Global Automated Data Science and Machine Learning Platforms Market Research Report 2022
Global Automated Data Science and Machine Learning Platforms Market Overview:
Global Automated Data Science and Machine Learning Platforms Market Report 2022 comes with the extensive industry analysis by Introspective Market Research with development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2022-2028.This research study of Automated Data Science and Machine Learning Platforms involved the extensive usage of both primary and secondary data sources. This includes the study of various parameters affecting the industry, including the government policy, market environment, competitive landscape, historical data, present trends in the market, technological innovation, upcoming technologies and the technical progress in related industry.
Scope of the Automated Data Science and Machine Learning Platforms Market
The Automated Data Science and Machine Learning Platforms Market Research report incorporate value chain analysis for each of the product type. Value chain analysis offers in depth information about value addition at each stage.The study includes drivers and restraints for Automated Data Science and Machine Learning Platforms Market along with their impact on demand during the forecast period. The study also provides key market indicators affecting thegrowth of the market. Research report includes major key player analysis with shares of each player inside market, growth rate and market attractiveness in different endusers/regions. Our study Automated Data Science and Machine Learning Platforms Market helps user to make precise decision in order to expand their market presence and increase market share.
Impact of COVID-19 on Automated Data Science and Machine Learning Platforms Market
Report covers Impact of Coronavirus COVID-19: Since the COVID-19 virus outbreak in December 2019, the disease has spread to almost every country around the globe with the World Health Organization declaring it a public health emergency. The global impacts of the coronavirus disease 2019 (COVID-19) are already starting to be felt, and will significantly affect the Automated Data Science and Machine Learning Platforms market in 2020. The outbreak of COVID-19 has brought effects on many aspects, like flight cancellations; travel bans and quarantines; restaurants closed; all indoor/outdoor events restricted; over forty countries state of emergency declared; massive slowing of the supply chain; stock market volatility; falling business confidence, growing panic among the population, and uncertainty about future.
Global Automated Data Science and Machine Learning Platforms Market Segmentation
Global Automated Data Science and Machine Learning Platforms Market Research report comprises of Porter's five forces analysis to do the detail study about its each segmentation like Product segmentation, End user/application segment analysis and Major key players analysis mentioned as below;
By Type, Automated Data Science and Machine Learning Platforms market has been segmented into:
Cloud-based
On-premises
By Application, Automated Data Science and Machine Learning Platforms market has been segmented into:
Small and Medium Enterprises (SMEs)
Large Enterprises
Regional Analysis:
North America (U.S., Canada, Mexico)
Europe (Germany, U.K., France, Italy, Russia, Spain, Rest of Europe)
Asia-Pacific (China, India, Japan, Singapore, Australia, New Zealand, Rest of APAC)
South America (Brazil, Argentina, Rest of SA)
Middle East & Africa (Turkey, Saudi Arabia, Iran, UAE, Africa, Rest of MEA)
Competitive Landscape:
Competitive analysis is the study of strength and weakness, market investment, market share, market sales volume, market trends of major players in the market.The Automated Data Science and Machine Learning Platforms market study focused on including all the primary level, secondary level and tertiary level competitors in the report. The data generated by conducting the primary and secondary research.The report covers detail analysis of driver, constraints and scope for new players entering the Automated Data Science and Machine Learning Platforms market.
Top Key Players Covered in Automated Data Science and Machine Learning Platforms market are:
TIBCO Software
Palantier
Databricks
IBM
Microsoft
SAS
KNIME
Altair
Google
Dataiku
MathWorks
Domino
Anaconda
DataRobot
RapidMiner
Alteryx
H2O.ai
Objective to buy this Report:
1. Automated Data Science and Machine Learning Platforms analysis predicts the representation of this market, supply and demand, capacity, detailed investigations, etc.
2. Even the report, along with the international series, conducts an in-depth study of rules, policies and current policy.
3. In addition, additional factors are mentioned: imports, arrangement of commodity prices for the market, supply and demand of industry products, major manufacturers.
4. The report starts with Automated Data Science and Machine Learning Platforms market statistics and moves to important points, with dependent markets categorized by market trend by application.
5. Applications of market may also be assessed based on their performances.
6. Other market attributes, such as future aspects, limitations and growth for all departments.
Global Automated Data Science and Machine Learning Platforms Market Report 2022 comes with the extensive industry analysis by Introspective Market Research with development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2022-2028.This research study of Automated Data Science and Machine Learning Platforms involved the extensive usage of both primary and secondary data sources. This includes the study of various parameters affecting the industry, including the government policy, market environment, competitive landscape, historical data, present trends in the market, technological innovation, upcoming technologies and the technical progress in related industry.
Scope of the Automated Data Science and Machine Learning Platforms Market
The Automated Data Science and Machine Learning Platforms Market Research report incorporate value chain analysis for each of the product type. Value chain analysis offers in depth information about value addition at each stage.The study includes drivers and restraints for Automated Data Science and Machine Learning Platforms Market along with their impact on demand during the forecast period. The study also provides key market indicators affecting thegrowth of the market. Research report includes major key player analysis with shares of each player inside market, growth rate and market attractiveness in different endusers/regions. Our study Automated Data Science and Machine Learning Platforms Market helps user to make precise decision in order to expand their market presence and increase market share.
Impact of COVID-19 on Automated Data Science and Machine Learning Platforms Market
Report covers Impact of Coronavirus COVID-19: Since the COVID-19 virus outbreak in December 2019, the disease has spread to almost every country around the globe with the World Health Organization declaring it a public health emergency. The global impacts of the coronavirus disease 2019 (COVID-19) are already starting to be felt, and will significantly affect the Automated Data Science and Machine Learning Platforms market in 2020. The outbreak of COVID-19 has brought effects on many aspects, like flight cancellations; travel bans and quarantines; restaurants closed; all indoor/outdoor events restricted; over forty countries state of emergency declared; massive slowing of the supply chain; stock market volatility; falling business confidence, growing panic among the population, and uncertainty about future.
Global Automated Data Science and Machine Learning Platforms Market Segmentation
Global Automated Data Science and Machine Learning Platforms Market Research report comprises of Porter's five forces analysis to do the detail study about its each segmentation like Product segmentation, End user/application segment analysis and Major key players analysis mentioned as below;
By Type, Automated Data Science and Machine Learning Platforms market has been segmented into:
Cloud-based
On-premises
By Application, Automated Data Science and Machine Learning Platforms market has been segmented into:
Small and Medium Enterprises (SMEs)
Large Enterprises
Regional Analysis:
North America (U.S., Canada, Mexico)
Europe (Germany, U.K., France, Italy, Russia, Spain, Rest of Europe)
Asia-Pacific (China, India, Japan, Singapore, Australia, New Zealand, Rest of APAC)
South America (Brazil, Argentina, Rest of SA)
Middle East & Africa (Turkey, Saudi Arabia, Iran, UAE, Africa, Rest of MEA)
Competitive Landscape:
Competitive analysis is the study of strength and weakness, market investment, market share, market sales volume, market trends of major players in the market.The Automated Data Science and Machine Learning Platforms market study focused on including all the primary level, secondary level and tertiary level competitors in the report. The data generated by conducting the primary and secondary research.The report covers detail analysis of driver, constraints and scope for new players entering the Automated Data Science and Machine Learning Platforms market.
Top Key Players Covered in Automated Data Science and Machine Learning Platforms market are:
TIBCO Software
Palantier
Databricks
IBM
Microsoft
SAS
KNIME
Altair
Dataiku
MathWorks
Domino
Anaconda
DataRobot
RapidMiner
Alteryx
H2O.ai
Objective to buy this Report:
1. Automated Data Science and Machine Learning Platforms analysis predicts the representation of this market, supply and demand, capacity, detailed investigations, etc.
2. Even the report, along with the international series, conducts an in-depth study of rules, policies and current policy.
3. In addition, additional factors are mentioned: imports, arrangement of commodity prices for the market, supply and demand of industry products, major manufacturers.
4. The report starts with Automated Data Science and Machine Learning Platforms market statistics and moves to important points, with dependent markets categorized by market trend by application.
5. Applications of market may also be assessed based on their performances.
6. Other market attributes, such as future aspects, limitations and growth for all departments.
CHAPTER 1: INTRODUCTION
1.1 RESEARCH OBJECTIVES
1.2 RESEARCH METHODOLOGY
1.3 RESEARCH PROCESS
1.4 SCOPE AND COVERAGE
1.4.1 MARKET DEFINITION
1.4.2 KEY QUESTIONS ANSWERED
1.5 MARKET SEGMENTATION
CHAPTER 2:EXECUTIVE SUMMARY
CHAPTER 3:GROWTH OPPORTUNITIES BY SEGMENT
3.1 BY TYPE
3.2 BY APPLICATION
CHAPTER 4: MARKET LANDSCAPE
4.1 PORTER'S FIVE FORCES ANALYSIS
4.1.1 BARGAINING POWER OF SUPPLIER
4.1.2 THREAT OF NEW ENTRANTS
4.1.3 THREAT OF SUBSTITUTES
4.1.4 COMPETITIVE RIVALRY
4.1.5 BARGAINING POWER AMONG BUYERS
4.2 INDUSTRY VALUE CHAIN ANALYSIS
4.3 MARKET DYNAMICS
4.3.1 DRIVERS
4.3.2 RESTRAINTS
4.3.3 OPPORTUNITIES
4.5.4 CHALLENGES
4.4 PESTLE ANALYSIS
4.5 TECHNOLOGICAL ROADMAP
4.6 REGULATORY LANDSCAPE
4.7 SWOT ANALYSIS
4.8 PRICE TREND ANALYSIS
4.9 PATENT ANALYSIS
4.10 ANALYSIS OF THE IMPACT OF COVID-19
4.10.1 IMPACT ON THE OVERALL MARKET
4.10.2 IMPACT ON THE SUPPLY CHAIN
4.10.3 IMPACT ON THE KEY MANUFACTURERS
4.10.4 IMPACT ON THE PRICING
CHAPTER 5: AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BY TYPE
5.1 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW SNAPSHOT AND GROWTH ENGINE
5.2 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW
5.3 CLOUD-BASED
5.3.1 INTRODUCTION AND MARKET OVERVIEW
5.3.2 HISTORIC AND FORECASTED MARKET SIZE (2016-2028F)
5.3.3 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
5.3.4 CLOUD-BASED: GEOGRAPHIC SEGMENTATION
5.4 ON-PREMISES
5.4.1 INTRODUCTION AND MARKET OVERVIEW
5.4.2 HISTORIC AND FORECASTED MARKET SIZE (2016-2028F)
5.4.3 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
5.4.4 ON-PREMISES: GEOGRAPHIC SEGMENTATION
CHAPTER 6: AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BY APPLICATION
6.1 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW SNAPSHOT AND GROWTH ENGINE
6.2 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW
6.3 SMALL AND MEDIUM ENTERPRISES (SMES)
6.3.1 INTRODUCTION AND MARKET OVERVIEW
6.3.2 HISTORIC AND FORECASTED MARKET SIZE (2016-2028F)
6.3.3 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
6.3.4 SMALL AND MEDIUM ENTERPRISES (SMES): GEOGRAPHIC SEGMENTATION
6.4 LARGE ENTERPRISES
6.4.1 INTRODUCTION AND MARKET OVERVIEW
6.4.2 HISTORIC AND FORECASTED MARKET SIZE (2016-2028F)
6.4.3 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
6.4.4 LARGE ENTERPRISES: GEOGRAPHIC SEGMENTATION
CHAPTER 7: COMPANY PROFILES AND COMPETITIVE ANALYSIS
7.1 COMPETITIVE LANDSCAPE
7.1.1 COMPETITIVE POSITIONING
7.1.2 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS SALES AND MARKET SHARE BY PLAYERS
7.1.3 INDUSTRY BCG MATRIX
7.1.4 HEAT MAP ANALYSIS
7.1.5 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS INDUSTRY CONCENTRATION RATIO (CR5 AND HHI)
7.1.6 TOP 5 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS PLAYERS MARKET SHARE
7.1.7 MERGERS AND ACQUISITIONS
7.1.8 BUSINESS STRATEGIES BY TOP PLAYERS
7.2 TIBCO SOFTWARE
7.2.1 COMPANY OVERVIEW
7.2.2 KEY EXECUTIVES
7.2.3 COMPANY SNAPSHOT
7.2.4 OPERATING BUSINESS SEGMENTS
7.2.5 PRODUCT PORTFOLIO
7.2.6 BUSINESS PERFORMANCE
7.2.7 KEY STRATEGIC MOVES AND RECENT DEVELOPMENTS
7.2.8 SWOT ANALYSIS
7.3 PALANTIER
7.4 DATABRICKS
7.5 IBM
7.6 MICROSOFT
7.7 SAS
7.8 KNIME
7.9 ALTAIR
7.10 GOOGLE
7.11 DATAIKU
7.12 MATHWORKS
7.13 DOMINO
7.14 ANACONDA
7.15 DATAROBOT
7.16 RAPIDMINER
7.17 ALTERYX
7.18 H2O.AI
CHAPTER 8: GLOBAL AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
8.1 MARKET OVERVIEW
8.2 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
8.2.1 CLOUD-BASED
8.2.2 ON-PREMISES
8.3 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
8.3.1 SMALL AND MEDIUM ENTERPRISES (SMES)
8.3.2 LARGE ENTERPRISES
CHAPTER 9: NORTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
9.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
9.2 IMPACT OF COVID-19
9.3 KEY PLAYERS
9.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
9.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
9.4.1 CLOUD-BASED
9.4.2 ON-PREMISES
9.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
9.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
9.5.2 LARGE ENTERPRISES
9.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
9.6.1 U.S.
9.6.2 CANADA
9.6.3 MEXICO
CHAPTER 10: EUROPE AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
10.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
10.2 IMPACT OF COVID-19
10.3 KEY PLAYERS
10.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
10.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
10.4.1 CLOUD-BASED
10.4.2 ON-PREMISES
10.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
10.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
10.5.2 LARGE ENTERPRISES
10.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
10.6.1 GERMANY
10.6.2 U.K.
10.6.3 FRANCE
10.6.4 ITALY
10.6.5 RUSSIA
10.6.6 SPAIN
10.6.7 REST OF EUROPE
CHAPTER 11: ASIA-PACIFIC AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
11.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
11.2 IMPACT OF COVID-19
11.3 KEY PLAYERS
11.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
11.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
11.4.1 CLOUD-BASED
11.4.2 ON-PREMISES
11.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
11.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
11.5.2 LARGE ENTERPRISES
11.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
11.6.1 CHINA
11.6.2 INDIA
11.6.3 JAPAN
11.6.4 SINGAPORE
11.6.5 AUSTRALIA
11.6.6 NEW ZEALAND
11.6.7 REST OF APAC
CHAPTER 12: MIDDLE EAST & AFRICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
12.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
12.2 IMPACT OF COVID-19
12.3 KEY PLAYERS
12.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
12.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
12.4.1 CLOUD-BASED
12.4.2 ON-PREMISES
12.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
12.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
12.5.2 LARGE ENTERPRISES
12.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
12.6.1 TURKEY
12.6.2 SAUDI ARABIA
12.6.3 IRAN
12.6.4 UAE
12.6.5 AFRICA
12.6.6 REST OF MEA
CHAPTER 13: SOUTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
13.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
13.2 IMPACT OF COVID-19
13.3 KEY PLAYERS
13.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
13.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
13.4.1 CLOUD-BASED
13.4.2 ON-PREMISES
13.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
13.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
13.5.2 LARGE ENTERPRISES
13.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
13.6.1 BRAZIL
13.6.2 ARGENTINA
13.6.3 REST OF SA
CHAPTER 14 INVESTMENT ANALYSIS
CHAPTER 15 ANALYST VIEWPOINT AND CONCLUSION
1.1 RESEARCH OBJECTIVES
1.2 RESEARCH METHODOLOGY
1.3 RESEARCH PROCESS
1.4 SCOPE AND COVERAGE
1.4.1 MARKET DEFINITION
1.4.2 KEY QUESTIONS ANSWERED
1.5 MARKET SEGMENTATION
CHAPTER 2:EXECUTIVE SUMMARY
CHAPTER 3:GROWTH OPPORTUNITIES BY SEGMENT
3.1 BY TYPE
3.2 BY APPLICATION
CHAPTER 4: MARKET LANDSCAPE
4.1 PORTER'S FIVE FORCES ANALYSIS
4.1.1 BARGAINING POWER OF SUPPLIER
4.1.2 THREAT OF NEW ENTRANTS
4.1.3 THREAT OF SUBSTITUTES
4.1.4 COMPETITIVE RIVALRY
4.1.5 BARGAINING POWER AMONG BUYERS
4.2 INDUSTRY VALUE CHAIN ANALYSIS
4.3 MARKET DYNAMICS
4.3.1 DRIVERS
4.3.2 RESTRAINTS
4.3.3 OPPORTUNITIES
4.5.4 CHALLENGES
4.4 PESTLE ANALYSIS
4.5 TECHNOLOGICAL ROADMAP
4.6 REGULATORY LANDSCAPE
4.7 SWOT ANALYSIS
4.8 PRICE TREND ANALYSIS
4.9 PATENT ANALYSIS
4.10 ANALYSIS OF THE IMPACT OF COVID-19
4.10.1 IMPACT ON THE OVERALL MARKET
4.10.2 IMPACT ON THE SUPPLY CHAIN
4.10.3 IMPACT ON THE KEY MANUFACTURERS
4.10.4 IMPACT ON THE PRICING
CHAPTER 5: AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BY TYPE
5.1 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW SNAPSHOT AND GROWTH ENGINE
5.2 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW
5.3 CLOUD-BASED
5.3.1 INTRODUCTION AND MARKET OVERVIEW
5.3.2 HISTORIC AND FORECASTED MARKET SIZE (2016-2028F)
5.3.3 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
5.3.4 CLOUD-BASED: GEOGRAPHIC SEGMENTATION
5.4 ON-PREMISES
5.4.1 INTRODUCTION AND MARKET OVERVIEW
5.4.2 HISTORIC AND FORECASTED MARKET SIZE (2016-2028F)
5.4.3 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
5.4.4 ON-PREMISES: GEOGRAPHIC SEGMENTATION
CHAPTER 6: AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BY APPLICATION
6.1 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW SNAPSHOT AND GROWTH ENGINE
6.2 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW
6.3 SMALL AND MEDIUM ENTERPRISES (SMES)
6.3.1 INTRODUCTION AND MARKET OVERVIEW
6.3.2 HISTORIC AND FORECASTED MARKET SIZE (2016-2028F)
6.3.3 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
6.3.4 SMALL AND MEDIUM ENTERPRISES (SMES): GEOGRAPHIC SEGMENTATION
6.4 LARGE ENTERPRISES
6.4.1 INTRODUCTION AND MARKET OVERVIEW
6.4.2 HISTORIC AND FORECASTED MARKET SIZE (2016-2028F)
6.4.3 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
6.4.4 LARGE ENTERPRISES: GEOGRAPHIC SEGMENTATION
CHAPTER 7: COMPANY PROFILES AND COMPETITIVE ANALYSIS
7.1 COMPETITIVE LANDSCAPE
7.1.1 COMPETITIVE POSITIONING
7.1.2 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS SALES AND MARKET SHARE BY PLAYERS
7.1.3 INDUSTRY BCG MATRIX
7.1.4 HEAT MAP ANALYSIS
7.1.5 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS INDUSTRY CONCENTRATION RATIO (CR5 AND HHI)
7.1.6 TOP 5 AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS PLAYERS MARKET SHARE
7.1.7 MERGERS AND ACQUISITIONS
7.1.8 BUSINESS STRATEGIES BY TOP PLAYERS
7.2 TIBCO SOFTWARE
7.2.1 COMPANY OVERVIEW
7.2.2 KEY EXECUTIVES
7.2.3 COMPANY SNAPSHOT
7.2.4 OPERATING BUSINESS SEGMENTS
7.2.5 PRODUCT PORTFOLIO
7.2.6 BUSINESS PERFORMANCE
7.2.7 KEY STRATEGIC MOVES AND RECENT DEVELOPMENTS
7.2.8 SWOT ANALYSIS
7.3 PALANTIER
7.4 DATABRICKS
7.5 IBM
7.6 MICROSOFT
7.7 SAS
7.8 KNIME
7.9 ALTAIR
7.10 GOOGLE
7.11 DATAIKU
7.12 MATHWORKS
7.13 DOMINO
7.14 ANACONDA
7.15 DATAROBOT
7.16 RAPIDMINER
7.17 ALTERYX
7.18 H2O.AI
CHAPTER 8: GLOBAL AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
8.1 MARKET OVERVIEW
8.2 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
8.2.1 CLOUD-BASED
8.2.2 ON-PREMISES
8.3 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
8.3.1 SMALL AND MEDIUM ENTERPRISES (SMES)
8.3.2 LARGE ENTERPRISES
CHAPTER 9: NORTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
9.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
9.2 IMPACT OF COVID-19
9.3 KEY PLAYERS
9.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
9.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
9.4.1 CLOUD-BASED
9.4.2 ON-PREMISES
9.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
9.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
9.5.2 LARGE ENTERPRISES
9.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
9.6.1 U.S.
9.6.2 CANADA
9.6.3 MEXICO
CHAPTER 10: EUROPE AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
10.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
10.2 IMPACT OF COVID-19
10.3 KEY PLAYERS
10.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
10.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
10.4.1 CLOUD-BASED
10.4.2 ON-PREMISES
10.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
10.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
10.5.2 LARGE ENTERPRISES
10.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
10.6.1 GERMANY
10.6.2 U.K.
10.6.3 FRANCE
10.6.4 ITALY
10.6.5 RUSSIA
10.6.6 SPAIN
10.6.7 REST OF EUROPE
CHAPTER 11: ASIA-PACIFIC AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
11.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
11.2 IMPACT OF COVID-19
11.3 KEY PLAYERS
11.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
11.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
11.4.1 CLOUD-BASED
11.4.2 ON-PREMISES
11.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
11.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
11.5.2 LARGE ENTERPRISES
11.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
11.6.1 CHINA
11.6.2 INDIA
11.6.3 JAPAN
11.6.4 SINGAPORE
11.6.5 AUSTRALIA
11.6.6 NEW ZEALAND
11.6.7 REST OF APAC
CHAPTER 12: MIDDLE EAST & AFRICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
12.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
12.2 IMPACT OF COVID-19
12.3 KEY PLAYERS
12.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
12.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
12.4.1 CLOUD-BASED
12.4.2 ON-PREMISES
12.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
12.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
12.5.2 LARGE ENTERPRISES
12.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
12.6.1 TURKEY
12.6.2 SAUDI ARABIA
12.6.3 IRAN
12.6.4 UAE
12.6.5 AFRICA
12.6.6 REST OF MEA
CHAPTER 13: SOUTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET ANALYSIS, INSIGHTS AND FORECAST, 2016-2028
13.1 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
13.2 IMPACT OF COVID-19
13.3 KEY PLAYERS
13.4 KEY MARKET TRENDS, GROWTH FACTORS AND OPPORTUNITIES
13.4 HISTORIC AND FORECASTED MARKET SIZE BY TYPE
13.4.1 CLOUD-BASED
13.4.2 ON-PREMISES
13.5 HISTORIC AND FORECASTED MARKET SIZE BY APPLICATION
13.5.1 SMALL AND MEDIUM ENTERPRISES (SMES)
13.5.2 LARGE ENTERPRISES
13.6 HISTORIC AND FORECAST MARKET SIZE BY COUNTRY
13.6.1 BRAZIL
13.6.2 ARGENTINA
13.6.3 REST OF SA
CHAPTER 14 INVESTMENT ANALYSIS
CHAPTER 15 ANALYST VIEWPOINT AND CONCLUSION
LIST OF TABLES
TABLE 001. EXECUTIVE SUMMARY
TABLE 002. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BARGAINING POWER OF SUPPLIERS
TABLE 003. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BARGAINING POWER OF CUSTOMERS
TABLE 004. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET COMPETITIVE RIVALRY
TABLE 005. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET THREAT OF NEW ENTRANTS
TABLE 006. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET THREAT OF SUBSTITUTES
TABLE 007. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BY TYPE
TABLE 008. CLOUD-BASED MARKET OVERVIEW (2016-2028)
TABLE 009. ON-PREMISES MARKET OVERVIEW (2016-2028)
TABLE 010. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BY APPLICATION
TABLE 011. SMALL AND MEDIUM ENTERPRISES (SMES) MARKET OVERVIEW (2016-2028)
TABLE 012. LARGE ENTERPRISES MARKET OVERVIEW (2016-2028)
TABLE 013. NORTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 014. NORTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 015. N AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 016. EUROPE AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 017. EUROPE AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 018. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 019. ASIA PACIFIC AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 020. ASIA PACIFIC AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 021. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 022. MIDDLE EAST & AFRICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 023. MIDDLE EAST & AFRICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 024. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 025. SOUTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 026. SOUTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 027. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 028. TIBCO SOFTWARE: SNAPSHOT
TABLE 029. TIBCO SOFTWARE: BUSINESS PERFORMANCE
TABLE 030. TIBCO SOFTWARE: PRODUCT PORTFOLIO
TABLE 031. TIBCO SOFTWARE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 031. PALANTIER: SNAPSHOT
TABLE 032. PALANTIER: BUSINESS PERFORMANCE
TABLE 033. PALANTIER: PRODUCT PORTFOLIO
TABLE 034. PALANTIER: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 034. DATABRICKS: SNAPSHOT
TABLE 035. DATABRICKS: BUSINESS PERFORMANCE
TABLE 036. DATABRICKS: PRODUCT PORTFOLIO
TABLE 037. DATABRICKS: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 037. IBM: SNAPSHOT
TABLE 038. IBM: BUSINESS PERFORMANCE
TABLE 039. IBM: PRODUCT PORTFOLIO
TABLE 040. IBM: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 040. MICROSOFT: SNAPSHOT
TABLE 041. MICROSOFT: BUSINESS PERFORMANCE
TABLE 042. MICROSOFT: PRODUCT PORTFOLIO
TABLE 043. MICROSOFT: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 043. SAS: SNAPSHOT
TABLE 044. SAS: BUSINESS PERFORMANCE
TABLE 045. SAS: PRODUCT PORTFOLIO
TABLE 046. SAS: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 046. KNIME: SNAPSHOT
TABLE 047. KNIME: BUSINESS PERFORMANCE
TABLE 048. KNIME: PRODUCT PORTFOLIO
TABLE 049. KNIME: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 049. ALTAIR: SNAPSHOT
TABLE 050. ALTAIR: BUSINESS PERFORMANCE
TABLE 051. ALTAIR: PRODUCT PORTFOLIO
TABLE 052. ALTAIR: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 052. GOOGLE: SNAPSHOT
TABLE 053. GOOGLE: BUSINESS PERFORMANCE
TABLE 054. GOOGLE: PRODUCT PORTFOLIO
TABLE 055. GOOGLE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 055. DATAIKU: SNAPSHOT
TABLE 056. DATAIKU: BUSINESS PERFORMANCE
TABLE 057. DATAIKU: PRODUCT PORTFOLIO
TABLE 058. DATAIKU: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 058. MATHWORKS: SNAPSHOT
TABLE 059. MATHWORKS: BUSINESS PERFORMANCE
TABLE 060. MATHWORKS: PRODUCT PORTFOLIO
TABLE 061. MATHWORKS: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 061. DOMINO: SNAPSHOT
TABLE 062. DOMINO: BUSINESS PERFORMANCE
TABLE 063. DOMINO: PRODUCT PORTFOLIO
TABLE 064. DOMINO: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 064. ANACONDA: SNAPSHOT
TABLE 065. ANACONDA: BUSINESS PERFORMANCE
TABLE 066. ANACONDA: PRODUCT PORTFOLIO
TABLE 067. ANACONDA: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 067. DATAROBOT: SNAPSHOT
TABLE 068. DATAROBOT: BUSINESS PERFORMANCE
TABLE 069. DATAROBOT: PRODUCT PORTFOLIO
TABLE 070. DATAROBOT: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 070. RAPIDMINER: SNAPSHOT
TABLE 071. RAPIDMINER: BUSINESS PERFORMANCE
TABLE 072. RAPIDMINER: PRODUCT PORTFOLIO
TABLE 073. RAPIDMINER: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 073. ALTERYX: SNAPSHOT
TABLE 074. ALTERYX: BUSINESS PERFORMANCE
TABLE 075. ALTERYX: PRODUCT PORTFOLIO
TABLE 076. ALTERYX: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 076. H2O.AI: SNAPSHOT
TABLE 077. H2O.AI: BUSINESS PERFORMANCE
TABLE 078. H2O.AI: PRODUCT PORTFOLIO
TABLE 079. H2O.AI: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 001. EXECUTIVE SUMMARY
TABLE 002. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BARGAINING POWER OF SUPPLIERS
TABLE 003. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BARGAINING POWER OF CUSTOMERS
TABLE 004. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET COMPETITIVE RIVALRY
TABLE 005. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET THREAT OF NEW ENTRANTS
TABLE 006. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET THREAT OF SUBSTITUTES
TABLE 007. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BY TYPE
TABLE 008. CLOUD-BASED MARKET OVERVIEW (2016-2028)
TABLE 009. ON-PREMISES MARKET OVERVIEW (2016-2028)
TABLE 010. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET BY APPLICATION
TABLE 011. SMALL AND MEDIUM ENTERPRISES (SMES) MARKET OVERVIEW (2016-2028)
TABLE 012. LARGE ENTERPRISES MARKET OVERVIEW (2016-2028)
TABLE 013. NORTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 014. NORTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 015. N AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 016. EUROPE AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 017. EUROPE AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 018. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 019. ASIA PACIFIC AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 020. ASIA PACIFIC AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 021. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 022. MIDDLE EAST & AFRICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 023. MIDDLE EAST & AFRICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 024. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 025. SOUTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY TYPE (2016-2028)
TABLE 026. SOUTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY APPLICATION (2016-2028)
TABLE 027. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET, BY COUNTRY (2016-2028)
TABLE 028. TIBCO SOFTWARE: SNAPSHOT
TABLE 029. TIBCO SOFTWARE: BUSINESS PERFORMANCE
TABLE 030. TIBCO SOFTWARE: PRODUCT PORTFOLIO
TABLE 031. TIBCO SOFTWARE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 031. PALANTIER: SNAPSHOT
TABLE 032. PALANTIER: BUSINESS PERFORMANCE
TABLE 033. PALANTIER: PRODUCT PORTFOLIO
TABLE 034. PALANTIER: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 034. DATABRICKS: SNAPSHOT
TABLE 035. DATABRICKS: BUSINESS PERFORMANCE
TABLE 036. DATABRICKS: PRODUCT PORTFOLIO
TABLE 037. DATABRICKS: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 037. IBM: SNAPSHOT
TABLE 038. IBM: BUSINESS PERFORMANCE
TABLE 039. IBM: PRODUCT PORTFOLIO
TABLE 040. IBM: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 040. MICROSOFT: SNAPSHOT
TABLE 041. MICROSOFT: BUSINESS PERFORMANCE
TABLE 042. MICROSOFT: PRODUCT PORTFOLIO
TABLE 043. MICROSOFT: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 043. SAS: SNAPSHOT
TABLE 044. SAS: BUSINESS PERFORMANCE
TABLE 045. SAS: PRODUCT PORTFOLIO
TABLE 046. SAS: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 046. KNIME: SNAPSHOT
TABLE 047. KNIME: BUSINESS PERFORMANCE
TABLE 048. KNIME: PRODUCT PORTFOLIO
TABLE 049. KNIME: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 049. ALTAIR: SNAPSHOT
TABLE 050. ALTAIR: BUSINESS PERFORMANCE
TABLE 051. ALTAIR: PRODUCT PORTFOLIO
TABLE 052. ALTAIR: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 052. GOOGLE: SNAPSHOT
TABLE 053. GOOGLE: BUSINESS PERFORMANCE
TABLE 054. GOOGLE: PRODUCT PORTFOLIO
TABLE 055. GOOGLE: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 055. DATAIKU: SNAPSHOT
TABLE 056. DATAIKU: BUSINESS PERFORMANCE
TABLE 057. DATAIKU: PRODUCT PORTFOLIO
TABLE 058. DATAIKU: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 058. MATHWORKS: SNAPSHOT
TABLE 059. MATHWORKS: BUSINESS PERFORMANCE
TABLE 060. MATHWORKS: PRODUCT PORTFOLIO
TABLE 061. MATHWORKS: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 061. DOMINO: SNAPSHOT
TABLE 062. DOMINO: BUSINESS PERFORMANCE
TABLE 063. DOMINO: PRODUCT PORTFOLIO
TABLE 064. DOMINO: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 064. ANACONDA: SNAPSHOT
TABLE 065. ANACONDA: BUSINESS PERFORMANCE
TABLE 066. ANACONDA: PRODUCT PORTFOLIO
TABLE 067. ANACONDA: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 067. DATAROBOT: SNAPSHOT
TABLE 068. DATAROBOT: BUSINESS PERFORMANCE
TABLE 069. DATAROBOT: PRODUCT PORTFOLIO
TABLE 070. DATAROBOT: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 070. RAPIDMINER: SNAPSHOT
TABLE 071. RAPIDMINER: BUSINESS PERFORMANCE
TABLE 072. RAPIDMINER: PRODUCT PORTFOLIO
TABLE 073. RAPIDMINER: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 073. ALTERYX: SNAPSHOT
TABLE 074. ALTERYX: BUSINESS PERFORMANCE
TABLE 075. ALTERYX: PRODUCT PORTFOLIO
TABLE 076. ALTERYX: KEY STRATEGIC MOVES AND DEVELOPMENTS
TABLE 076. H2O.AI: SNAPSHOT
TABLE 077. H2O.AI: BUSINESS PERFORMANCE
TABLE 078. H2O.AI: PRODUCT PORTFOLIO
TABLE 079. H2O.AI: KEY STRATEGIC MOVES AND DEVELOPMENTS
LIST OF FIGURES
FIGURE 001. YEARS CONSIDERED FOR ANALYSIS
FIGURE 002. SCOPE OF THE STUDY
FIGURE 003. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY REGIONS
FIGURE 004. PORTER'S FIVE FORCES ANALYSIS
FIGURE 005. BARGAINING POWER OF SUPPLIERS
FIGURE 006. COMPETITIVE RIVALRYFIGURE 007. THREAT OF NEW ENTRANTS
FIGURE 008. THREAT OF SUBSTITUTES
FIGURE 009. VALUE CHAIN ANALYSIS
FIGURE 010. PESTLE ANALYSIS
FIGURE 011. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY TYPE
FIGURE 012. CLOUD-BASED MARKET OVERVIEW (2016-2028)
FIGURE 013. ON-PREMISES MARKET OVERVIEW (2016-2028)
FIGURE 014. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY APPLICATION
FIGURE 015. SMALL AND MEDIUM ENTERPRISES (SMES) MARKET OVERVIEW (2016-2028)
FIGURE 016. LARGE ENTERPRISES MARKET OVERVIEW (2016-2028)
FIGURE 017. NORTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 018. EUROPE AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 019. ASIA PACIFIC AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 020. MIDDLE EAST & AFRICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 021. SOUTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 001. YEARS CONSIDERED FOR ANALYSIS
FIGURE 002. SCOPE OF THE STUDY
FIGURE 003. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY REGIONS
FIGURE 004. PORTER'S FIVE FORCES ANALYSIS
FIGURE 005. BARGAINING POWER OF SUPPLIERS
FIGURE 006. COMPETITIVE RIVALRYFIGURE 007. THREAT OF NEW ENTRANTS
FIGURE 008. THREAT OF SUBSTITUTES
FIGURE 009. VALUE CHAIN ANALYSIS
FIGURE 010. PESTLE ANALYSIS
FIGURE 011. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY TYPE
FIGURE 012. CLOUD-BASED MARKET OVERVIEW (2016-2028)
FIGURE 013. ON-PREMISES MARKET OVERVIEW (2016-2028)
FIGURE 014. AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY APPLICATION
FIGURE 015. SMALL AND MEDIUM ENTERPRISES (SMES) MARKET OVERVIEW (2016-2028)
FIGURE 016. LARGE ENTERPRISES MARKET OVERVIEW (2016-2028)
FIGURE 017. NORTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 018. EUROPE AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 019. ASIA PACIFIC AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 020. MIDDLE EAST & AFRICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
FIGURE 021. SOUTH AMERICA AUTOMATED DATA SCIENCE AND MACHINE LEARNING PLATFORMS MARKET OVERVIEW BY COUNTRY (2016-2028)
