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).

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.

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.

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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

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

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.

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

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

In the academic year 2025, *** universities in India offered **** online courses. The number of online degrees offered by Indian universities has doubled since 2020. India's online degrees were made possible by regulations by the University Grants Commission in 2018.

Key indicators of broadband adoption, service and infrastructure in New York City. Data Limitations: Data accuracy is limited as of the date of publication and by the methodology and accuracy of the original sources. The City shall not be liable for any costs related to, or in reliance of, the data contained in these datasets.

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

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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

One of the sectors that felt the impact of the Corona Virus Disease 2019 (COVID-19) pandemic was the educational sector. The outbreak led to the immediate closure of schools at all levels thereby sending billions of students away from their various institutions of learning. However, the shut down of academic institutions was not a total one as some institutions that were solely running online programmes were not affected. Those who were running face to face and online modes quickly switched over to the online mode. Unfortunately, institutions that have not fully embraced online mode of study were greatly affected. 85% of academic institutions in Nigeria are operating face to face mode of study, therefore, majority of Nigerian students at all levels were affected by the COVID-19 lockdown. Social media platforms and emerging technologies were the major backbones of institutions that are running online mode of study, therefore, this survey uses the unified theory of acceptance and use of technology (UTAUT) model to capture selected Face to face Nigerian University students accessibility, usage, intention and willingness to use these social media platforms and emerging technologies for learning. The challenges that could mar the usage of these technologies were also revealed. Eight hundred and fifty undergraduate students participated in the survey.

The dataset includes the questionnaire used to retrieve the data, the responses obtained in spreadsheet format, the charts generated from the responses received, the Statistical Package of the Social Sciences (SPSS) file and the descriptive statistics for all the variables captured. This second version contains the reliability statistics of the UTAUT variables using Cronbach's alpha. This measured the reliability as well as the internal consistency of the UTAUT variables. This was measured in terms of the reliability statistics, inter-item correlation matrix and item-total statistics. Authors believed that the dataset will enhance understanding of how face to face students use social media platforms and how these platforms could be used to engage the students outside their classroom activities. Also, the dataset exposes how familiar face to face University students are to these emerging teaching and learning technologies.

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

Percentage data of the number of searches: We investigated the transition of people searching for the keywords "university students", "distance learning" and "online classes" using Google search. (Number of searches) ÷ base value is a relative value, i.e. the data with the highest number of searches in the data is 100%, and the other data are calculated as a percentage as (data) ÷ (most frequent data).

Learning Management System online courses for USAID staff to access.

Between 2015 and 2024, the number of bachelor's students who graduated from online universities in Italy steadily increased. In 2015, less than ***** people obtained their bachelor's from an online university. After nine years, the number of students more than doubled, reaching ****** graduates. In Italy, bachelor's students represented the largest group of e-learning university students, ******* people.