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

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with 65 percent of the companies reporting the technology will create jobs and 7 percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with 23 percent of organizations expecting the technology to displace jobs and 49 percent expecting AI to create jobs.

Big Data and Society FAQ - 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

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. 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. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

Hadoop Big Data Analytics Market size was valued at USD 61.6 Billion in 2024 and is projected to reach USD 968.89 Billion by 2031, growing at a CAGR of 45.36% during the forecast period 2024-2031.

Global Hadoop Big Data Analytics Market Drivers

Explosive Growth of Data: One of the main factors propelling the Hadoop big data analytics market is the exponential growth of data collected across multiple sectors, such as social media, IoT devices, and enterprise applications. Large datasets may be stored, processed, and analysed with Hadoop, which is a scalable and affordable option for enterprises looking to extract value from this enormous amount of data.

Cost-Effectiveness: Businesses looking to analyse massive volumes of data may find traditional data warehousing solutions unaffordable due to their high prices. An affordable substitute is provided by the open-source Hadoop framework, which uses distributed computing and commodity hardware to drastically lower infrastructure costs.

Flexibility and Scalability: Hadoop's distributed computing architecture facilitates smooth scalability, enabling businesses to grow their data infrastructure in response to changing needs. Its adaptability to manage a range of data kinds, such as unstructured, semi-structured, and structured data, further makes it a desirable option for businesses interacting with a variety of data sources.

Advanced Analytics Capabilities: Machine learning, real-time processing, and predictive analytics are just a few of the advanced analytics jobs that organisations can carry out thanks to the abundance of tools and frameworks included in Hadoop's ecosystem, including Apache Spark, Hive, and HBase. With the use of these skills, businesses may extract useful insights from their data, resulting in better decision-making and a competitive advantage.

Growing Need for Real-Time Insights: Being able to glean real-time insights from data is critical in the fast-paced business world of today. When used in conjunction with Apache Kafka and Spark Streaming, Hadoop enables real-time data processing and analytics, allowing businesses to react quickly to shifting consumer preferences and market conditions.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for big data analytics and information technology in the U.S.

Big Data Analytics in Banking Market size was valued at USD 5.67 Billion in 2024 and is projected to reach USD 12.89 Billion by 2031, growing at a CAGR of 10.8% during the forecast period 2024-2031.

Global Big Data Analytics In Banking Market Drivers

Big data analytics assists banks in understanding consumer habits, preferences, and needs by analyzing enormous amounts of data from a variety of sources, including transaction records, social media, mobile engagements, and web visits. This allows banks to modify their products and services, providing personalized banking experiences that greatly increase consumer happiness and loyalty, hence driving market development.

Banks operate in a highly regulated environment, where risk management and compliance are critical. Big data analytics provides instruments for effective risk monitoring, analysis, and management. It aids in the detection of fraudulent activity by spotting anomalous trends, analyzing credit risks, and guaranteeing regulatory compliance through continuous monitoring of the transactions that banks handle daily, thus accelerating market growth.

Furthermore, big data analytics help banks become more efficient and cost-effective. Banks can uncover inefficiencies and areas for improvement by examining data from their processes and client interactions. This results in enhanced resource management, lower costs due to regular work automation, and better decision-making processes, all of which help to drive market expansion.

This statistic displays the results of a survey conducted on Norwegian population representatives in 2018 and their views on the impact of big data analysis and artificial intelligence on their job place in the future. The majority of respondents (37 percent) thought there would be no significant change. Four percent of respondents thought their job would get excessive.

According to Cognitive Market Research, the global SME Big Data market size is USD xx million in 2024. It will expand at a compound annual growth rate (CAGR) of 4.60% from 2024 to 2031. North America held the major market share for more than 40% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.8% from 2024 to 2031. Europe accounted for a market share of over 30% of the global revenue with a market size of USD xx million. Asia Pacific held a market share of around 23% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.6% from 2024 to 2031. Latin America had a market share for more than 5% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.0% from 2024 to 2031. Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.3% from 2024 to 2031. The Software held the highest SME Big Data market revenue share in 2024. Market Dynamics of SME Big Data Market Key Drivers for SME Big Data Market Growing Recognition of Data-Driven Decision Making The growing recognition of data-driven decision making is a key driver in the SME Big Data market as businesses increasingly understand the value of leveraging data for strategic decisions. This shift enables SMEs to optimize operations, enhance customer experiences, and gain competitive advantages. Access to affordable big data technologies and analytics tools has democratized data usage, making it feasible for smaller enterprises to adopt these solutions. SMEs can now analyze market trends, customer behaviors, and operational inefficiencies, leading to more informed and agile business strategies. This recognition propels demand for big data solutions, as SMEs seek to harness data insights to improve outcomes, innovate, and stay competitive in a rapidly evolving business landscape. Growing Number of Affordable Big Data Solutions The growing number of affordable big data solutions is driving the SME Big Data market by lowering the entry barrier for smaller enterprises to adopt advanced analytics. Cost-effective technologies, particularly cloud-based services, allow SMEs to access powerful data analytics tools without substantial upfront investments in infrastructure. This affordability enables SMEs to harness big data to gain insights into customer behavior, streamline operations, and enhance decision-making processes. As a result, more SMEs are integrating big data into their business models, leading to improved efficiency, innovation, and competitiveness. The availability of scalable and flexible solutions tailored to SME needs further accelerates adoption, making big data analytics an accessible and valuable resource for small and medium-sized businesses aiming for growth and success. Restraint Factor for the SME Big Data Market High Initial Investment Cost to Limit the Sales High initial costs are a significant restraint on the SME Big Data market, as they can deter smaller businesses from adopting big data technologies. Implementing big data solutions often requires substantial investment in hardware, software, and skilled personnel, which can be prohibitively expensive for SMEs with limited budgets. These costs include purchasing or subscribing to analytics platforms, upgrading IT infrastructure, and hiring data scientists or analysts. The financial burden associated with these initial expenses can make SMEs hesitant to commit to big data projects, despite the potential long-term benefits. Consequently, high initial costs limit the accessibility of big data analytics for SMEs, slowing the market's overall growth and the widespread adoption of these transformative technologies among smaller enterprises. Impact of Covid-19 on the SME Big Data Market The COVID-19 pandemic significantly impacted the SME Big Data market, accelerating digital transformation as businesses sought to adapt to rapidly changing conditions. With disruptions in traditional operations and a shift towards remote work, SMEs increasingly turned to big data analytics to maintain efficiency, manage supply chains, and understand evolving customer behaviors. The pandemic underscored the importance of real-time data insights for agile decision-making, dr...

This statistic displays the adoption rates of Big Data analytics in the United Kingdom (UK) in 2015 and 2020. In 2015, the adoption rate amounted to 56 percent across all examined industry. 67 percent of the industries will adopt Big Data analytics in 2020.

Big data is expected to be a huge creator of jobs in the future. This source estimates that by 2015, there will be an additional 4.4 million IT jobs internationally in this field and that 1.9 million of these jobs will be based in the United States.

This dataset is about books. It has 1 row and is filtered where the book is Big data analytics : turning big data into big money. It features 7 columns including author, publication date, language, and book publisher.

This dataset contains metrics for a 10 minute job running on YARN. More specifically it contains metrics for:

YARN

HDFS

System Metrics (CPU, RAM, HDD, Interface etc.)

Since July 2016, the Big Data Interagency Working Group (BD IWG) has been involved in meetings to discuss the viability of and possible first steps to creating a public-private data network infrastructure, the Open Knowledge Network (OKN). On October 4–5, 2017, the Big Data IWG hosted a workshop to both expand the discussion to similar work being done in biomedicine, finance, geoscience, and manufacturing and to gather expert advice on next steps to advance the OKN. This report summarizes those discussions.

This dataset is about books. It has 3 rows and is filtered where the book is Big data demystified : how to use big data, data science and AI to make better business decisions and gain competitive advantage. It features 7 columns including author, publication date, language, and book publisher.

Big Data and Society Acceptance Rate - 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

This dataset is about book subjects. It has 5 rows and is filtered where the books is Big data made easy : a working guide to the complete Hadoop toolset. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Big Data and Society CiteScore 2024-2025 - 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

This statistic shows the growth rate of the global big data market from 2012 to 2027. The big data market is forecast to grow by 20 percent in 2018, reaching 42 billion U.S. dollars in size.

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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