8.3. Statistical Modeling

21. MARKET OPPORTUNITIES BASED ON TYPE OF APPLICATION

  • 21.1. Chapter Overview
  • 21.2. Key Assumptions and Methodology
  • 21.3. Revenue Shift Analysis
  • 21.4. Market Movement Analysis
  • 21.5. Penetration-Growth (P-G) Matrix
  • 21.6. Data Science Platform Market for Business Operation: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.7. Data Science Platform Market for Customer Support: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.8. Data Science Platform Market for Finance & Accounting: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.9. Data Science Platform Market for Logistics: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.10. Data Science Platform Market for Marketing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.11. Data Science Platform Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.12. Data Triangulation and Validation
    • 21.12.1. Secondary Sources
    • 21.12.2. Primary Sources
    • 21.12.3. Statistical Modeling

22. MARKET OPPORTUNITIES BASED ON TYPE OF VERTICAL

  • 22.1. Chapter Overview
  • 22.2. Key Assumptions and Methodology
  • 22.3. Revenue Shift Analysis
  • 22.4. Market Movement Analysis
  • 22.5. Penetration-Growth (P-G) Matrix
  • 22.6. Data Science Platform Market for BFSI: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.7. Data Science Platform Market for Energy Utilities: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.8. Data Science Platform Market for Government: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.9. Data Science Platform Market for Healthcare: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.10. Data Science Platform Market for IT & Telecom: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.11. Data Science Platform Market for Manufacturing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.12. Data Science Platform Market for Retail: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.13. Data Science Platform Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.14. Data Triangulation and Validation
    • 22.14.1. Secondary Sources
    • 22.14.2. Primary Sources
    • 22.14.3. Statistical Modeling

23. MARKET OPPORTUNITIES FOR DATA SCIENCE PLATFORMS IN NORTH AMERICA

  • 23.1. Chapter Overview
  • 23.2. Key Assumptions and Methodology
  • 23.3. Revenue Shift Analysis
  • 23.4. Market Movement Analysis
  • 23.5. Penetration-Growth (P-G) Matrix
  • 23.6. Data Science Platform Market in North America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 23.6.1. Data Science Platform Market in the US: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 23.6.2. Data Science Platform Market in Canada: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 23.6.3. Data Science Platform Market in Mexico: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 23.6.4. Data Science Platform Market in Other North American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.7. Data Triangulation and Validation

24. MARKET OPPORTUNITIES FOR DATA SCIENCE PLATFORMS IN EUROPE

  • 24.1. Chapter Overview
  • 24.2. Key Assumptions and Methodology
  • 24.3. Revenue Shift Analysis
  • 24.4. Market Movement Analysis
  • 24.5. Penetration-Growth (P-G) Matrix
  • 24.6. Data Science Platform Market in Europe: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.1. Data Science Platform Market in Austria: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.2. Data Science Platform Market in Belgium: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.3. Data Science Platform Market in Denmark: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.4. Data Science Platform Market in France: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.5. Data Science Platform Market in Germany: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.6. Data Science Platform Market in Ireland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.7. Data Science Platform Market in Italy: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.8. Data Science Platform Market in Netherlands: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.9. Data Science Platform Market in Norway: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.10. Data Science Platform Market in Russia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.11. Data Science Platform Market in Spain: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.12. Data Science Platform Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.13. Data Science Platform Market in Switzerland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.14. Data Science Platform Market in the UK: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 24.6.15. Data Science Platform Market in Other European Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.7. Data Triangulation and Validation

25. MARKET OPPORTUNITIES FOR DATA SCIENCE PLATFORMS IN ASIA

  • 25.1. Chapter Overview
  • 25.2. Key Assumptions and Methodology
  • 25.3. Revenue Shift Analysis
  • 25.4. Market Movement Analysis
  • 25.5. Penetration-Growth (P-G) Matrix
  • 25.6. Data Science Platform Market in Asia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.1. Data Science Platform Market in China: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.2. Data Science Platform Market in India: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.3. Data Science Platform Market in Japan: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.4. Data Science Platform Market in Singapore: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.5. Data Science Platform Market in South Korea: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 25.6.6. Data Science Platform Market in Other Asian Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.7. Data Triangulation and Validation

26. MARKET OPPORTUNITIES FOR DATA SCIENCE PLATFORMS IN MIDDLE EAST AND NORTH AFRICA (MENA)

  • 26.1. Chapter Overview
  • 26.2. Key Assumptions and Methodology
  • 26.3. Revenue Shift Analysis
  • 26.4. Market Movement Analysis
  • 26.5. Penetration-Growth (P-G) Matrix
  • 26.6. Data Science Platform Market in Middle East and North Africa (MENA): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.1. Data Science Platform Market in Egypt: Historical Trends (Since 2019) and Forecasted Estimates (Till 205)
    • 26.6.2. Data Science Platform Market in Iran: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.3. Data Science Platform Market in Iraq: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.4. Data Science Platform Market in Israel: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.5. Data Science Platform Market in Kuwait: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.6. Data Science Platform Market in Saudi Arabia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.7. Neuromorphic Computing Marke in United Arab Emirates (UAE): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.8. Data Science Platform Market in Other MENA Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.7. Data Triangulation and Validation

27. MARKET OPPORTUNITIES FOR DATA SCIENCE PLATFORMS IN LATIN AMERICA

  • 27.1. Chapter Overview
  • 27.2. Key Assumptions and Methodology
  • 27.3. Revenue Shift Analysis
  • 27.4. Market Movement Analysis
  • 27.5. Penetration-Growth (P-G) Matrix
  • 27.6. Data Science Platform Market in Latin America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.1. Data Science Platform Market in Argentina: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.2. Data Science Platform Market in Brazil: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.3. Data Science Platform Market in Chile: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.4. Data Science Platform Market in Colombia Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.5. Data Science Platform Market in Venezuela: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.6. Data Science Platform Market in Other Latin American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.7. Data Triangulation and Validation

28. MARKET OPPORTUNITIES FOR DATA SCIENCE PLATFORMS IN REST OF THE WORLD

  • 28.1. Chapter Overview
  • 28.2. Key Assumptions and Methodology
  • 28.3. Revenue Shift Analysis
  • 28.4. Market Movement Analysis
  • 28.5. Penetration-Growth (P-G) Matrix
  • 28.6. Data Science Platform Market in Rest of the World: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.1. Data Science Platform Market in Australia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.2. Data Science Platform Market in New Zealand: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.3. Data Science Platform Market in Other Countries
  • 28.7. Data Triangulation and Validation

29. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

30. KEY WINNING STRATEGIES

31. PORTER'S FIVE FORCES ANALYSIS

32. SWOT ANALYSIS

33. VALUE CHAIN ANALYSIS

34. ROOTS STRATEGIC RECOMMENDATIONS

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

35. INSIGHTS FROM PRIMARY RESEARCH

36. REPORT CONCLUSION

SECTION IX: APPENDIX

37. TABULATED DATA

38. LIST OF COMPANIES AND ORGANIZATIONS

39. CUSTOMIZATION OPPORTUNITIES

40. ROOTS SUBSCRIPTION SERVICES

41. AUTHOR DETAILS