데이터 사이언스 플랫폼 시장 : 구성요소 유형별, 전개 유형별, 용도 유형별, 업계 유형별, 지역별, 업계 동향, 예측(-2035년)

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

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한글목차

데이터 사이언스 플랫폼 시장 개요

세계 데이터 사이언스 플랫폼 시장 규모는 현재 1,380억 달러에서 2035년까지 1조 6,780억 달러에 이를 것으로 추정되며, 2035년까지의 예측 기간에 CAGR로 25.47%의 성장이 예상됩니다.

데이터 사이언스 플랫폼 시장 : 성장과 동향

디지털 전환이 가속화되고 스마트 디바이스가 점점 보급되고 있는 가운데, 데이터 사이언스 플랫폼 시장은 현저한 성장을 보이고 있습니다. 데이터 사이언스 플랫폼은 데이터 과학자, 분석가 및 엔지니어가 데이터 기반 솔루션을 구축, 배포 및 관리하기 위한 도구, 프레임워크 및 인프라를 제공하는 종합적인 소프트웨어 및 솔루션으로 정의됩니다. 이러한 플랫폼을 통해 데이터 과학자는 데이터 검색 및 특징 엔지니어링에서 데이터 시각화에 이르기까지 광범위한 활동을 수행할 수 있습니다. 기업이 데이터 분석 및 비즈니스 인텔리전스의 잠재력을 활용하려고 노력하는 동안 첨단 데이터 사이언스 플랫폼에 대한 수요가 증가하고 있습니다. 의사결정을 개선할 필요성, 업무 효율성 향상, 고객 행동에 대한 깊은 이해 등 다양한 영향요인이 시장의 시야를 넓히고 있습니다.

이러한 요인 외에도 복잡한 데이터 세트를 쉽게 이해할 수 있는 지식으로 변환하는 데이터 시각화 플랫폼의 동향이 증가하고 있습니다. 이러한 도구는 조직의 신속하고 효과적인 의사 결정을 지원합니다. 게다가 머신러닝 플랫폼은 프로세스 자동화와 데이터 세트에서 숨겨진 패턴의 발견을 가능하게 하기 때문에 점점 인기가 높아지고 있습니다. 결과적으로 비즈니스 분야에서의 데이터 사이언스 플랫폼의 적용 범위는 광범위하고 다양하며, 마케팅 전략에 사용되는 예측 분석에서부터 첨단 예측 기법을 통한 공급망 분석의 개선에 이르기까지 모든 것이 포함됩니다. 이러한 플랫폼과 서비스에 대한 수요가 증가함에 따라 다양한 부서의 조직이 시장 성장을 가속하고 있습니다.

이 보고서는 세계 데이터 사이언스 플랫폼 시장을 조사했으며, 시장 규모 추정 및 기회 분석, 경쟁 구도, 기업 프로파일 등의 정보를 제공합니다.

목차

섹션 1 보고서 개요

제1장 서문

제2장 조사 방법

제3장 시장 역학

제4장 거시경제지표

섹션 2 질적 지식

제5장 주요 요약

제6장 소개

제7장 규제 시나리오

섹션 3 시장 개요

제8장 주요 기업의 종합적인 데이터베이스

제9장 경쟁 구도

제10장 미개척 시장 분석

제11장 기업의 경쟁력 분석

제12장 데이터 사이언스 플랫폼 시장에서의 스타트업 생태계

섹션 4 기업 프로파일

제13장 기업 프로파일

• 장의 개요
• Altair
• Alteryx
• Amvik Systems
• Arrikto
• AWS
• Cloudera
• Databand
• Databricks
• Dataiku
• DataRobot
• Google
• H2O.ai
• IBM
• MathWorks
• Microsoft
• RapidMiner
• SAP
• SAS
• 스펠
• Teradata
• TIBCO

섹션 5 시장 동향

제14장 메가트렌드 분석

제15장 미충족 요구의 분석

제16장 특허 분석

제17장 최근의 발전

섹션 6 시장 기회 분석

제18장 세계 데이터 사이언스 플랫폼 시장

제19장 시장 기회 : 구성요소 유형별

제20장 시장 기회 : 전개 유형별

제21장 시장 기회 : 용도 유형별

제22장 시장 기회 : 업계 유형별

제23장 북미의 데이터 사이언스 플랫폼 시장 기회

제24장 유럽의 데이터 사이언스 플랫폼 시장 기회

제25장 아시아의 데이터 사이언스 플랫폼 시장 기회

제26장 중동 및 북아프리카(MENA)의 데이터 사이언스 플랫폼 시장 기회

제27장 라틴아메리카의 데이터 사이언스 플랫폼 시장 기회

제28장 기타 지역 데이터 사이언스 플랫폼 시장 기회

제29장 인접 시장 분석

섹션 7 전략적 도구

제30장 중요한 성공 전략

제31장 Porter's Five Forces 분석

제32장 SWOT 분석

제33장 밸류체인 분석

제34장 Roots의 전략적 제안

섹션 8 기타 독점적 발견

제35장 1차 조사로부터의 지견

제36장 보고서 결론

섹션 9 부록

SHW

영문 목차

영문목차

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.

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

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