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

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.

This dataset provides comprehensive information about various Data Science and Analytics master's programs offered in the United States. It includes details such as the program name, university name, annual tuition fees, program duration, location of the university, and additional information about the programs.

Column Descriptions:

• Subject Name: The name or field of study of the master's program, such as Data Science, Data Analytics, or Applied Biostatistics.

• University Name: The name of the university offering the master's program.

• Per Year Fees: The tuition fees for the program, usually given in euros per year. For some programs, the fees may be listed as "full" or "full-time," indicating a lump sum for the entire program or for full-time enrollment, respectively.

• About Program: A brief description or overview of the master's program, providing insights into its curriculum, focus areas, and any unique features.

• Program Duration: The duration of the master's program, typically expressed in years or months.

• University Location: The location of the university where the program is offered, including the city and state.

• Program Name: The official name of the master's program, often indicating its degree type (e.g., M.Sc. for Master of Science) and format (e.g., full-time, part-time, online).

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, ** percent of schools reported that students asked for placement and employment rates.

During a survey conducted in Spring 2023 in the United States, the most popular factor for choosing online education was the affordability of the program, with ** percent of respondents reporting this as one of their top three reasons. The second most popular factor was the reputation of the school or program.

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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 for data-driven decisio

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

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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The online higher education market share in the US is expected to increase by USD 33.35 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 19.82%.

This online higher education market in the US research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers online higher education market in the US segmentation by subjects (commerce and management, STEM, Arts, and others) and courses (non-degree courses and degree courses). The online higher education market in the US report also offers information on several market vendors, including American Public Education Inc., Adtalem Global Education Inc., Apollo Education Group Inc., Graham Holdings Co., Grand Canyon Education Inc., ITT Educational Services Inc., LINCOLN EDUCATIONAL SERVICES Corp., Perdoceo Education Corp., Strategic Education Inc., and Zovio Inc. among others.

What will the Online Higher Education Market Size in the US be During the Forecast Period?

Download the Free Report Sample to Unlock the Online Higher Education Market Size in the US for the Forecast Period and Other Important Statistics

Online Higher Education Market in the US: Key Drivers, Trends, and Challenges

The collaborations between enterprises and educational institutions is notably driving the online higher education market growth in the US, although factors such as designing e-learning courses may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the online higher education industry in the US. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Online Higher Education Market Driver in the US

The collaborations between enterprises and educational institutions is one of the key drivers supporting the online higher education market growth in the US. Although such collaborations can take numerous forms, the most common are training, partnerships, or R&D partnerships. For instance, IBM-Charlotte was designed by the University of North Carolina-Charlotte (UNC-Charlotte) and IBM with the aim of enhancing the university’s technical vitality, expanding its local personnel resource bank, and ultimately offering IBM's technical communicators a way of earning degrees in their field of work. For UNC-Charlotte, the relationship with IBM gave its developing Technical Communication program community support and visibility and simultaneously provided faculty with research opportunities and practical experience at the workplace. Such collaborations are driving the growth of the online higher education market in the US during the forecast period.

Key Online Higher Education Market Trend in the US

Increased use of wearable gadgets is another factor supporting the online higher education market growth in the US. Technologies such as augmented reality (AR) are also changing the learning experience of participants. 3D simulations and scenarios that are developed using wearable technology devices give users a chance to learn in different learning environments. Moreover, the theoretical explanation of various concepts and step-by-step training on operations in an organization, followed by familiarizing students with on-the-floor working environments, are time-consuming. Therefore, wearable technology devices can help universities or educational institutions to engage with students directly on the floor. This reduces the duration and makes students more comfortable with online learning. Thus, the affordable prices of wearable gadgets will foster their greater adoption, in turn fostering the growth of the online higher education market in the US

Key Online Higher Education Market Challenge in the US

Designing e-learning courses is one of the factors hindering the online higher education market growth in the US. A significant amount of time, money, and resources are needed for developing the content for online courses. On average, moderately interactive online content takes about 90-240 hours to develop and costs developers approximately $10,000 per produced hour for moderate-level content. Similarly, the cost keeps rising as the complexity of the content increases. The major factors impacting the cost incurred on creating online education content are the resources needed, the state of the source content, the elements embedded in the online content, and the interactivity and instructional complexities involved. Therefore, this is a challenging factor for the growth of the online higher education market in the US.

This online higher education market in the US analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market

In 2024, about **** percent of all students who chose online degree programs in the United States said they did so because COVID-19 made it the only option available to them, a slight decrease from ** percent in the previous year. In both 2023 and 2024, however, the most commonly cited reason for students to choose online degree programs was due to existing commitments, such as work and family, preventing their attendance in campus-based courses.

Career and Technical Education Programs in Public School Districts, 2016-17 (FRSS 108) is a data collection that is part of the Fast Response Survey System (FRSS) program; program data are available since 1998-99 at . FRSS 108 (https://nces.ed.gov/surveys/frss/index.asp) is a cross-sectional data collection that provides nationally representative data on career and technical education (CTE) programs. Public local education agencies (LEAs) instructing either grades 11 or 12 in the 50 United States and the District of Columbia were sampled. The study was conducted using mailed questionnaires that could be completed on paper or online. The data collection's response rate was 86 percent. Key statistics produced from FRSS 108 include data on the entities that provide the CTE programs, the locations at which the CTE programs are offered, work-based learning activities and employer involvement in CTE programs, barriers to the district offering CTE programs, barriers to student participation in CTE programs, and the extent to which various factors influence the district's decisions on whether to add or phase out CTE programs.

Dual Enrollment Programs and Courses for High School Students, 2002-03 (PEQIS 14), is a study that is part of the Postsecondary Education Quick Information System (PEQIS) program; program data is available since 1997 at . PEQIS 14 (https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2009045) is a cross-sectional survey that collected information on the topic of dual enrollment of high school students at postsecondary institutions. 1,600 Title IV degree-granting postsecondary institutions in the 50 United States and the District of Columbia were sampled. The study was conducted using online or paper surveys. The overall response rates were 92 percent weighted and 91 percent unweighted. Key statistics produced from PEQIS 14 were information on the prevalence of college course-taking by high school students at their institutions during the 2002-03 12-month academic year, both within and outside of dual enrollment programs. Among institutions with dual enrollment programs, additional information was obtained on the characteristics of programs, including course location and type of instructors, program and course curriculum, academic eligibility requirements, and funding.

The online data science training programs market is experiencing explosive growth, projected to reach $1.90 billion in 2025 and exhibiting a robust Compound Annual Growth Rate (CAGR) of 34.73% from 2025 to 2033. This surge is driven by the escalating demand for data scientists across various industries, coupled with the accessibility and flexibility offered by online learning platforms. The increasing availability of high-quality online courses, encompassing both professional degree programs and specialized certifications, caters to a diverse learner base, ranging from career changers to experienced professionals seeking upskilling. North America, particularly the U.S. and Canada, currently holds a significant market share, fueled by a strong technological ecosystem and high adoption rates. However, the Asia-Pacific region (APAC), especially China and India, is poised for substantial growth, driven by a burgeoning tech sector and a large pool of young professionals. The market is highly competitive, with established players like Coursera, Udacity, and Udemy competing with specialized platforms like DataCamp and AnalytixLabs, as well as traditional universities offering online programs. This competitive landscape fosters innovation and ensures a diverse range of courses and pricing models, further contributing to market expansion. Continued growth is anticipated due to several factors. The increasing integration of data science into various sectors, from finance and healthcare to marketing and e-commerce, continuously necessitates skilled professionals. Furthermore, the ongoing advancements in artificial intelligence (AI) and machine learning (ML) are expanding the scope of data science applications, thereby increasing the demand for training programs that address these emerging technologies. While the market faces certain challenges, such as ensuring the quality and relevance of online courses and addressing the digital divide, the overall trajectory indicates a sustained period of growth, promising significant opportunities for both established and emerging players in the online data science education sector.

Pandemic has influenced all spheres of the humanity. COVID-19 impacted the education vertical in larger manner. Traditional classroom environment plays a very vital role in molding the life of an individual. Bond nurtured in the early ages of the life acts as the great moral support in the latter stages of the journey. As the pandemic has forced us into online education, this data collection aims to analyze the impact of online education. To check out the satisfactory level of the learners, review was conducted.

Gender – Male, Female Home Location – Rural, Urban Level of Education – Post Graduate, School, Under Graduate Age – Years Number of Subjects – 1- 20 Device type used to attend classes – Desktop, Laptop, Mobile Economic status – Middle Class, Poor, Rich Family size – 1 -10 Internet facility in your locality – Number scale (Very Bad to Very Good) Are you involved in any sports? – Yes, No Do elderly people monitor you? – Yes, No Study time – Hours Sleep time – Hours Time spent on social media – Hours Interested in Gaming? – Yes, No Have separate room for studying? – Yes, No Engaged in group studies? – Yes, No Average marks scored before pandemic in traditional classroom – range Your interaction in online mode - Number scale (Very Bad to Very Good) Clearing doubts with faculties in online mode - Number scale (Very Bad to Very Good) Interested in? – Practical, Theory, Both Performance in online - Number scale (Very Bad to Very Good) Your level of satisfaction in Online Education – Average, Bad, Good

radhakrishnan, sujatha (2021), “Online Education System - Review”, Mendeley Data, V1, doi: 10.17632/bzk9zbyvv7.1

Programs and Services for High School English Learners, 2015-16 (FRSS 107) is a study that is part of the Fast Response Survey System (FRSS) program; program data is available since 1998-99 at . FRSS 107 (https://nces.ed.gov/surveys/frss/index.asp) is a study that provides nationally representative data on programs and services for high-school English learners (Els), including instructional approaches, newcomer programs, online or computer-based programs, and programs or services (e.g., tutoring) designed specifically for high school Els. The study was conducted using mailed questionnaires that could be completed on paper or online. Public local education agencies (LEAs) instructing either of Grades 11 or 12 in the 50 United States and the District of Columbia were sampled. The study's weighted response rate was 89 percent. Key statistics produced from FRSS 107 include data on the use of native language(s) for content instruction, instructional support, materials, and services; information that LEAs provide about educational programs or services to Els aged 18 to 21 years-old seeking to newly enroll in the LEA; and factors LEAs consider when providing information about these programs and services to Els in this group.

This domain covers statistics and indicators on key aspects of the education systems across Europe. The data show entrants and enrolments in education levels, education personnel and the cost and type of resources dedicated to education.

For a general technical description of the UOE Data Collection see UNESCO OECD Eurostat (UOE) joint data collection – methodology - Statistics Explained (europa.eu).

The standards on international statistics on education and training systems are set by the three international organisations jointly administering the annual UOE data collection:

• The United Nations Educational, Scientific, and Cultural Organisation Institute for Statistics (UNESCO-UIS),
• The Organisation for Economic Co-operation and Development (OECD) and,
• The Statistical Office of the European Union (EUROSTAT).

The following topics are covered:

• Pupils and students – Enrolments and Entrants,
• Learning mobility,
• Education personnel,
• Education finance,
• Graduates,
• Language learning.

Data on enrolments in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Age or age group,
• NUTS1 and NUTS2 regions,
• Type of educational institution (public or private) – referred to as the ‘sector’ in Eurobase,
• Intensity of participation (full-time, part-time, full-time equivalent) – referred to as ‘working time’ in Eurobase,
• Programme orientation (general/academic or vocational/professional),
• Type of vocational programme (school-based only or combined school and work-based),
• Level of attainment that can be achieved upon programme completion (e.g. insufficient for level completion or partial level completion, sufficient for partial level completion without direct access to tertiary education),
• Field of education (ISCED-F13).

Additionally, the following types of indicators on enrolments are calculated (all indicators using population data use Eurostat’s population database (demo_pjan)):

• Participation rates by age or by age groups as % of corresponding age population.
• Participation rates by age as % of total population.
• Pupils from age 0, 3, 4 and 5 to the starting age of compulsory education at primary level, as % of the population of the corresponding age. In some countries, the start of primary education is not compulsory and in some countries compulsory education starts at pre-primary level. This indicator calculates the participation rates of pupils up until (but not including) the starting age of formal education that is both compulsory and at the primary level. This age varies from 5 years to 7 years across countries and the national starting ages for compulsory primary education used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
• Pupils under the age of 3 as % of corresponding age population. This indicator does not include 3 year olds (includes ages 0, 1 and 2).
• Out-of-school rates at different ages. This indicator is calculated as 100 – (students of a particular age who are enrolled in education at any ISCED level / Total population of that age *100).
  • Out-of-school rates in population of lower secondary school age and in population of upper secondary school age. This indicator is calculated as 100 – (students who are of the official age range for ISCED X who are enrolled in education at any ISCED level / Total population in the official age range for ISCED X *100). The official age range for each ISCED level varies across countries, and national age ranges for lower and upper secondary used in the calculation of this indicator are listed in the file Ages_educ_indicators which is available to download in the Annexes section of this page.
  • Students in education of post-compulsory school age - as % of the total population of post-compulsory school age. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students participation at the end of compulsory education - as % of the corresponding age population. Indicator is calculated for age (X-1), (X), (X+1), (X+2) where X = the final age at which formal education is compulsory in national education systems. The final age at which formal education is considered as compulsory in national education systems in the calculation of this indicator are listed in the file Ages_educ_indicators.
  • Students in education aged 30 and over - per 1000 of corresponding age population
    • Expected school years of pupils and students at different levels of education
    • Distribution of pupils and students enrolled in general and vocational programmes by education level and NUTS2 regions
    • Distribution of students in different fields of education
    • Ratio of the proportion of the population who are tertiary students in NUTS1 regions to the proportion of the population who are tertiary students in NUTS2 regions

Data on entrants in education are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Programme orientation (general/academic or vocational/professional),
• Sex,
• Age or age group,
• Field of education (ISCED-F13).

Additionally the following indicator on entrants is calculated:

• Distribution of new entrants in different fields of education.

Data on learning mobility is available for degree mobile students, degree mobile graduates and credit mobile graduates. Degree mobility means that students/graduates are/were enrolled as regular students in any semester/term of a programme taught in the country of destination with the intention of graduating from it in the country of destination. Credit mobility is defined as temporary tertiary education or/and study-related traineeship abroad within the framework of enrolment in a tertiary education programme at a "home institution" (usually) for the purpose of gaining academic credit (i.e. credit that will be recognised in that home institution). Further definitions are in Section 2.8 of the UOE manual.

Degree mobile students are referred to as just ‘mobile students’ in UOE learning mobility tables. Data is disseminated for degree mobile students and degree mobile graduates in absolute numbers with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Field of education (ISCED-F13),
• Country of origin (defined as the country of education prior to entering tertiary although there may be national deviations. These are listed in the Helpsheet of the latest footnotes report available to download in the Annexes section of this page) – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Additionally the following types of indicators on degree mobile students and degree mobile graduates are calculated ((all indicators using population data use Eurostat’s population database (demo_pjan)):

• Share of all students/graduates who are mobile students/degree mobile graduates from abroad,
• Distribution of mobile students/degree mobile graduates from abroad in different fields of education.

For credit mobile graduates, data are disseminated in absolute numbers, with breakdowns available for the following dimensions:

• ISCED level of education,
• Sex,
• Type of mobility scheme (e.g. Credit mobility under EU programmes i.e. ERASMUS, Credit mobility in other international/national programmes),
• Type of mobility (study period only or study period combined with work placement),
• Country of destination – referred to as ‘Geopolitical entity (partner)’ in Eurobase.

Data on personnel in education are available for classroom teachers/academic staff, teacher aides and school-management personnel. Teachers are employed in a professional capacity to guide and direct the learning experiences of students, irrespective of their training, qualifications or delivery mechanism. Teacher aides support teachers in providing instruction to students. Academic staff are personnel employed at the tertiary level of education whose primary assignment is instruction and/or research. School management personnel covers professional personnel who are responsible for school management/administration (ISCED 0-4) or whose primary or major responsibility is the management of the institution, or a recognised department or subdivision of the institution (tertiary levels). Full definitions of these statistical units are in Section 3.5 of the UOE manual.

Data are disseminated on teachers and academic staff in absolute numbers, with breakdowns available for the following dimensions:

• ISCED

Historical Dataset of Willoughby-eastlake City Schools Online Academy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2022-2023),Distribution of Students By Grade Trends,Hispanic Student Percentage Comparison Over Years (2022-2023),Black Student Percentage Comparison Over Years (2022-2023),White Student Percentage Comparison Over Years (2022-2023),Two or More Races Student Percentage Comparison Over Years (2022-2023),Diversity Score Comparison Over Years (2022-2023),Math Proficiency Comparison Over Years (2022-2023),Overall School Rank Trends Over Years (2022-2023)

Historical Dataset of Jefferson Area Local Schools Online is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2022-2023),Distribution of Students By Grade Trends,White Student Percentage Comparison Over Years (2022-2023),Math Proficiency Comparison Over Years (2022-2023),Overall School Rank Trends Over Years (2022-2023)