In 2023, the most common advice offered by alumni of online higher education programs in the United States, suggested by 30 percent of alumni, was to do more research about cost and financial aid. A further 22 percent of alumni of online programs said to compare more programs when researching schools.

As of June 2024, around ******* students were enrolled at an online bachelor's program in Italy. In addition, ****** individuals chose e-learning for their master's studies. Among the largest Italian universities, the Pegaso online University ranks at the second place, nationwide. In the academic year 2023/2024, the e-learning institute had more than ****** enrolled students.

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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

Online and Distance Education at Postsecondary Institutions, 2006-07 (PEQIS 16), is a study that is part of the Postsecondary Education Quick Information System (PEQIS) program; program data is available since 1997 at . PEQIS 16 (https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2009044) is a survey that collects data on the prevalence and delivery of distance education courses in the 2006-07 12-month academic year, including the number of courses and enrollment for online courses, hybrid/blended online courses, and all other distance education courses. The survey also collects information about numbers of degree or certificate programs designed to be completed entirely through distance education and the technologies used for the instructional delivery of credit-granting distance education courses. The study was conducted using paper and web surveys. The weighted response rate was 87 percent. Postsecondary institutions were sample for this study. Key statistics produced from PEQIS 16 relate to information on the prevalence, types, delivery, policies, and acquisition or development of distance education courses and programs.

The online higher education market is experiencing robust growth, fueled by increasing accessibility, affordability demands, and technological advancements. The market's Compound Annual Growth Rate (CAGR) of 19.82% from 2019 to 2024 suggests a significant expansion, likely driven by factors such as the rising adoption of online learning platforms, flexible learning options catering to working professionals and geographically dispersed students, and the increasing recognition of online degrees by employers. The market segmentation, encompassing diverse types of online programs (e.g., bachelor's, master's, certificate programs) and applications across various fields (e.g., business, technology, healthcare), contributes to its broad appeal and expansion. Major players like American Public Education, Adtalem Global Education, and others are deploying competitive strategies focused on enhancing the learning experience, improving student support services, and expanding their program offerings to maintain a competitive edge. The geographic distribution indicates strong growth across North America and Asia-Pacific, driven by higher internet penetration and a growing young population seeking educational opportunities. However, challenges remain, including concerns about the perceived quality of online education compared to traditional institutions, the digital divide limiting access for certain demographics, and the need for continuous investment in technology and curriculum development to meet evolving learner needs. Looking ahead to 2033, the online higher education market is projected to maintain significant momentum, further expanding its reach and influence. Continued technological innovation, including advancements in virtual reality and artificial intelligence, will enhance the learning experience and attract a broader range of students. The growing importance of lifelong learning and upskilling will also drive demand for online courses and degree programs. Competitive pressures will likely lead to further innovation in pricing models, program offerings, and marketing strategies, fostering a dynamic and evolving market landscape. To fully capitalize on this growth, educational institutions must prioritize creating engaging and effective online learning environments, addressing concerns around quality and accessibility, and adapting to the ever-changing needs of students in a globally competitive market.

This statistic shows the program outcome data for online education providers that were the most requested by students in the United States in 2016. In 2016, 77 percent of schools reported that students asked for placement and employment rates.

A file that holds the master records for all online training courses nominated for reimbursement.

Percentage of Canadians' use of selected online activities, during the past three months.

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.     The study...

The American Institutes for Research conducted a multisite randomized study that tested an online learning model for credit recovery at 24 high schools in Los Angeles, California in 2018 and 2019. The study focused on first-year high school students who failed Algebra 1 or English 9 (their ninth-grade English course) and retook the course during the summer before their second year of high school. Within each participating school, we used a lottery to determine whether each student was placed in either the school’s typical teacher-directed class (business-as-usual control condition) or a class that used an online learning model (treatment condition). For the online learning model, an online provider supplied the main course content, and the school provided a subject-appropriate, credentialed in-class teacher who could supplement the digital content with additional instruction.The study compared outcomes of students assigned to the treatment condition to outcomes of students assigned to the control condition. Analyses focused both on proximal outcomes (ex: student course experiences, content knowledge, and credit recovery rates) and distal outcomes (ex: on-time graduation and cumulative credits earned by the end of the 4th year of high school). We estimated average treatment effects for the intent-to-treat sample using regression models that control for student characteristics and randomization blocks. We conducted separate analyses for students who failed Algebra 1 and students who failed at least one semester of their English 9 course.This ICPSR data deposit includes our final analytical dataset and three supplemental files. Data come from three sources: (1) extant district data on student information and academic outcomes, (2) end-of-course surveys of students’ and teachers’ experiences, and (3) end-of-course test of students’ content knowledge. Data fields include:Sample information: term, school (anonymized), teacher (anonymized), course, randomization block, student cohort, treatment statusDemographics: sex, race/ethnicity, National School Lunch Program status, inclusion in the Gifted/Talented program, Special Education status, and English language learner statusPre-treatment information (treatment group only): 9th grade GPA, 9th grade attendance rate, number of 9th grade courses failed, 8th grade test scoresOnline course engagement information: percentage of online course completed, average score on online activities, minutes spent in online platformStudent survey data: responses a survey administered at the end of the course for treatment and control students. Questions cover degree of student engagement with the course, perceptions of teacher support and course difficulty, and clarity of course expectations.End-of-course test data: answers and scores on an end-of-course assessment administered to treatment and control students to evaluate content knowledge (Algebra 1 or English 9). The test did not count towards the final course grade and included 17-20 multiple choice questions.Academic outcomes: grade in credit recovery course, credits attempted/earned in each year of high school, GPA in each year of high school, credits/GPA in math and ELA in each year of high school, indicator for on-time high school graduation, 10th grade PSAT scoresTeacher survey and logs: teacher-reported logs on the use of different instructional activities and responses to surveys about course pacing, content, goals, and degree of student support

In 2023, 24 percent of prospective graduate business students in the United States were interested in hybrid programs, an increase from 16 percent in 2019. However, the overall preference in 2023 was for in-person business school programs, at 60 percent.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

In 2025, Texas A&M University-College Station was ranked as the best distance learning institution in the United States, with 40 percent of its students enrolled online. Florida International University, University of Florida, Arizona State University Digital Immersion, and University of Arizona rounded out the top five.

The University of Washington - Beyond High School (UW-BHS) project surveyed students in Washington State to examine factors impacting educational attainment and the transition to adulthood among high school seniors. The project began in 1999 in an effort to assess the impact of I-200 (the referendum that ended Affirmative Action) on minority enrollment in higher education in Washington. The research objectives of the project were: (1) to describe and explain differences in the transition from high school to college by race and ethnicity, socioeconomic origins, and other characteristics, (2) to evaluate the impact of the Washington State Achievers Program, and (3) to explore the implications of multiple race and ethnic identities. Following a successful pilot survey in the spring of 2000, the project eventually included baseline and one-year follow-up surveys (conducted in 2002, 2003, 2004, and 2005) of almost 10,000 high school seniors in five cohorts across several Washington school districts. The high school senior surveys included questions that explored students' educational aspirations and future career plans, as well as questions on family background, home life, perceptions of school and home environments, self-esteem, and participation in school related and non-school related activities. To supplement the 2000, 2002, and 2003 student surveys, parents of high school seniors were also queried to determine their expectations and aspirations for their child's education, as well as their own educational backgrounds and fields of employment. Parents were also asked to report any financial measures undertaken to prepare for their child's continued education, and whether the household received any form of financial assistance. In 2010, a ten-year follow-up with the 2000 senior cohort was conducted to assess educational, career, and familial outcomes. The ten year follow-up surveys collected information on educational attainment, early employment experiences, family and partnership, civic engagement, and health status. The baseline, parent, and follow-up surveys also collected detailed demographic information, including age, sex, ethnicity, language, religion, education level, employment, income, marital status, and parental status.

Historical Dataset of Herzing University Online is provided by CommunityCollegeReview and contain statistics on metrics:Diversity Score Comparison Over Years (2014-2023)

This study is aimed at exploring the major challenges of OE/DL at universities of Bangladesh from the perspective of students, and to help the educationists, the policy-makers as well as educational institutions to plan and implement policies and strategies to ensure an effective and quality OE/DL and to allow the students to complete their higher education successfully.

The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE).

The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:

• abstract,
• instrument,
• copy of the descriptive statistics,
• and SPSS dataset.

Financial overview and grant giving statistics of Quinnipiac University Online Inc

Number of Internet User: Instruction Level: Northeast: University Education: Complete data was reported at 5,164.000 Person th in 2023. This records an increase from the previous number of 5,057.000 Person th for 2022. Number of Internet User: Instruction Level: Northeast: University Education: Complete data is updated yearly, averaging 4,310.000 Person th from Dec 2016 (Median) to 2023, with 7 observations. The data reached an all-time high of 5,164.000 Person th in 2023 and a record low of 3,211.000 Person th in 2016. Number of Internet User: Instruction Level: Northeast: University Education: Complete data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Telecommunication Sector – Table BR.TBA024: Number of Internet User: by Instruction Level.

Percentage of Internet users by selected online activities during the past three months.