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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

This page provides information about award recipients and summaries of competitions under the Training Program for Federal TRIO Programs.

Most machine learning, data science, and artificial intelligence (AI) developers work with unstructured text data of the size between ** MB and * GB, with a combined ** percent of respondents indicating as such. Twelve percent of respondents work with unstructured video data with a size larger than * TB.

The dataset provides training information extracted from Home Care Registry (HCR) application. HCR is a web-based registry of all personal care and home health aides who have successfully completed a personal care or home health aide training program approved by either the New York State Department of Health (NYSDOH) or the New York State Education Department (NYSED). This registry is the central repository of the individuals who have successfully completed State-approved education or training programs for Home Health Aides and Personal Care Aides. The Training Programs are the sources for most of the training information available in HCR. This dataset is refreshed on monthly basis.

employee training program

International projects targeting training in some disciplines Data and Resources المشاريع الدولية في مجال البرامج التدريبيةPDF المشاريع الدولية في مجال البرامج التدريبية Explore More information Download

Increase the number of TANF (Temporary Assistance for Needy Families) clients who complete career and technology programs or special projects in two-year colleges from 555 in 2014 to 700 by 2018.

Because of the COVID-19 pandemic, presentation of public health data to the public has increased without much of the public having the knowledge to understand what these statistics mean or why some populations are at higher risk of adverse outcomes. Recognizing that those most impacted by COVID-19 are from vulnerable populations, we developed a training program called "The quantitative public health data literacy training program", aimed at increasing the data literacy of towards high school and college students from such vulnerable groups that introduces the basics of public health, data literacy, statistical software, descriptive statistics, and data ethics. The instructors taught eight synchronous sessions (five were also offered asynchronously), consisting of lectures and experiential group exercises. The program recruited, engaged, and retained a large cohort (n > 100) of underrepresented students in biostatistics and data science for a virtual data literacy training. The course provides a framework for developing and implementing similar public health training programs designed to increase diversity in the field.This project provides de-identified data for program's baseline/final assessment , program feedback as well as grades for certain portion of the program. The "Data-files" folder contains all the data collected during program. Along with the deidentified data, code is also provided (in R language) to analyze the data as presented in tables in potential publications.

Machine Learning Software Market Outlook

The global machine learning software market is anticipated to experience a robust growth trajectory, with the market size projected to expand from USD 15 billion in 2023 to an estimated USD 85 billion by 2032, at a Compound Annual Growth Rate (CAGR) of 20.5%. This remarkable growth is driven by a confluence of technological advancements, increased adoption of AI across various sectors, and a surge in demand for intelligent business solutions that automate processes and enhance decision-making efficiency. The ability of machine learning software to process large volumes of data and generate actionable insights is transforming industries, making it a crucial element in the digital transformation strategies of organizations worldwide.

One of the primary growth factors contributing to this market expansion is the exponential increase in data generation from various sources, including IoT devices, social media, and enterprise applications. The need to derive strategic insights from this massive data pool is pushing organizations to adopt machine learning solutions. Furthermore, the evolution of big data technologies and cloud computing platforms has made it feasible for businesses of all sizes to implement sophisticated machine learning models without incurring prohibitive costs. This democratization of machine learning technologies is particularly beneficial for small and medium enterprises (SMEs), enabling them to leverage data analytics to drive business growth.

Another significant driver for the machine learning software market is the growing emphasis on customer-centric business strategies. Industries such as retail, BFSI, and healthcare are increasingly adopting machine learning algorithms to gain a deeper understanding of customer behavior, tailor personalized experiences, and improve customer satisfaction. For example, predictive analytics and natural language processing (NLP) technologies are being used to anticipate customer needs, optimize pricing strategies, and enhance customer service. Additionally, the integration of machine learning with automation processes is enabling industries to streamline operations, reduce operational costs, and enhance productivity, thereby further fueling market growth.

The growing focus on innovation and technological advancements in artificial intelligence is also a potent growth catalyst. Governments and private sectors across the globe are investing heavily in AI research and development to gain technological superiority, fostering an environment conducive to the proliferation of machine learning applications. The rise of edge computing and 5G technology further amplifies this growth, as it enables faster data processing and real-time analytics, crucial for applications such as autonomous driving and IoT. Consequently, the synergy between machine learning and these emerging technologies is anticipated to unlock new avenues for market expansion over the forecast period.

The advent of Deep Learning System Software is revolutionizing the capabilities of machine learning applications. These systems are designed to mimic the human brain's neural networks, allowing for more complex data processing and pattern recognition. As a result, industries are able to tackle more sophisticated challenges, such as image and speech recognition, with unprecedented accuracy. This advancement is particularly transformative in sectors like healthcare, where deep learning is being used to analyze medical images and predict patient outcomes. The integration of deep learning systems with existing machine learning frameworks is enhancing the overall efficiency and effectiveness of AI-driven solutions, paving the way for groundbreaking innovations.

Regionally, North America is anticipated to dominate the machine learning software market due to the presence of leading technology firms and a highly mature digital ecosystem. The region's focus on innovation, coupled with substantial investments in R&D, has positioned it as a leader in AI and machine learning technologies. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate, driven by rapid digitalization, growing internet penetration, and significant government initiatives promoting AI adoption. Countries such as China and India are emerging as key markets, leveraging their large IT talent pool and increasing industrial automation to boost machine learning software deployment.

Component Analysis

This file contains a dataset of untangled code changes, created with the help of two developers who accurately split their code changes into self contained tasks over a period of four months. The developers worked in Pharo 1, and their code changes were recorded using Epicea 2. The format of the files is STON 3, which is very similar to JSON. Contact: tinchodias@gmail.com

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

IntroductionIn recent years, numerous AI tools have been employed to equip learners with diverse technical skills such as coding, data analysis, and other competencies related to computational sciences. However, the desired outcomes have not been consistently achieved. This study aims to analyze the perspectives of students and professionals from non-computational fields on the use of generative AI tools, augmented with visualization support, to tackle data analytics projects. The focus is on promoting the development of coding skills and fostering a deep understanding of the solutions generated. Consequently, our research seeks to introduce innovative approaches for incorporating visualization and generative AI tools into educational practices.MethodsThis article examines how learners perform and their perspectives when using traditional tools vs. LLM-based tools to acquire data analytics skills. To explore this, we conducted a case study with a cohort of 59 participants among students and professionals without computational thinking skills. These participants developed a data analytics project in the context of a Data Analytics short session. Our case study focused on examining the participants' performance using traditional programming tools, ChatGPT, and LIDA with GPT as an advanced generative AI tool.ResultsThe results shown the transformative potential of approaches based on integrating advanced generative AI tools like GPT with specialized frameworks such as LIDA. The higher levels of participant preference indicate the superiority of these approaches over traditional development methods. Additionally, our findings suggest that the learning curves for the different approaches vary significantly. Since learners encountered technical difficulties in developing the project and interpreting the results. Our findings suggest that the integration of LIDA with GPT can significantly enhance the learning of advanced skills, especially those related to data analytics. We aim to establish this study as a foundation for the methodical adoption of generative AI tools in educational settings, paving the way for more effective and comprehensive training in these critical areas.DiscussionIt is important to highlight that when using general-purpose generative AI tools such as ChatGPT, users must be aware of the data analytics process and take responsibility for filtering out potential errors or incompleteness in the requirements of a data analytics project. These deficiencies can be mitigated by using more advanced tools specialized in supporting data analytics tasks, such as LIDA with GPT. However, users still need advanced programming knowledge to properly configure this connection via API. There is a significant opportunity for generative AI tools to improve their performance, providing accurate, complete, and convincing results for data analytics projects, thereby increasing user confidence in adopting these technologies. We hope this work underscores the opportunities and needs for integrating advanced LLMs into educational practices, particularly in developing computational thinking skills.

Analysis of ‘TANF Training Programs’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/26c42ab4-82dc-44f7-ab57-b5167615f08f on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Increase the number of TANF (Temporary Assistance for Needy Families) clients who complete career and technology programs or special projects in two-year colleges from 555 in 2014 to 700 by 2018.

--- Original source retains full ownership of the source dataset ---

Get aggregate data on Working at Heights training programs approved by the Chief Prevention Officer.

Data includes:

• program type (full or refresher)
• total approved programs
• number of unique training providers
• successful completions

Additional information about training for working at heights can be located here.

*[O.Reg]: Ontario Regulation *[WAH]: Working at Heights

List of training courses provided under the “Smart Silver” digital inclusion programmes - the DPO launched “Smart Silver” digital inclusion programmes to help those in need (especially the elderly) to understand and use digital technology products and services.

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data.

We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset.

We provide links to the PTM Dataset and PTM Torrent Source Code.

Comprehensive dataset of 7 Software training institutes in Iowa, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The H-1B skills training grant programs are competitive grants that focus on specific interventions, populations, partnerships, or structures in job training. The H-1B grants Quarterly Performance Report (QPR) dataset contains aggregate data on the number of participants, services provided, and performance outcomes. There is no individual data in the dataset, only summary level quarterly data by grantee.

The National Science Foundation, the Department of Labor and the Department of Energy have programs that support training for jobs in energy and manufacturing related workforce training programs. This dataset provides a searchable list of the training programs in these areas showing the subjects being taught, grantee, project title, and state. In some cases the list also shows the certificates provided by the courses. The list is still a work in progress and will be updated as more information is obtained. It may contain incomplete information, unintentional omissions and errors in topic identification and taxonomy. Please contact us with suggestions or corrections.