Occupational safety training course and conference monthly statistical data.

Snapshot img

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

Data extracted from the user, event and group profile for delegates in the 9th R users conference in Spain

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

This article introduces how to teach an interactive one-semester-long statistics and programming class. The setting can also be applied to shorter and longer classes as well as for beginner and advanced courses. I propose a project-based seminar that also inherits elements of an inverted classroom. Thanks to this character, the seminar supports the students' learning progress and can also create engaging virtual classes. To showcase how to apply a project-based seminar setting to teaching statistics and programming classes, I use an introductory class to data wrangling and management with the statistical software R. Students are guided through a typical data science workflow that requires data management, data wrangling, and ends with visualizing and presenting first research results during a mini-conference.

Date: Survey of 2018

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.
The major contributions have been materialized in the form of novel algorithms.
Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.
Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.
Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".

If you use the data, implementation, or any details of the paper, please cite!

BIBTEX:

_

@inproceedings{nedelkoski2020multi,
 title={Multi-source Distributed System Data for AI-Powered Analytics},
 author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej},
 booktitle={European Conference on Service-Oriented and Cloud Computing},
 pages={161--176},
 year={2020},
 organization={Springer}
}

_

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.

Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

Inspired by the increasing availability of large text corpora online, digital humanities scholars are adopting computational approaches to explore questions in the field of literature from new perspectives. In this paper, we examine detailed social networks of characters, extracted from several works of 19th century fiction by Jane Austen and Charles Dickens. This allows us to apply methodologies from social network analysis, such as community detection, to explore the structure of these networks. By evaluating the results in collaboration with literary scholars, we find that the structure of the character networks can reveal underlying structural aspects within a novel, particularly in relation to plot and characterisation. 8th International ACM Web Science Conference 2016, Hanover, Germany, 22-25 may 2016 Science Foundation Ireland Irish Research Council

This workshop was developed for the 2024 Open Repositories conference. The Open Science Framework (OSF) is a platform that supports the research process through data repository services, scientific study registration, and preprint publishing. This workshop will feature a hands-on tutorial on creating queries for the new OSF Search API and parsing results. With the new API users can extract useful information to glean valuable insights into research activities on OSF. Two types of OSF Search API queries will be taught and attendees will be able to form their own queries using the Search API parameters. Typical use cases will be reviewed and attendees will be able to practice queries using the live API during the session. By the end of this workshop data librarians, repository managers, research support staff and repository developers will be able to use the OSF Search API to gather data for reports or other applications.

Archived website of the XVIth International Conference of the Association for History and Computing, Amsterdam, the Netherlands, 14-17th September 2005.



The XVIth Conference of the international AHC aims to bring together specialists from three broad streams:


- Scholars, using computers in historical and related studies (history of art, archaeology, literary studies, etc.)

- Information and computing scientists, working in the domain of cultural heritage and the humanities

- Professionals, working in cultural heritage institutes (archives, libraries, museums) who use ICT to preserve and give access to their collections

The subject matter of the conference is primarily oriented at methodological issues, and not restricted to one particular domain within historical sciences and the humanities. Preferably, sessions will consist of a mix of these three interest groups and fields. There will be numerous cross links between the streams.



Topics for sessions and papers include:
- Data access, retrieval and presentation: Data bases in historical/humanities research;

- Data mining, data harvesting and data syndication;

- Digital data archives & longevity of digital heritage;

- Personalization and presentation of heritage information;

- Virtual libraries and virtual collaboratories in the humanities;

- Enriching data: Digital source editions; Knowledge enrichment and encoding methods;

- Metadata standards and semantic interoperability for access to cultural heritage;

- Images & multimedia: Image analysis and visual culture;

- Content based and other image retrieval methods;

- Digital photo/image/video collections;

- Digital museums;

- Geographical Information Systems: GIS Applications in the humanities and historical studies;

- GIS methods and techniques; GIS for access to heritage information;
- Qualitative & Quantitative data analysis: Advanced statistics in historical research;

- Models and simulations;

- Exploratory analysis and visualization techniques
- Digitization of heritage information: Large digitization projects of historical sources;

- Optical character and document recognition for historical materials;

- Handwriting recognition and script analysis tools

- Text analysis and retrieval: Applications of text analysis in the humanities;

- Methodological issues of text mining and text analysis;

- Digital text archives

- Theoretical, methodological and educational issues: e-Science, e-Humanities and e-History;

- Historiography of humanities computing;

- Educational issues



Low Countries Organization Committee:

- Onno Boonstra (Humanities computing, University of Nijmegen)

- Leen Breure (Computer and Information Science, University of Utrecht)

- Peter Doorn (NIWI - Netherlands Institute for Scientific Information Services, Amsterdam)
- Jaap van den Herik (Computer Science, Universities of Leiden and Limburg)
- Bart de Nil (Amsab - Institute for Social History, Gent, Belgium)

- Paula Witkamp (European Commission on Preservation and Access, Amsterdam)



Organizing institutions:

- Netherlands Institute for Scientific Information Services (NIWI)

- Royal Netherlands Academy of Arts and Sciences (KNAW)

- Vereniging voor Geschiedenis en Informatica (VGI)

- The Association for History and Computing (AHC)

- Dutch Research School for Information and Knowledge Systems (SIKS)

The content of the website has been saved in three PDF packages with information over the conference and the collections of abstracts and posters.

Data sets compiled for a review paper on Gender Equity in Oceanography, to appear in Annual Review of Marine Science, 2023. Includes: Annual Reviews of Marine Science invited author gender statistics; China and USA oceanography career progression gender statistics; Current employment fractions of 2010-2019 physical oceanography PhDs; Gordon Research Conference Oceanography gender statistics; JGR oceans author and reviewer gender statistics; Oceanography faculty gender statistics for China; Oceanography graduate degree gender statistics for China; Oceanography magazine author gender data.

American College Football network of Girvan and Newman Mark Newman provides a football.gml file which contains the network of American football games between Division IA colleges during regular season Fall 2000. The file asks you to cite M. Girvan and M. E. J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002). There are are two issues with the original GN file. First three teams met twice in one season so the graph is not simple. This is easily dealt with if required. Secondly, the assignments to conferences, the node values, seem to be for the 2001 season and not the 2000 season. The games do appear to be for the 2000 season as stated. For instance the Big West conference existed for football till 2000 while the Sun Belt conference was only started in 2001. Also there were 11 conferences and 5 independents in 2001 but 10 conferences and 8 independents in 2000. I have provided a set of files footballTSE* which define a simple graph with the correct conference assignments in the archive here. There is a read me file included with more details. Further information about the problems with this data and the solutions are given in T.S. Evans, “Clique Graphs and Overlapping Communities”, J. Stat. Mech. (2010) P12037 [arXiv:1009.0638] which would be the appropriate source to cite along with the original GN publication.Note that Gschwind et al, 2015, Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques, independently finds similar errors in this data.

This dataset contains the data underlying the statistics reported in the paper 'Crossref as a source of open bibliographic metadata' by Nees Jan van Eck and Ludo Waltman. The data provides insight into the availability of different metadata elements (i.e., references, abstracts, ORCIDs, author affiliations, funding information, and license information) for journal articles, book chapters, conference papers, and preprints in Crossref. The data is based on the Crossref XML Metadata Plus Snapshot from January 2024. The file 'Crossref_metadata_statistics.ods' contains for each combination of a publisher, a publication type, and a publication year the total number of records and the number of records for which specific metadata elements are available. The file 'Crossref_metadata_figures.ods' contains for each figure in the paper the statistics presented in the figure.

There is only little statistical information on families in Austria. The Mikrozensus standard survey similar to the population census has the sample unit household; relationships are only visible in connection with the main breadwinner of the household. A family statistic that records family relation across the boarders of the household has yet not been conducted in Austria. Recently, the topic family has become important (appointment of a state secretary for family issues, beginning of the reform of the family law). On a European level, the family ministers hold regular conferences to support families and for collaborative measures. The lack of statistical data has thereby become more and more palpable. Therefore, the topic of this Mikrozensus special survey is questions on family-related topics.