Market Analysis and Insights: Global Data Science and Machine-Learning Platforms Market
The global Data Science and Machine-Learning Platforms market is projected to grow from US$ million in 2023 to US$ million by 2029, at a Compound Annual Growth Rate (CAGR) of % during the forecast period.
The US & Canada market for Data Science and Machine-Learning Platforms is estimated to increase from $ million in 2023 to reach $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
The China market for Data Science and Machine-Learning Platforms is estimated to increase from $ million in 2023 to reach $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
The Europe market for Data Science and Machine-Learning Platforms is estimated to increase from $ million in 2023 to reach $ million by 2029, at a CAGR of % during the forecast period of 2023 through 2029.
The global key companies of Data Science and Machine-Learning Platforms include SAS, Alteryx, IBM, RapidMiner, KNIME, Microsoft, Dataiku, Databricks and TIBCO Software, etc. in 2022, the global top five players had a share approximately % in terms of revenue.
Report Includes
This report presents an overview of global market for Data Science and Machine-Learning Platforms market size. Analyses of the global market trends, with historic market revenue data for 2018 - 2022, estimates for 2023, and projections of CAGR through 2029.
This report researches the key producers of Data Science and Machine-Learning Platforms, also provides the revenue of main regions and countries. Highlights of the upcoming market potential for Data Science and Machine-Learning Platforms, and key regions/countries of focus to forecast this market into various segments and sub-segments. Country specific data and market value analysis for the U.S., Canada, Mexico, Brazil, China, Japan, South Korea, Southeast Asia, India, Germany, the U.K., Italy, Middle East, Africa, and Other Countries.
This report focuses on the Data Science and Machine-Learning Platforms revenue, market share and industry ranking of main companies, data from 2018 to 2023. Identification of the major stakeholders in the global Data Science and Machine-Learning Platforms market, and analysis of their competitive landscape and market positioning based on recent developments and segmental revenues. This report will help stakeholders to understand the competitive landscape and gain more insights and position their businesses and market strategies in a better way.
This report analyzes the segments data by type and by application, revenue, and growth rate, from 2018 to 2029. Evaluation and forecast the market size for Data Science and Machine-Learning Platforms revenue, projected growth trends, production technology, application and end-user industry.
Descriptive company profiles of the major global players, including SAS, Alteryx, IBM, RapidMiner, KNIME, Microsoft, Dataiku, Databricks and TIBCO Software, etc.
By Company
SAS
Alteryx
IBM
RapidMiner
KNIME
Microsoft
Dataiku
Databricks
TIBCO Software
MathWorks
H20.ai
Anaconda
SAP
Google
Domino Data Lab
Angoss
Lexalytics
Rapid Insight
Segment by Type
Open Source Data Integration Tools
Cloud-based Data Integration Tools
Segment by Application
Small-Sized Enterprises
Medium-Sized Enterprise
Large Enterprises
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East, Africa, and Latin America
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (product type, application, etc.), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Revenue of Data Science and Machine-Learning Platforms in global and regional level. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world. This section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Data Science and Machine-Learning Platforms companies鈥� competitive landscape, revenue, market share and industry ranking, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by type, covering the revenue, and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by application, covering the revenue, and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6: North America by type, by application and by country, revenue for each segment.
Chapter 7: Europe by type, by application and by country, revenue for each segment.
Chapter 8: China by type and by application revenue for each segment.
Chapter 9: Asia (excluding China) by type, by application and by region, revenue for each segment.
Chapter 10: Middle East, Africa, and Latin America by type, by application and by country, revenue for each segment.
Chapter 11: Provides profiles of key companies, introducing the basic situation of the main companies in the market in detail, including product descriptions and specifications, Data Science and Machine-Learning Platforms revenue, gross margin, and recent development, etc.
Chapter 12: Analyst's Viewpoints/Conclusions
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1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Data Science and Machine-Learning Platforms Market Size Growth Rate by Type, 2018 VS 2022 VS 2029
1.2.2 Open Source Data Integration Tools
1.2.3 Cloud-based Data Integration Tools
1.3 Market by Application
1.3.1 Global Data Science and Machine-Learning Platforms Market Size Growth Rate by Application, 2018 VS 2022 VS 2029
1.3.2 Small-Sized Enterprises
1.3.3 Medium-Sized Enterprise
1.3.4 Large Enterprises
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Global Growth Trends
2.1 Global Data Science and Machine-Learning Platforms Market Perspective (2018-2029)
2.2 Global Data Science and Machine-Learning Platforms Growth Trends by Region
2.2.1 Data Science and Machine-Learning Platforms Market Size by Region: 2018 VS 2022 VS 2029
2.2.2 Data Science and Machine-Learning Platforms Historic Market Size by Region (2018-2023)
2.2.3 Data Science and Machine-Learning Platforms Forecasted Market Size by Region (2024-2029)
2.3 Data Science and Machine-Learning Platforms Market Dynamics
2.3.1 Data Science and Machine-Learning Platforms Industry Trends
2.3.2 Data Science and Machine-Learning Platforms Market Drivers
2.3.3 Data Science and Machine-Learning Platforms Market Challenges
2.3.4 Data Science and Machine-Learning Platforms Market Restraints
3 Competition Landscape by Key Players
3.1 Global Revenue Data Science and Machine-Learning Platforms by Players
3.1.1 Global Data Science and Machine-Learning Platforms Revenue by Players (2018-2023)
3.1.2 Global Data Science and Machine-Learning Platforms Revenue Market Share by Players (2018-2023)
3.2 Global Data Science and Machine-Learning Platforms Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Global Key Players of Data Science and Machine-Learning Platforms, Ranking by Revenue, 2021 VS 2022 VS 2023
3.4 Global Data Science and Machine-Learning Platforms Market Concentration Ratio
3.4.1 Global Data Science and Machine-Learning Platforms Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Data Science and Machine-Learning Platforms Revenue in 2022
3.5 Global Key Players of Data Science and Machine-Learning Platforms Head office and Area Served
3.6 Global Key Players of Data Science and Machine-Learning Platforms, Product and Application
3.7 Global Key Players of Data Science and Machine-Learning Platforms, Date of Enter into This Industry
3.8 Mergers & Acquisitions, Expansion Plans
4 Data Science and Machine-Learning Platforms Breakdown Data by Type
4.1 Global Data Science and Machine-Learning Platforms Historic Market Size by Type (2018-2023)
4.2 Global Data Science and Machine-Learning Platforms Forecasted Market Size by Type (2024-2029)
5 Data Science and Machine-Learning Platforms Breakdown Data by Application
5.1 Global Data Science and Machine-Learning Platforms Historic Market Size by Application (2018-2023)
5.2 Global Data Science and Machine-Learning Platforms Forecasted Market Size by Application (2024-2029)
6 North America
6.1 North America Data Science and Machine-Learning Platforms Market Size (2018-2029)
6.2 North America Data Science and Machine-Learning Platforms Market Size by Type
6.2.1 North America Data Science and Machine-Learning Platforms Market Size by Type (2018-2023)
6.2.2 North America Data Science and Machine-Learning Platforms Market Size by Type (2024-2029)
6.2.3 North America Data Science and Machine-Learning Platforms Market Share by Type (2018-2029)
6.3 North America Data Science and Machine-Learning Platforms Market Size by Application
6.3.1 North America Data Science and Machine-Learning Platforms Market Size by Application (2018-2023)
6.3.2 North America Data Science and Machine-Learning Platforms Market Size by Application (2024-2029)
6.3.3 North America Data Science and Machine-Learning Platforms Market Share by Application (2018-2029)
6.4 North America Data Science and Machine-Learning Platforms Market Size by Country
6.4.1 North America Data Science and Machine-Learning Platforms Market Size by Country: 2018 VS 2022 VS 2029
6.4.2 North America Data Science and Machine-Learning Platforms Market Size by Country (2018-2023)
6.4.3 North America Data Science and Machine-Learning Platforms Market Size by Country (2024-2029)
6.4.4 U.S.
6.4.5 Canada
7 Europe
7.1 Europe Data Science and Machine-Learning Platforms Market Size (2018-2029)
7.2 Europe Data Science and Machine-Learning Platforms Market Size by Type
7.2.1 Europe Data Science and Machine-Learning Platforms Market Size by Type (2018-2023)
7.2.2 Europe Data Science and Machine-Learning Platforms Market Size by Type (2024-2029)
7.2.3 Europe Data Science and Machine-Learning Platforms Market Share by Type (2018-2029)
7.3 Europe Data Science and Machine-Learning Platforms Market Size by Application
7.3.1 Europe Data Science and Machine-Learning Platforms Market Size by Application (2018-2023)
7.3.2 Europe Data Science and Machine-Learning Platforms Market Size by Application (2024-2029)
7.3.3 Europe Data Science and Machine-Learning Platforms Market Share by Application (2018-2029)
7.4 Europe Data Science and Machine-Learning Platforms Market Size by Country
7.4.1 Europe Data Science and Machine-Learning Platforms Market Size by Country: 2018 VS 2022 VS 2029
7.4.2 Europe Data Science and Machine-Learning Platforms Market Size by Country (2018-2023)
7.4.3 Europe Data Science and Machine-Learning Platforms Market Size by Country (2024-2029)
7.4.3 Germany
7.4.4 France
7.4.5 U.K.
7.4.6 Italy
7.4.7 Russia
7.4.8 Nordic Countries
8 China
8.1 China Data Science and Machine-Learning Platforms Market Size (2018-2029)
8.2 China Data Science and Machine-Learning Platforms Market Size by Type
8.2.1 China Data Science and Machine-Learning Platforms Market Size by Type (2018-2023)
8.2.2 China Data Science and Machine-Learning Platforms Market Size by Type (2024-2029)
8.2.3 China Data Science and Machine-Learning Platforms Market Share by Type (2018-2029)
8.3 China Data Science and Machine-Learning Platforms Market Size by Application
8.3.1 China Data Science and Machine-Learning Platforms Market Size by Application (2018-2023)
8.3.2 China Data Science and Machine-Learning Platforms Market Size by Application (2024-2029)
8.3.3 China Data Science and Machine-Learning Platforms Market Share by Application (2018-2029)
9 Asia (excluding China)
9.1 Asia Data Science and Machine-Learning Platforms Market Size (2018-2029)
9.2 Asia Data Science and Machine-Learning Platforms Market Size by Type
9.2.1 Asia Data Science and Machine-Learning Platforms Market Size by Type (2018-2023)
9.2.2 Asia Data Science and Machine-Learning Platforms Market Size by Type (2024-2029)
9.2.3 Asia Data Science and Machine-Learning Platforms Market Share by Type (2018-2029)
9.3 Asia Data Science and Machine-Learning Platforms Market Size by Application
9.3.1 Asia Data Science and Machine-Learning Platforms Market Size by Application (2018-2023)
9.3.2 Asia Data Science and Machine-Learning Platforms Market Size by Application (2024-2029)
9.3.3 Asia Data Science and Machine-Learning Platforms Market Share by Application (2018-2029)
9.4 Asia Data Science and Machine-Learning Platforms Market Size by Region
9.4.1 Asia Data Science and Machine-Learning Platforms Market Size by Region: 2018 VS 2022 VS 2029
9.4.2 Asia Data Science and Machine-Learning Platforms Market Size by Region (2018-2023)
9.4.3 Asia Data Science and Machine-Learning Platforms Market Size by Region (2024-2029)
9.4.4 Japan
9.4.5 South Korea
9.4.6 China Taiwan
9.4.7 Southeast Asia
9.4.8 India
9.4.9 Australia
10 Middle East, Africa, and Latin America
10.1 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size (2018-2029)
10.2 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Type
10.2.1 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Type (2018-2023)
10.2.2 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Type (2024-2029)
10.2.3 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Share by Type (2018-2029)
10.3 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Application
10.3.1 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Application (2018-2023)
10.3.2 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Application (2024-2029)
10.3.3 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Share by Application (2018-2029)
10.4 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Country
10.4.1 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Country: 2018 VS 2022 VS 2029
10.4.2 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Country (2018-2023)
10.4.3 Middle East, Africa, and Latin America Data Science and Machine-Learning Platforms Market Size by Country (2024-2029)
10.4.4 Brazil
10.4.5 Mexico
10.4.6 Turkey
10.4.7 Saudi Arabia
10.4.8 Israel
10.4.9 GCC Countries
11 Key Players Profiles
11.1 SAS
11.1.1 SAS Company Details
11.1.2 SAS Business Overview
11.1.3 SAS Data Science and Machine-Learning Platforms Introduction
11.1.4 SAS Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.1.5 SAS Recent Developments
11.2 Alteryx
11.2.1 Alteryx Company Details
11.2.2 Alteryx Business Overview
11.2.3 Alteryx Data Science and Machine-Learning Platforms Introduction
11.2.4 Alteryx Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.2.5 Alteryx Recent Developments
11.3 IBM
11.3.1 IBM Company Details
11.3.2 IBM Business Overview
11.3.3 IBM Data Science and Machine-Learning Platforms Introduction
11.3.4 IBM Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.3.5 IBM Recent Developments
11.4 RapidMiner
11.4.1 RapidMiner Company Details
11.4.2 RapidMiner Business Overview
11.4.3 RapidMiner Data Science and Machine-Learning Platforms Introduction
11.4.4 RapidMiner Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.4.5 RapidMiner Recent Developments
11.5 KNIME
11.5.1 KNIME Company Details
11.5.2 KNIME Business Overview
11.5.3 KNIME Data Science and Machine-Learning Platforms Introduction
11.5.4 KNIME Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.5.5 KNIME Recent Developments
11.6 Microsoft
11.6.1 Microsoft Company Details
11.6.2 Microsoft Business Overview
11.6.3 Microsoft Data Science and Machine-Learning Platforms Introduction
11.6.4 Microsoft Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.6.5 Microsoft Recent Developments
11.7 Dataiku
11.7.1 Dataiku Company Details
11.7.2 Dataiku Business Overview
11.7.3 Dataiku Data Science and Machine-Learning Platforms Introduction
11.7.4 Dataiku Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.7.5 Dataiku Recent Developments
11.8 Databricks
11.8.1 Databricks Company Details
11.8.2 Databricks Business Overview
11.8.3 Databricks Data Science and Machine-Learning Platforms Introduction
11.8.4 Databricks Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.8.5 Databricks Recent Developments
11.9 TIBCO Software
11.9.1 TIBCO Software Company Details
11.9.2 TIBCO Software Business Overview
11.9.3 TIBCO Software Data Science and Machine-Learning Platforms Introduction
11.9.4 TIBCO Software Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.9.5 TIBCO Software Recent Developments
11.10 MathWorks
11.10.1 MathWorks Company Details
11.10.2 MathWorks Business Overview
11.10.3 MathWorks Data Science and Machine-Learning Platforms Introduction
11.10.4 MathWorks Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.10.5 MathWorks Recent Developments
11.11 H20.ai
11.11.1 H20.ai Company Details
11.11.2 H20.ai Business Overview
11.11.3 H20.ai Data Science and Machine-Learning Platforms Introduction
11.11.4 H20.ai Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.11.5 H20.ai Recent Developments
11.12 Anaconda
11.12.1 Anaconda Company Details
11.12.2 Anaconda Business Overview
11.12.3 Anaconda Data Science and Machine-Learning Platforms Introduction
11.12.4 Anaconda Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.12.5 Anaconda Recent Developments
11.13 SAP
11.13.1 SAP Company Details
11.13.2 SAP Business Overview
11.13.3 SAP Data Science and Machine-Learning Platforms Introduction
11.13.4 SAP Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.13.5 SAP Recent Developments
11.14 Google
11.14.1 Google Company Details
11.14.2 Google Business Overview
11.14.3 Google Data Science and Machine-Learning Platforms Introduction
11.14.4 Google Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.14.5 Google Recent Developments
11.15 Domino Data Lab
11.15.1 Domino Data Lab Company Details
11.15.2 Domino Data Lab Business Overview
11.15.3 Domino Data Lab Data Science and Machine-Learning Platforms Introduction
11.15.4 Domino Data Lab Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.15.5 Domino Data Lab Recent Developments
11.16 Angoss
11.16.1 Angoss Company Details
11.16.2 Angoss Business Overview
11.16.3 Angoss Data Science and Machine-Learning Platforms Introduction
11.16.4 Angoss Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.16.5 Angoss Recent Developments
11.17 Lexalytics
11.17.1 Lexalytics Company Details
11.17.2 Lexalytics Business Overview
11.17.3 Lexalytics Data Science and Machine-Learning Platforms Introduction
11.17.4 Lexalytics Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.17.5 Lexalytics Recent Developments
11.18 Rapid Insight
11.18.1 Rapid Insight Company Details
11.18.2 Rapid Insight Business Overview
11.18.3 Rapid Insight Data Science and Machine-Learning Platforms Introduction
11.18.4 Rapid Insight Revenue in Data Science and Machine-Learning Platforms Business (2018-2023)
11.18.5 Rapid Insight Recent Developments
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details
SAS
Alteryx
IBM
RapidMiner
KNIME
Microsoft
Dataiku
Databricks
TIBCO Software
MathWorks
H20.ai
Anaconda
SAP
Google
Domino Data Lab
Angoss
Lexalytics
Rapid Insight
听
听
*If Applicable.
