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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million, at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This fusion enables organizations to derive deeper insights from their data, fueling business innovation and decision-making. Another trend shaping the market is the emergence of containerization and microservices in data science platforms. This approach offers enhanced flexibility, scalability, and efficiency, making it an attractive choice for businesses seeking to streamline their data science operations. However, the market also faces challenges. Data privacy and security remain critical concerns, with the increasing volume and complexity of data posing significant risks. Ensuring robust data security and privacy measures is essential for companies to maintain customer trust and comply with regulatory requirements. Additionally, managing the complexity of data science platforms and ensuring seamless integration with existing systems can be a daunting task, requiring significant investment in resources and expertise. Companies must navigate these challenges effectively to capitalize on the market's opportunities and stay competitive in the rapidly evolving data landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for advanced analytics and artificial intelligence solutions across various sectors. Real-time analytics and classification models are at the forefront of this evolution, with APIs integrations enabling seamless implementation. Deep learning and model deployment are crucial components, powering applications such as fraud detection and customer segmentation. Data science platforms provide essential tools for data cleaning and data transformation, ensuring data integrity for big data analytics. Feature engineering and data visualization facilitate model training and evaluation, while data security and data governance ensure data privacy and compliance. Machine learning algorithms, including regression models and clustering models, are integral to predictive modeling and anomaly detection. Statistical analysis and time series analysis provide valuable insights, while ETL processes streamline data integration. Cloud computing enables scalability and cost savings, while risk management and algorithm selection optimize model performance. Natural language processing and sentiment analysis offer new opportunities for data storytelling and computer vision. Supply chain optimization and recommendation engines are among the latest applications of data science platforms, demonstrating their versatility and continuous value proposition. Data mining and data warehousing provide the foundation for these advanced analytics capabilities.

How is this Data Science Platform Industry segmented?

The data science platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudComponentPlatformServicesEnd-userBFSIRetail and e-commerceManufacturingMedia and entertainmentOthersSectorLarge enterprisesSMEsApplicationData PreparationData VisualizationMachine LearningPredictive AnalyticsData GovernanceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, businesses increasingly adopt solutions to gain real-time insights from their data, enabling them to make informed decisions. Classification models and deep learning algorithms are integral parts of these platforms, providing capabilities for fraud detection, customer segmentation, and predictive modeling. API integrations facilitate seamless data exchange between systems, while data security measures ensure the protection of valuable business information. Big data analytics and feature engineering are essential for deriving meaningful insights from vast datasets. Data transformation, data mining, and statistical analysis are crucial processes in data preparation and discovery. Machine learning models, including regression and clustering, are employed for model training and evaluation. Time series analysis and natural language processing are valuable tools for understanding trends and customer sen

Data Science Platform Market Outlook

The global data science platform market size was valued at approximately USD 49.3 billion in 2023 and is projected to reach USD 174.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.1% during the forecast period. This exponential growth can be attributed to the increasing demand for data-driven decision-making processes, the surge in big data technologies, and the need for more advanced analytics solutions across various industries.

One of the primary growth factors driving the data science platform market is the rapid digital transformation efforts undertaken by organizations globally. Companies are shifting towards data-centric business models to gain a competitive edge, improve operational efficiency, and enhance customer experiences. The proliferation of IoT devices and the subsequent explosion of data generated have further propelled the need for sophisticated data science platforms capable of analyzing vast datasets in real-time. This transformation is not only seen in large enterprises but also increasingly in small and medium enterprises (SMEs) that recognize the potential of data analytics in driving business growth.

Moreover, the advancements in artificial intelligence (AI) and machine learning (ML) technologies have significantly augmented the capabilities of data science platforms. These technologies enable the automation of complex data analysis processes, allowing for more accurate predictions and insights. As a result, sectors such as healthcare, finance, and retail are increasingly adopting data science solutions to leverage AI and ML for personalized services, fraud detection, and supply chain optimization. The integration of AI/ML into data science platforms is thus a critical factor contributing to market growth.

Another crucial factor is the growing regulatory and compliance requirements across various industries. Organizations are mandated to ensure data accuracy, security, and privacy, necessitating the adoption of robust data science platforms that can handle these aspects efficiently. The implementation of regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States has compelled organizations to invest in advanced data management and analytics solutions. These regulatory frameworks are not only a challenge but also an opportunity for the data science platform market to innovate and provide compliant solutions.

Regionally, North America dominates the data science platform market due to the early adoption of advanced technologies, a strong presence of key market players, and significant investments in research and development. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the increasing digitalization initiatives, a growing number of tech startups, and the rising demand for analytics solutions in countries like China, India, and Japan. The competitive landscape and economic development in these regions are creating ample opportunities for market expansion.

Component Analysis

The data science platform market, segmented by components, includes platforms and services. The platform segment encompasses software and tools designed for data integration, preparation, and analysis, while the services segment covers professional and managed services that support the implementation and maintenance of these platforms. The platform component is crucial as it provides the backbone for data science operations, enabling data scientists to perform data wrangling, model building, and deployment efficiently. The increasing demand for customized solutions tailored to specific business needs is driving the growth of the platform segment. Additionally, with the rise of open-source platforms, organizations have more flexibility and control over their data science workflows, further propelling this segment.

On the other hand, the services segment is equally vital as it ensures that organizations can effectively deploy and utilize data science platforms. Professional services include consulting, training, and support, which help organizations in the seamless integration of data science solutions into their existing IT infrastructure. Managed services provide ongoing support and maintenance, ensuring data science platforms operate optimally. The rising complexity of data ecosystems and the shortage of skilled data scientists are factors contributing to the growth of the services segment, as organizations often rely on external expert

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

The global big data analytics market size was valued at $307.52 billion in 2023 & is projected to grow from $348.21 billion in 2024 to $961.89 billion by 2032

The data science platform market size is projected to grow from USD 138 billion in 2024 to USD 1,678 trillion by 2035, representing a high CAGR of 25.47%.

This study was conducted to identify correlations between Altmetric data and its set of scientific topics. The scholarly articles published online were organized by distribution source, totaling the popularity of each article and its topic. In this case, topic refers to any scientific field of study and the data sets used were retrieved from Altmetric and Twitter sources. The purpose of this study is to help gain insight into where the future of science lies in subject matter as well as professional and public practice. as well as distinguish between what research the public favours to focus on in relation to privatized interests. Publishing dates, Scopus subjects, and Altmetric scores were extracted from the Altmetric data. This data was compared with Twitter posters, helping differentiate the professional and public demographics that were interested in this field. Through the results, it was identified that the data showed that different cohorts – types of demographics – have different interests. Therefore, the future of science cannot be limited to single fields, rather an amalgamation of those popular among scholarly and public interests.

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Big Data Market Size 2025-2029

The big data market size is forecast to increase by USD 193.2 billion at a CAGR of 13.3% between 2024 and 2029.

The market is experiencing a significant rise due to the increasing volume of data being generated across industries. This data deluge is driving the need for advanced analytics and processing capabilities to gain valuable insights and make informed business decisions. A notable trend in this market is the rising adoption of blockchain solutions to enhance big data implementation. Blockchain's decentralized and secure nature offers an effective solution to address data security concerns, a growing challenge in the market. However, the increasing adoption of big data also brings forth new challenges. Data security issues persist as organizations grapple with protecting sensitive information from cyber threats and data breaches.
Companies must navigate these challenges by investing in robust security measures and implementing best practices to mitigate risks and maintain trust with their customers. To capitalize on the market opportunities and stay competitive, businesses must focus on harnessing the power of big data while addressing these challenges effectively. Deep learning frameworks and machine learning algorithms are transforming data science, from data literacy assessments to computer vision models.

What will be the Size of the Big Data Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In today's data-driven business landscape, the demand for advanced data management solutions continues to grow. Companies are investing in business intelligence dashboards and data analytics tools to gain insights from their data and make informed decisions. However, with this increased reliance on data comes the need for robust data governance policies and regular data compliance audits. Data visualization software enables businesses to effectively communicate complex data insights, while data engineering ensures data is accessible and processed in real-time. Data-driven product development and data architecture are essential for creating agile and responsive business strategies. Data management encompasses data accessibility standards, data privacy policies, and data quality metrics.
Data usability guidelines, prescriptive modeling, and predictive modeling are critical for deriving actionable insights from data. Data integrity checks and data agility assessments are crucial components of a data-driven business strategy. As data becomes an increasingly valuable asset, businesses must prioritize data security and privacy. Prescriptive and predictive modeling, data-driven marketing, and data culture surveys are key trends shaping the future of data-driven businesses. Data engineering, data management, and data accessibility standards are interconnected, with data privacy policies and data compliance audits ensuring regulatory compliance.
Data engineering and data architecture are crucial for ensuring data accessibility and enabling real-time data processing. The data market is dynamic and evolving, with businesses increasingly relying on data to drive growth and inform decision-making. Data engineering, data management, and data analytics tools are essential components of a data-driven business strategy, with trends such as data privacy, data security, and data storytelling shaping the future of data-driven businesses.

How is this Big Data Industry segmented?

The big data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


End-user

  BFSI
  Healthcare
  Retail and e-commerce
  IT and telecom
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

In the realm of big data, on-premise and cloud-based deployment models cater to varying business needs. On-premise deployment allows for complete control over hardware and software, making it an attractive option for some organizations. However, this model comes with a significant upfront investment and ongoing maintenance costs. In contrast, cloud-based deployment offers flexibility and scalability, with service providers handling infrastructure and maintenance. Yet, it introduces potential security risks, as data is accessed through multiple points and stored on external servers. Data

Big Data and Society Abstract & Indexing - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

Big Data Market Outlook

According to our latest research, the global big data market size reached USD 332.7 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to grow at a CAGR of 13.2% during the forecast period, reaching USD 862.5 billion by 2033. This remarkable growth is primarily driven by increasing data volumes, the proliferation of connected devices, and the rising demand for actionable insights to support strategic business decisions. The rapid evolution of digital transformation initiatives and the integration of artificial intelligence and machine learning into analytics platforms are further accelerating market momentum, as enterprises strive to harness the full potential of big data to gain a competitive edge.

One of the primary growth factors fueling the big data market is the exponential increase in data generation from various sources, including social media, IoT devices, enterprise applications, and digital transactions. Organizations are increasingly recognizing the value of leveraging this data to extract actionable insights, optimize operations, and personalize customer experiences. As the digital ecosystem expands, the need for advanced analytics tools capable of processing and analyzing vast, complex datasets has become paramount. The integration of big data analytics with cloud computing platforms further enhances scalability and accessibility, enabling even small and medium-sized enterprises (SMEs) to deploy sophisticated data-driven strategies without incurring significant infrastructure costs. This democratization of data analytics is significantly broadening the market’s addressable base.

Another significant driver is the surge in regulatory requirements and compliance mandates, particularly in sectors such as banking, healthcare, and government. These industries are compelled to implement robust data management and analytics frameworks to ensure data integrity, security, and regulatory compliance. Big data solutions offer advanced capabilities for real-time monitoring, risk assessment, and fraud detection, which are critical for organizations operating in highly regulated environments. Additionally, the growing emphasis on customer-centric strategies is prompting businesses to invest in customer analytics, enabling them to anticipate market trends, improve customer satisfaction, and foster loyalty through personalized offerings. The convergence of big data with emerging technologies like artificial intelligence, blockchain, and edge computing is opening new avenues for innovation and value creation.

Despite the positive outlook, the big data market faces challenges related to data privacy, security, and talent shortages. The increasing complexity of data ecosystems necessitates skilled professionals proficient in data science, analytics, and cybersecurity. Organizations are actively investing in workforce development and partnering with technology vendors to bridge these gaps. Furthermore, the shift towards hybrid and multi-cloud environments is driving demand for interoperable big data solutions that can seamlessly integrate disparate data sources while maintaining compliance with data sovereignty regulations. As businesses continue to navigate these complexities, the adoption of advanced big data platforms is expected to remain a critical enabler of digital transformation and business agility.

From a regional perspective, North America continues to dominate the big data market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of leading technology companies, advanced digital infrastructure, and a strong focus on innovation underpin North America’s leadership. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, government initiatives, and the proliferation of internet-enabled devices. Countries such as China, India, and Japan are investing heavily in big data analytics to enhance public services, healthcare delivery, and industrial productivity. Meanwhile, Europe’s emphasis on data protection and digital sovereignty is spurring demand for secure and compliant big data solutions. The Middle East & Africa and Latin America are also emerging as promising markets, supported by increasing investments in smart city projects and digital transformation initiatives.

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Online Data Science Training Programs Market Size 2025-2029

The online data science training programs market size is forecast to increase by USD 8.67 billion, at a CAGR of 35.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for data science professionals in various industries. The job market offers lucrative opportunities for individuals with data science skills, making online training programs an attractive option for those seeking to upskill or reskill. Another key driver in the market is the adoption of microlearning and gamification techniques in data science training. These approaches make learning more engaging and accessible, allowing individuals to acquire new skills at their own pace. Furthermore, the availability of open-source learning materials has democratized access to data science education, enabling a larger pool of learners to enter the field. However, the market also faces challenges, including the need for continuous updates to keep up with the rapidly evolving data science landscape and the lack of standardization in online training programs, which can make it difficult for employers to assess the quality of graduates. Companies seeking to capitalize on market opportunities should focus on offering up-to-date, high-quality training programs that incorporate microlearning and gamification techniques, while also addressing the challenges of continuous updates and standardization. By doing so, they can differentiate themselves in a competitive market and meet the evolving needs of learners and employers alike.

What will be the Size of the Online Data Science Training Programs Market during the forecast period?

Request Free SampleThe online data science training market continues to evolve, driven by the increasing demand for data-driven insights and innovations across various sectors. Data science applications, from computer vision and deep learning to natural language processing and predictive analytics, are revolutionizing industries and transforming business operations. Industry case studies showcase the impact of data science in action, with big data and machine learning driving advancements in healthcare, finance, and retail. Virtual labs enable learners to gain hands-on experience, while data scientist salaries remain competitive and attractive. Cloud computing and data science platforms facilitate interactive learning and collaborative research, fostering a vibrant data science community. Data privacy and security concerns are addressed through advanced data governance and ethical frameworks. Data science libraries, such as TensorFlow and Scikit-Learn, streamline the development process, while data storytelling tools help communicate complex insights effectively. Data mining and predictive analytics enable organizations to uncover hidden trends and patterns, driving innovation and growth. The future of data science is bright, with ongoing research and development in areas like data ethics, data governance, and artificial intelligence. Data science conferences and education programs provide opportunities for professionals to expand their knowledge and expertise, ensuring they remain at the forefront of this dynamic field.

How is this Online Data Science Training Programs Industry segmented?

The online data science training programs industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeProfessional degree coursesCertification coursesApplicationStudentsWorking professionalsLanguageR programmingPythonBig MLSASOthersMethodLive streamingRecordedProgram TypeBootcampsCertificatesDegree ProgramsGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Type Insights

The professional degree courses segment is estimated to witness significant growth during the forecast period.The market encompasses various segments catering to diverse learning needs. The professional degree course segment holds a significant position, offering comprehensive and in-depth training in data science. This segment's curriculum covers essential aspects such as statistical analysis, machine learning, data visualization, and data engineering. Delivered by industry professionals and academic experts, these courses ensure a high-quality education experience. Interactive learning environments, including live lectures, webinars, and group discussions, foster a collaborative and engaging experience. Data science applications, including deep learning, computer vision, and natural language processing, are integral to the market's growth. Data analysis, a crucial application, is gaining traction due to the increasing demand

The global data science platform market was valued at USD 95.31 billion in 2021 and is expected to grow at a CAGR of 27.6% during the forecast period.

Data Science Platform Market Outlook

According to our latest research, the global Data Science Platform market size reached $96.2 billion in 2024, reflecting robust demand across multiple industries. The market is expected to expand at a CAGR of 26.1% from 2025 to 2033, reaching a projected value of $764.6 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of artificial intelligence and machine learning technologies, the surge in big data analytics, and the need for advanced data-driven decision-making frameworks across enterprises worldwide.

The primary growth factor fueling the Data Science Platform market is the exponential rise in data generation and the subsequent demand for actionable insights. Organizations are increasingly leveraging data science platforms to harness structured and unstructured data, enabling them to gain a competitive edge through predictive analytics and real-time decision-making. The proliferation of IoT devices, digital transformation initiatives, and cloud-based infrastructure has further accelerated the deployment of data science solutions. Moreover, the growing complexity of business operations and the need for personalized customer experiences are compelling enterprises to invest heavily in advanced analytics platforms, contributing significantly to the market's expansion.

Another critical driver is the rapid advancement in machine learning algorithms and automation capabilities integrated into data science platforms. These platforms now offer end-to-end solutions, from data ingestion and preparation to model deployment and monitoring, reducing the time and expertise required to derive value from data. The democratization of data science tools, coupled with the emergence of no-code and low-code platforms, is empowering a broader range of professionals to participate in analytics workflows. This democratization is particularly beneficial for small and medium enterprises (SMEs), enabling them to leverage sophisticated analytics without the need for extensive in-house expertise, thereby broadening the addressable market.

Additionally, the increasing emphasis on regulatory compliance, data privacy, and security is shaping the evolution of the Data Science Platform market. Enterprises across sectors such as BFSI, healthcare, and government are prioritizing platforms that offer robust data governance, auditability, and transparency. The integration of explainable AI and ethical AI frameworks within data science platforms is becoming a key differentiator, especially as organizations navigate complex regulatory landscapes. This trend is not only fostering innovation but also building trust among stakeholders, further driving market growth.

From a regional perspective, North America continues to dominate the Data Science Platform market, accounting for over 38% of the global revenue in 2024. The region's leadership can be attributed to its mature technology ecosystem, strong presence of leading market players, and high adoption rates of advanced analytics across industries. Asia Pacific is emerging as the fastest-growing region, with a projected CAGR of 29.4% through 2033, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in AI and big data analytics in countries such as China, India, and Japan. Europe and Latin America are also witnessing steady growth, supported by digital transformation initiatives and the growing focus on data-driven business strategies.

Component Analysis

The Data Science Platform market by component is segmented into platform and services. The platform segment dominates the market, accounting for the largest share due to the rising demand for integrated solutions that streamline the entire analytics workflow. These platforms provide comprehensive tools for data preparation, model development, deployment, and monitoring, enabling organizations to accelerate their analytics initiatives. The increasing adoption of cloud-based platforms, which offer scalability, flexi

The Data Science Platform market is experiencing robust growth, projected to reach $10.15 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 23.50% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data generated across diverse industries necessitates sophisticated platforms for analysis and insights extraction. Businesses are increasingly adopting cloud-based solutions for their scalability, cost-effectiveness, and accessibility, driving the growth of the cloud deployment segment. Furthermore, the rising demand for advanced analytics capabilities across sectors like BFSI (Banking, Financial Services, and Insurance), retail and e-commerce, and IT & Telecom is significantly boosting market demand. The availability of robust and user-friendly platforms is empowering businesses of all sizes, from SMEs to large enterprises, to leverage data science effectively for improved decision-making and competitive advantage. The market is witnessing the emergence of innovative solutions such as automated machine learning (AutoML) and integrated platforms that combine data preparation, model building, and deployment capabilities. The market segmentation reveals significant opportunities across various offerings and deployment models. While the platform segment holds a larger share, the services segment is poised for significant growth driven by the need for expert consulting and support in data science projects. Geographically, North America currently dominates the market, but the Asia-Pacific region is expected to witness faster growth due to increasing digitalization and technological advancements. Key players like IBM, Google, Microsoft, and Amazon are driving innovation and competition, with new entrants continuously emerging, adding to the market's dynamism. While challenges such as data security and privacy concerns remain, the overall market outlook is exceptionally positive, promising considerable growth over the forecast period. Continued technological innovation, coupled with rising adoption across a wider array of industries, will be central to the market's continued expansion. Recent developments include: November 2023 - Stagwell announced a partnership with Google Cloud and SADA, a Google Cloud premier partner, to develop generative AI (gen AI) marketing solutions that support Stagwell agencies, client partners, and product development within the Stagwell Marketing Cloud (SMC). The partnership will help in harnessing data analytics and insights by developing and training a proprietary Stagwell large language model (LLM) purpose-built for Stagwell clients, productizing data assets via APIs to create new digital experiences for brands, and multiplying the value of their first-party data ecosystems to drive new revenue streams using Vertex AI and open source-based models., May 2023 - IBM launched a new AI and data platform, watsonx, it is aimed at allowing businesses to accelerate advanced AI usage with trusted data, speed and governance. IBM also introduced GPU-as-a-service, which is designed to support AI intensive workloads, with an AI dashboard to measure, track and help report on cloud carbon emissions. With watsonx, IBM offers an AI development studio with access to IBMcurated and trained foundation models and open-source models, access to a data store to gather and clean up training and tune data,. Key drivers for this market are: Rapid Increase in Big Data, Emerging Promising Use Cases of Data Science and Machine Learning; Shift of Organizations Toward Data-intensive Approach and Decisions. Potential restraints include: Rapid Increase in Big Data, Emerging Promising Use Cases of Data Science and Machine Learning; Shift of Organizations Toward Data-intensive Approach and Decisions. Notable trends are: Small and Medium Enterprises to Witness Major Growth.

MATLAB led the global advanced analytics and data science software industry in 2025 with a market share of ***** percent. First launched in 1984, MATLAB is developed by the U.S. firm MathWorks.

International Journal of Data Science and Analytics Impact Factor 2024-2025 - ResearchHelpDesk - International Journal of Data Science and Analytics - Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations.

The data science and predictive analytics market size was over USD 19.07 billion in 2024 and is projected to reach USD 179.05 billion by 2037, witnessing around 18.8% CAGR during the forecast period i.e., between 2025-2037. North America industry is estimated to dominate majority revenue share of 35% by 2037, on account of high rate of adoption of cutting-edge technology in the region.

Data Science Platform Market is Segmented by Offering (Platform, Services), Deployment (On-Premise, Cloud), Enterprise Size (Small and Medium Enterprises, Large Enterprises), End-User Industry (IT and Telecom, BFSI, Retail and E-Commerce, Manufacturing, and More), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

Psychology still relies often on questionnaires to gather data. Despite ubiquitous computing and internet, these are often still conducted with paper and clipboard. Where the internet is used, standalone questionnaires from bulk providers like Survey Monkey are the norm. For our studies related to social networks on online disclosure, we have developed a custom site for online questionnaires, designed to engage participants and allow linking of data from one study to the next – PsyQu.com PsyQu is a modern website developed around a database and as such has a ‘schema’. This data structure encapsulates the project, researcher(s) and participant(s) in a manner that allows for participants to link multiple attempts at multiple studies under their single account. This will allow cross-linked and longitudinal studies to be performed. By moving beyond standalone questionnaires, we hope to discover new correlative and predictive patterns between online behavior and other psychological dimensions. At present, the site is in alpha testing mode with only 1 group of researchers and 3 studies: social capital, online self-disclosure and personality. In the social capital study, we used a standard scale for investigating online social capital and social trust, in an attempt to find out differences between various groups. A paper survey was also conducted in order to compare with the online survey since there has been debate on the reliability of online participation. We will present the website, initial results of the social capital study.

Global Data Science Platform market size is expected to reach $485.95 billion by 2029 at 33.1%, growing demand for big data analytics fuels data science platform market growth

[Coursera] Introduction to Data Science (University of Washington) (datasci)