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Home > Data Science and Machine-Learning Platforms - Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030

Data Science and Machine-Learning Platforms - Global Market Share and Ranking, Overall Sales and Demand Forecast 2024-2030

Publication ID: 
PB509246023
Publication Date: 
September 16, 2024
Pages: 
122
Publisher: 
MRRPB5
Countries: 
Global [1]
USD 3,950.00

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IT Services [2]
Computing & Electronics [3]
Description: 

The global market for Data Science and Machine-Learning Platforms was estimated to be worth US$ million in 2023 and is forecast to a readjusted size of US$ million by 2030 with a CAGR of % during the forecast period 2024-2030.
North American market for Data Science and Machine-Learning Platforms was valued at $ million in 2023 and will reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for Data Science and Machine-Learning Platforms was valued at $ million in 2023 and will reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Europe market for Data Science and Machine-Learning Platforms was valued at $ million in 2023 and will reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
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 2023, the global five largest players hold a share approximately % in terms of revenue.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Data Science and Machine-Learning Platforms, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Data Science and Machine-Learning Platforms by region & country, by Type, and by Application.
The Data Science and Machine-Learning Platforms market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Data Science and Machine-Learning Platforms.
Market Segmentation
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
U.S.
Canada
Europe
Germany
France
U.K.
Italy
Russia
Asia-Pacific
China
Japan
South Korea
China Taiwan
Southeast Asia
India
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
Turkey
Saudi Arabia
UAE
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Data Science and Machine-Learning Platforms manufacturers competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Data Science and Machine-Learning Platforms in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Data Science and Machine-Learning Platforms in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.

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Table Of Contents: 

1 Market Overview
1.1 Data Science and Machine-Learning Platforms Product Introduction
1.2 Global Data Science and Machine-Learning Platforms Market Size Forecast
1.3 Data Science and Machine-Learning Platforms Market Trends & Drivers
1.3.1 Data Science and Machine-Learning Platforms Industry Trends
1.3.2 Data Science and Machine-Learning Platforms Market Drivers & Opportunity
1.3.3 Data Science and Machine-Learning Platforms Market Challenges
1.3.4 Data Science and Machine-Learning Platforms Market Restraints
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Competitive Analysis by Company
2.1 Global Data Science and Machine-Learning Platforms Players Revenue Ranking (2023)
2.2 Global Data Science and Machine-Learning Platforms Revenue by Company (2019-2024)
2.3 Key Companies Data Science and Machine-Learning Platforms Manufacturing Base Distribution and Headquarters
2.4 Key Companies Data Science and Machine-Learning Platforms Product Offered
2.5 Key Companies Time to Begin Mass Production of Data Science and Machine-Learning Platforms
2.6 Data Science and Machine-Learning Platforms Market Competitive Analysis
2.6.1 Data Science and Machine-Learning Platforms Market Concentration Rate (2019-2024)
2.6.2 Global 5 and 10 Largest Companies by Data Science and Machine-Learning Platforms Revenue in 2023
2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Data Science and Machine-Learning Platforms as of 2023)
2.7 Mergers & Acquisitions, Expansion
3 Segmentation by Type
3.1 Introduction by Type
3.1.1 Open Source Data Integration Tools
3.1.2 Cloud-based Data Integration Tools
3.2 Global Data Science and Machine-Learning Platforms Sales Value by Type
3.2.1 Global Data Science and Machine-Learning Platforms Sales Value by Type (2019 VS 2023 VS 2030)
3.2.2 Global Data Science and Machine-Learning Platforms Sales Value, by Type (2019-2030)
3.2.3 Global Data Science and Machine-Learning Platforms Sales Value, by Type (%) (2019-2030)
4 Segmentation by Application
4.1 Introduction by Application
4.1.1 Small-Sized Enterprises
4.1.2 Medium-Sized Enterprise
4.1.3 Large Enterprises
4.2 Global Data Science and Machine-Learning Platforms Sales Value by Application
4.2.1 Global Data Science and Machine-Learning Platforms Sales Value by Application (2019 VS 2023 VS 2030)
4.2.2 Global Data Science and Machine-Learning Platforms Sales Value, by Application (2019-2030)
4.2.3 Global Data Science and Machine-Learning Platforms Sales Value, by Application (%) (2019-2030)
5 Segmentation by Region
5.1 Global Data Science and Machine-Learning Platforms Sales Value by Region
5.1.1 Global Data Science and Machine-Learning Platforms Sales Value by Region: 2019 VS 2023 VS 2030
5.1.2 Global Data Science and Machine-Learning Platforms Sales Value by Region (2019-2024)
5.1.3 Global Data Science and Machine-Learning Platforms Sales Value by Region (2025-2030)
5.1.4 Global Data Science and Machine-Learning Platforms Sales Value by Region (%), (2019-2030)
5.2 North America
5.2.1 North America Data Science and Machine-Learning Platforms Sales Value, 2019-2030
5.2.2 North America Data Science and Machine-Learning Platforms Sales Value by Country (%), 2023 VS 2030
5.3 Europe
5.3.1 Europe Data Science and Machine-Learning Platforms Sales Value, 2019-2030
5.3.2 Europe Data Science and Machine-Learning Platforms Sales Value by Country (%), 2023 VS 2030
5.4 Asia Pacific
5.4.1 Asia Pacific Data Science and Machine-Learning Platforms Sales Value, 2019-2030
5.4.2 Asia Pacific Data Science and Machine-Learning Platforms Sales Value by Country (%), 2023 VS 2030
5.5 South America
5.5.1 South America Data Science and Machine-Learning Platforms Sales Value, 2019-2030
5.5.2 South America Data Science and Machine-Learning Platforms Sales Value by Country (%), 2023 VS 2030
5.6 Middle East & Africa
5.6.1 Middle East & Africa Data Science and Machine-Learning Platforms Sales Value, 2019-2030
5.6.2 Middle East & Africa Data Science and Machine-Learning Platforms Sales Value by Country (%), 2023 VS 2030
6 Segmentation by Key Countries/Regions
6.1 Key Countries/Regions Data Science and Machine-Learning Platforms Sales Value Growth Trends, 2019 VS 2023 VS 2030
6.2 Key Countries/Regions Data Science and Machine-Learning Platforms Sales Value
6.3 United States
6.3.1 United States Data Science and Machine-Learning Platforms Sales Value, 2019-2030
6.3.2 United States Data Science and Machine-Learning Platforms Sales Value by Type (%), 2023 VS 2030
6.3.3 United States Data Science and Machine-Learning Platforms Sales Value by Application, 2023 VS 2030
6.4 Europe
6.4.1 Europe Data Science and Machine-Learning Platforms Sales Value, 2019-2030
6.4.2 Europe Data Science and Machine-Learning Platforms Sales Value by Type (%), 2023 VS 2030
6.4.3 Europe Data Science and Machine-Learning Platforms Sales Value by Application, 2023 VS 2030
6.5 China
6.5.1 China Data Science and Machine-Learning Platforms Sales Value, 2019-2030
6.5.2 China Data Science and Machine-Learning Platforms Sales Value by Type (%), 2023 VS 2030
6.5.3 China Data Science and Machine-Learning Platforms Sales Value by Application, 2023 VS 2030
6.6 Japan
6.6.1 Japan Data Science and Machine-Learning Platforms Sales Value, 2019-2030
6.6.2 Japan Data Science and Machine-Learning Platforms Sales Value by Type (%), 2023 VS 2030
6.6.3 Japan Data Science and Machine-Learning Platforms Sales Value by Application, 2023 VS 2030
6.7 South Korea
6.7.1 South Korea Data Science and Machine-Learning Platforms Sales Value, 2019-2030
6.7.2 South Korea Data Science and Machine-Learning Platforms Sales Value by Type (%), 2023 VS 2030
6.7.3 South Korea Data Science and Machine-Learning Platforms Sales Value by Application, 2023 VS 2030
6.8 Southeast Asia
6.8.1 Southeast Asia Data Science and Machine-Learning Platforms Sales Value, 2019-2030
6.8.2 Southeast Asia Data Science and Machine-Learning Platforms Sales Value by Type (%), 2023 VS 2030
6.8.3 Southeast Asia Data Science and Machine-Learning Platforms Sales Value by Application, 2023 VS 2030
6.9 India
6.9.1 India Data Science and Machine-Learning Platforms Sales Value, 2019-2030
6.9.2 India Data Science and Machine-Learning Platforms Sales Value by Type (%), 2023 VS 2030
6.9.3 India Data Science and Machine-Learning Platforms Sales Value by Application, 2023 VS 2030
7 Company Profiles
7.1 SAS
7.1.1 SAS Profile
7.1.2 SAS Main Business
7.1.3 SAS Data Science and Machine-Learning Platforms Products, Services and Solutions
7.1.4 SAS Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.1.5 SAS Recent Developments
7.2 Alteryx
7.2.1 Alteryx Profile
7.2.2 Alteryx Main Business
7.2.3 Alteryx Data Science and Machine-Learning Platforms Products, Services and Solutions
7.2.4 Alteryx Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.2.5 Alteryx Recent Developments
7.3 IBM
7.3.1 IBM Profile
7.3.2 IBM Main Business
7.3.3 IBM Data Science and Machine-Learning Platforms Products, Services and Solutions
7.3.4 IBM Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.3.5 RapidMiner Recent Developments
7.4 RapidMiner
7.4.1 RapidMiner Profile
7.4.2 RapidMiner Main Business
7.4.3 RapidMiner Data Science and Machine-Learning Platforms Products, Services and Solutions
7.4.4 RapidMiner Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.4.5 RapidMiner Recent Developments
7.5 KNIME
7.5.1 KNIME Profile
7.5.2 KNIME Main Business
7.5.3 KNIME Data Science and Machine-Learning Platforms Products, Services and Solutions
7.5.4 KNIME Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.5.5 KNIME Recent Developments
7.6 Microsoft
7.6.1 Microsoft Profile
7.6.2 Microsoft Main Business
7.6.3 Microsoft Data Science and Machine-Learning Platforms Products, Services and Solutions
7.6.4 Microsoft Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.6.5 Microsoft Recent Developments
7.7 Dataiku
7.7.1 Dataiku Profile
7.7.2 Dataiku Main Business
7.7.3 Dataiku Data Science and Machine-Learning Platforms Products, Services and Solutions
7.7.4 Dataiku Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.7.5 Dataiku Recent Developments
7.8 Databricks
7.8.1 Databricks Profile
7.8.2 Databricks Main Business
7.8.3 Databricks Data Science and Machine-Learning Platforms Products, Services and Solutions
7.8.4 Databricks Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.8.5 Databricks Recent Developments
7.9 TIBCO Software
7.9.1 TIBCO Software Profile
7.9.2 TIBCO Software Main Business
7.9.3 TIBCO Software Data Science and Machine-Learning Platforms Products, Services and Solutions
7.9.4 TIBCO Software Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.9.5 TIBCO Software Recent Developments
7.10 MathWorks
7.10.1 MathWorks Profile
7.10.2 MathWorks Main Business
7.10.3 MathWorks Data Science and Machine-Learning Platforms Products, Services and Solutions
7.10.4 MathWorks Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.10.5 MathWorks Recent Developments
7.11 H20.ai
7.11.1 H20.ai Profile
7.11.2 H20.ai Main Business
7.11.3 H20.ai Data Science and Machine-Learning Platforms Products, Services and Solutions
7.11.4 H20.ai Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.11.5 H20.ai Recent Developments
7.12 Anaconda
7.12.1 Anaconda Profile
7.12.2 Anaconda Main Business
7.12.3 Anaconda Data Science and Machine-Learning Platforms Products, Services and Solutions
7.12.4 Anaconda Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.12.5 Anaconda Recent Developments
7.13 SAP
7.13.1 SAP Profile
7.13.2 SAP Main Business
7.13.3 SAP Data Science and Machine-Learning Platforms Products, Services and Solutions
7.13.4 SAP Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.13.5 SAP Recent Developments
7.14 Google
7.14.1 Google Profile
7.14.2 Google Main Business
7.14.3 Google Data Science and Machine-Learning Platforms Products, Services and Solutions
7.14.4 Google Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.14.5 Google Recent Developments
7.15 Domino Data Lab
7.15.1 Domino Data Lab Profile
7.15.2 Domino Data Lab Main Business
7.15.3 Domino Data Lab Data Science and Machine-Learning Platforms Products, Services and Solutions
7.15.4 Domino Data Lab Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.15.5 Domino Data Lab Recent Developments
7.16 Angoss
7.16.1 Angoss Profile
7.16.2 Angoss Main Business
7.16.3 Angoss Data Science and Machine-Learning Platforms Products, Services and Solutions
7.16.4 Angoss Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.16.5 Angoss Recent Developments
7.17 Lexalytics
7.17.1 Lexalytics Profile
7.17.2 Lexalytics Main Business
7.17.3 Lexalytics Data Science and Machine-Learning Platforms Products, Services and Solutions
7.17.4 Lexalytics Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.17.5 Lexalytics Recent Developments
7.18 Rapid Insight
7.18.1 Rapid Insight Profile
7.18.2 Rapid Insight Main Business
7.18.3 Rapid Insight Data Science and Machine-Learning Platforms Products, Services and Solutions
7.18.4 Rapid Insight Data Science and Machine-Learning Platforms Revenue (US$ Million) & (2019-2024)
7.18.5 Rapid Insight Recent Developments
8 Industry Chain Analysis
8.1 Data Science and Machine-Learning Platforms Industrial Chain
8.2 Data Science and Machine-Learning Platforms Upstream Analysis
8.2.1 Key Raw Materials
8.2.2 Raw Materials Key Suppliers
8.2.3 Manufacturing Cost Structure
8.3 Midstream Analysis
8.4 Downstream Analysis (Customers Analysis)
8.5 Sales Model and Sales Channels
8.5.1 Data Science and Machine-Learning Platforms Sales Model
8.5.2 Sales Channel
8.5.3 Data Science and Machine-Learning Platforms Distributors
9 Research Findings and Conclusion
10 Appendix
10.1 Research Methodology
10.1.1 Methodology/Research Approach
10.1.2 Data Source
10.2 Author Details
10.3 Disclaimer

Companies Mentioned: 

SAS
Alteryx
IBM
RapidMiner
KNIME
Microsoft
Dataiku
Databricks
TIBCO Software
MathWorks
H20.ai
Anaconda
SAP
Google
Domino Data Lab
Angoss
Lexalytics
Rapid Insight

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